Morphometric parameters (wing venation) of honeybee workers from 10 bee colonies of the Krasnoyarsk Krai (Yenisei population).
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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To uncover the miraculous plant, this book not only gives you the plant's origins, where the plant is grown now, but also provides current studies on its utilization, cultivation, breeding, and therapeutic benefits.",isbn:"978-1-83880-030-7",printIsbn:"978-1-83880-029-1",pdfIsbn:"978-1-83880-407-7",doi:"10.5772/intechopen.83688",price:119,priceEur:129,priceUsd:155,slug:"ginger-cultivation-and-its-antimicrobial-and-pharmacological-potentials",numberOfPages:162,isOpenForSubmission:!1,hash:"b0f597104b548a6b922696409ab891fa",bookSignature:"Haiping Wang",publishedDate:"February 19th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/9353.jpg",keywords:null,numberOfDownloads:3423,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:3,numberOfTotalCitations:3,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 20th 2019",dateEndSecondStepPublish:"August 29th 2019",dateEndThirdStepPublish:"October 28th 2019",dateEndFourthStepPublish:"January 16th 2020",dateEndFifthStepPublish:"March 16th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"280406",title:"Dr.",name:"Haiping",middleName:null,surname:"Wang",slug:"haiping-wang",fullName:"Haiping Wang",profilePictureURL:"https://mts.intechopen.com/storage/users/280406/images/system/280406.jpeg",biography:"Haiping Wang was born in Chifeng, Chian in 1975. He holds BSc in Plant protection (1998), MSc in Plant breeding (2001) and PhD in Vegetable science (2001) at Graduate School of Chinese Academy of Agricultural Sciences. Since 2001 he is a full time Research Scientist and Association Professor of Horticulture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences. His research interest include: research on vegetable genetic resources in order to collect the germplasm for preserving the diversity of Midterm Gene-Bank of Vegetables Genetic Resources in China; research on garlic and ginger genetics and breeding conducted to improve the crop for growers and consumers. Key areas of interest include garlic, ginger, radish and cucumber genetics and development of genomic tools, genetic improvement of garlic disease resistance, garlic diversity and origins, and of human nutritional quality and flavor of both garlic and ginger. Outreach activities include interaction with the garlic and ginger production and with consumers.",institutionString:"Chinese Academy of Agricultural Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Chinese Academy of Agricultural Sciences",institutionURL:null,country:{name:"China"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"41",title:"Plant Biology",slug:"agricultural-and-biological-sciences-plant-biology"}],chapters:[{id:"69831",title:"Introductory Chapter: Studies on Ginger",slug:"introductory-chapter-studies-on-ginger",totalDownloads:240,totalCrossrefCites:0,authors:[{id:"280406",title:"Dr.",name:"Haiping",surname:"Wang",slug:"haiping-wang",fullName:"Haiping Wang"}]},{id:"68980",title:"Utilisation and Functional Components Evaluation of Ginger",slug:"utilisation-and-functional-components-evaluation-of-ginger",totalDownloads:338,totalCrossrefCites:0,authors:[{id:"304365",title:"Prof.",name:"Suwijiyo",surname:"Pramono",slug:"suwijiyo-pramono",fullName:"Suwijiyo Pramono"}]},{id:"69314",title:"Biotechnology and Crop Improvement of Ginger (Zingiber officinale Rosc.)",slug:"biotechnology-and-crop-improvement-of-ginger-em-zingiber-officinale-em-rosc-",totalDownloads:409,totalCrossrefCites:0,authors:[{id:"299119",title:"Dr.",name:"Neeta",surname:"Shivakumar",slug:"neeta-shivakumar",fullName:"Neeta Shivakumar"}]},{id:"68348",title:"Cultivation of Ginger in Sikkim under an Organic System",slug:"cultivation-of-ginger-in-sikkim-under-an-organic-system",totalDownloads:269,totalCrossrefCites:0,authors:[{id:"299779",title:"Dr.",name:"Vijayan",surname:"Alavoor Keloth",slug:"vijayan-alavoor-keloth",fullName:"Vijayan Alavoor Keloth"}]},{id:"69227",title:"Diseases of Ginger",slug:"diseases-of-ginger",totalDownloads:687,totalCrossrefCites:0,authors:[{id:"301506",title:"Dr.",name:"Jebasingh",surname:"Tennyson",slug:"jebasingh-tennyson",fullName:"Jebasingh Tennyson"},{id:"308870",title:"Dr.",name:"Meenu",surname:"Gupta",slug:"meenu-gupta",fullName:"Meenu Gupta"}]},{id:"70485",title:"Harnessing the Therapeutic Properties of Ginger (Zingiber officinale Roscoe) for the Management of Plant Diseases",slug:"harnessing-the-therapeutic-properties-of-ginger-em-zingiber-officinale-em-roscoe-for-the-management-",totalDownloads:305,totalCrossrefCites:0,authors:[{id:"296475",title:"Prof.",name:"Elias",surname:"Sowley",slug:"elias-sowley",fullName:"Elias Sowley"},{id:"310656",title:"Dr.",name:"Frederick",surname:"Kankam",slug:"frederick-kankam",fullName:"Frederick Kankam"}]},{id:"69729",title:"Ginger (Zingiber officinale) Antimicrobial Potential: A Review",slug:"ginger-em-zingiber-officinale-em-antimicrobial-potential-a-review",totalDownloads:440,totalCrossrefCites:0,authors:[{id:"223173",title:"Dr.",name:"Ana Lucia",surname:"Abreu-Silva",slug:"ana-lucia-abreu-silva",fullName:"Ana Lucia Abreu-Silva"},{id:"287290",title:"Ph.D.",name:"Fernando",surname:"Almeida De Souza",slug:"fernando-almeida-de-souza",fullName:"Fernando Almeida De Souza"},{id:"294926",title:"MSc.",name:"Amanda",surname:"Mara Teles",slug:"amanda-mara-teles",fullName:"Amanda Mara Teles"},{id:"294927",title:"Prof.",name:"Adenilde Nascimento",surname:"Mouchrek",slug:"adenilde-nascimento-mouchrek",fullName:"Adenilde Nascimento Mouchrek"},{id:"294930",title:"Dr.",name:"Kátia Da Silva",surname:"Calabrese",slug:"katia-da-silva-calabrese",fullName:"Kátia Da Silva Calabrese"}]},{id:"69184",title:"A Review of the Antidiabetic Activities of Ginger",slug:"a-review-of-the-antidiabetic-activities-of-ginger",totalDownloads:345,totalCrossrefCites:0,authors:[{id:"303199",title:"Dr.",name:"Gloria",surname:"Otunola",slug:"gloria-otunola",fullName:"Gloria Otunola"},{id:"303201",title:"Prof.",name:"Anthony",surname:"Afolayan",slug:"anthony-afolayan",fullName:"Anthony Afolayan"}]},{id:"68833",title:"Pharmacological Potentials of Ginger",slug:"pharmacological-potentials-of-ginger",totalDownloads:411,totalCrossrefCites:0,authors:[{id:"200124",title:"Dr.",name:"Fatai Oladunni",surname:"Balogun",slug:"fatai-oladunni-balogun",fullName:"Fatai Oladunni Balogun"},{id:"267700",title:"Dr.",name:"Anofi",surname:"Ashafa",slug:"anofi-ashafa",fullName:"Anofi Ashafa"},{id:"309473",title:"Mrs.",name:"Temitayo Esther",surname:"Adeyeoluwa",slug:"temitayo-esther-adeyeoluwa",fullName:"Temitayo Esther Adeyeoluwa"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"177730",firstName:"Edi",lastName:"Lipovic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/177730/images/4741_n.jpg",email:"edi@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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It was the dark-colored forest bee Apis mellifera mellifera L., or the Middle Russian race (a term adopted in Russia), that was cultivated in Siberia as the most adapted to the harsh climatic conditions of the region. At the end of the last century, bees of southern races, such as the Carpathian race or Apis mellifera carpatica (a derivative of A. m. carnica) and the Caucasian gray mountain race (Apis mellifera caucasica Gorb.), have been actively imported to Siberia. This process had become widespread and almost uncontrollable, which leads to a high level of crossbreeding of bees.
\nAt present, one of the beekeeping problems in different countries is a massive bee hybridization, which leads to the reduction of the range of native subspecies, the formation of hybrids, and “deterioration” of the genotypic composition of honeybees. Hybrid populations are less adapted to environmental conditions that rapidly change during the year and are characterized by the higher morbidity and low immunity [1–3].
\nIntrogressive hybridization modifies the genetic pool of local honeybee populations leading to the loss of their genetic identity [4]. The process of hybridization of different subspecies of honeybee can cause the destruction of the established gene complexes, leading to decrease in adaptive properties of organisms and populations and the change in biological and economically significant indicators of bees. The observed widespread hybridization of honeybees and the formation of hybrid bees can certainly contribute to the spread of disease. The extent of hybridization, characteristics of hybrid bees, the study of genetic processes that occur during hybridization, and evaluation of the effects of hybridization are of considerable interest.
\nThe goal of this study is the morphometric and molecular genetic (mtDNA and microsatellite analysis) characterization of honeybees in Siberia and the assessment of the infestation of bee colonies by Nosema.
\nBees and bee colonies were investigated in three regions of Siberia: the Tomsk region, the Krasnoyarsk Krai, and the Altai Krai (Figure 1).
\nThe Tomsk region is located in the geographic center of Siberia, in the southeastern part of the West Siberian Plain. The distance between the northern and southern boundaries of the meridian is about 600 kilometers; therefore, the climate of the southern and northern regions is markedly different. A climatic characteristic of the northern region is a more severe and prolonged winter season. Almost the entire territory of the region is within the taiga zone, where forests cover about 60% of the territory. The climate is temperate continental with considerable daily and annual amplitudes and long winters (5–6 months). The average annual temperature is –0.6 °C, while the average temperature in July is +18.1 °C and in January is 19.2 °C. The frost-free period is 100–105 days. Precipitation is 435 mm.
\nMap of localization of studied areas of Siberia (dots A–C) and apiaries of the Tomsk region (dots 1–31): A, the Tomsk region; B, the Krasnoyarsk Krai; C, the Altai Krai. 1, s. Parabel; 2, vicinity of g. Kolpashevo; 3, d. Novoabramkino; 4, s. Leboter; 5, s. Podgornoe; 6, d. Strelnikovo; 7, s. Gorelovka; 8, d. Sarafanovka; 9, s. Sokolovka, s. Mogochino; 10, s. Krivosheino; 11, s. Vysoky Yar, d. Krylovka; 12, s. Bakchar, s. Parbig; 13, d. Tihomirovka; 14, ur. Kuzherbak; 15, s. Novikovka; 16, s. Kargala; 17, s. Dubrovka; 18, s. Okuneevo; 19, s. Zyryanskoe; 20, d. Kuskovo; 21, p. Zarechnyi (Mezheninovskoe rural settlement); 22, d. Bodazhkovo, s. Semiluzhki, p. Zarechnyi (Malinovskoe rural settlement); 23, d. Nizhne-Sechenovo, d. Berezkino, s. Zorkaltsevo, s. Rybalovo, d. Kudrinsky uchastok, d. Gubino; 24, p. Sinii Utes, d. Magadaevo, d. Prosekino, s. Kolarovo, vicinity of Tomsk; 25, d. Bolshoe Protopopovo; 26, s. Mezheninovka; 27, d. Kandinka, s. Kurlek; 28, s.Yar; 29, d. Elovka; 30, d. Krutolozhnoe; 31, s. Teguldet. Apiaries located at a distance less than 15 km from each other are marked as a single point.
The Krasnoyarsk Krai is located in the Eastern Siberia. The climate is sharply continental, where 70% of the territory is occupied by forests.
\nThe Altai Krai is located in the south-east of Western Siberia. The region contains almost all natural zones of Russia—the steppe and forest steppe, taiga, and mountains. The climate of the Altai Territory is highly heterogeneous because of various geographical conditions. Foothills have a temperate climate, the transition to continental.
\nThe samples are obtained from different geographic parts (ecologically and climatically different districts) of the Tomsk region, including districts with a high beekeeping activity (the southern districts) or districts with a low apicultural activity (the northern districts), according to the local knowledge of specialists from the Society of Beekeepers. Honeybees from the apiaries of the Krasnoyarsk Krai and the Altai Krai were also investigated for comparison.
\nA total of 332 bee colonies (60 apiaries) from Siberia were investigated by morphometric (3043 honey bee workers) and molecular genetic methods (2073 bees by mtDNA analysis and from 252 to 515 bees by microsatellite analysis): 318 bee colonies from the Tomsk region; 10 colonies from the Krasnoyarsk Krai, and 5 colonies from the Altai Krai (Figure 1).
\nBee colonies from the Krasnoyarsk Krai were collected from the unique isolated Old Believers population, which existed for more than 60 years in forest without the importation of new honeybees.
\nBee colonies from the Altai Krai have been collected in the apiary, located in the foothills.
\nInfestation of bee colonies by Nosema infections were studied in 1983 samples obtained from 132 bee colonies from 68 apiaries of Siberia during 2012–2015.
\nMorphometric parameters (wing venation), including the cubital index, the hantel index, and the discoidal shift, were studied (Figure 2).
\nScheme of the front wing venation of honeybee (I) and discoidal shift (II, III, and IV), showing the position of the horizontal and vertical lines (dashed lines). A, B, C, D, and E—the key points and segments that are used in determining the wing index (cubital index: CD/DE; hantel index: CE/AB). Options of discoidal shift: II—negative (point F is located to the left of the perpendicular line); III—zero (point F located on a perpendicular line); IV—positive (point F is located to the right of the perpendicular line). Designation of sells: 1, radial; 2, cubital; 3, discoidal.
DNA isolation and polymerase chain reaction (PCR) was carried out according to standard techniques with some modifications [5,6]. To amplify the COI–COII mtDNA locus, the following sequences of primers were used: 3′-CACATTTAGAAATTCCATTA, 5′-ATAAATATGAATCATGTGGA [5]. Amplification products were fractionated in 1.5% agarose gel, and the results were documented with the use of Gel-Doc XR+.
\nVariability of eight microsatellite loci was studied: А008 (=A8), Ap049, AC117, AC216, Ap243, H110, A024, and A113. PCR was performed using specific primers and reaction conditions according to Solignac et al. [7]. Amplification products were analyzed with ABI Prism 3730 Genetic Analyser (Applied Biosystems, Inc., Foster City, CA) and GeneMapper Software (Applied Biosystems, Inc.). Two microliters of PCR products were mixed with GeneScan500-ROX size standards (Applied Biosystems, Inc.) and deionized formamide. Samples were run according to the manufacturer’s recommendations. These genetic parameters were calculated: allelic frequencies and standard error.
\nFrom 10 to 70 bees were randomly selected from each bee colony and were examined for the presence of Nosema. Bee samples were stored in 70% (v/v) ethanol at room temperature prior to testing. The analysis was performed separately for each bee. The midgut of each sample was isolated, and one part of the midgut was used for the detection of Nosema spores under a light microscope, while the other part was used for DNA extraction. The midgut was suspended in 200 μL of distilled water and examined by dark-field microscopy for the presence of Nosema spores [8]. DNA was extracted from the midgut using a DNA purification kit, PureLink™ Mini (Invitrogen, Carlsbad, CA), according to the manufacturer’s protocol.
\nAfter extraction, the samples were submitted to duplex-PCR [9,10]. The primer sequences utilized to amplify the 218-bp fragment corresponding to the 16S ribosomal gene of N. ceranae were 218MITOC–FOR 5’–CGGCGACGATGTGATATGAAAATATTAA–3’ and 218MITOC–REV 5’–CCCGGTCATTCTCAAACAAAAAACCG–3’[9]. The primer sequences used to amplify the 321 bp fragment corresponding to the 16S ribosomal gene of N. apis were 321APIS–FOR 5’–GGGGGCATGTCTTTGACGTACTATGTA–3’ and 321APIS–REV 5’–GGGGGGCGTTTAAAATGTGAAACAACTATG–3’[9]. PCR was performed using specific primers and reaction conditions according to Hamiduzzaman et al. [10]. PCR products were analyzed on 1.5 % (m/v) agarose gels and visualized using UV illumination (Gel Doc XR+, BioRad, Foster City, CA, USA). All analyses were carried out in duplicate, positive and negative controls were used, and identical results were obtained.
\nIn addition to the use of specific primers and fragment size to identify the species present, a selection of fragments (both N. ceranae and N. apis) was verified by DNA sequencing. Sequencing was done in both directions using forward or reverse primer (BigDye Terminator v3.1 Cycle Sequencing Kit, Applied Biosystems, Foster City, CA). DNA sequencing was performed using ABI Genetic Analyzer 3730 (Applied Biosystems) according to the manufacturer’s protocol.
\nUsing the mtDNA analysis (locus COI-COII), we performed molecular genetic analysis of bee colonies (5–6 samples from each bee colony) to determine the origin of bee colony on the maternal line.
\nAn assessment of the genetic diversity of the COI-COII mtDNA locus in honeybee populations from the Tomsk region was conducted (see details in reference [11]). Three variants of the COI-COII mtDNA locus were registered: PQQ, PQQQ (typical for Middle Russian race), and Q (typical for southern races). We established that 64% of bee colonies on the maternal line originate from the Middle Russian race, 28% of colonies originate from southern subspecies, and 8% are mixed bee colonies. The southern parts of the Tomsk region (with a high beekeeping activity) show a higher genetic diversity of honeybees as compared with the northern regions, which are dominated by bee colonies (96%) and apiaries (73%) that are homogeneous for the genetic variant of locus COI-COII. The bee colonies derived from the Middle Russian breed were genetically heterogeneous for the COI-COII locus: the PQQ variant was registered in 86.1% of the total number of bee colonies of the Middle Russian race, PQQQ was registered in 9.4%, and another 4.5% of bee colonies showed the presence of individuals with both allele PQQ and allele PQQQ.
\nDistribution of COI-COII mtDNA locus variants for the districts (numbers 1–13) of the Tomsk region. Northern districts: 1, Parabelsky; 2, Kolpashevsky; 3, Chainsky; 4, Bakcharsky; 5, Molchanovsky; 6, Krivosheinsky; and southern districts: 7, Asinovsky; 8, Pervomaisky; 9, Teguldetsky; 10, Zyryansky; 11, Tomsky; 12, Shegarsky; 13, Kozhevnikovsky. Variants PQQ/PQQQ/Q (1%) and PQQQ/Q (3%), which are found only in the Tomsk district, are combined.
Based on the analysis of mtDNA (locus COI-COII), assessment of the genetic diversity of the honeybee in apiaries of the Tomsk region has shown that the genetic structure of bee populations in the Tomsk region is complex and mosaic, especially in the southern parts of the region (Figure 3). No large areas with an array of bees having a homogeneous genetic (race) composition and maternally originating from the Middle Russian race have been found; a few apiaries were revealed, in which all bees originated from the Middle Russian breed.
\n\n\nIn the study of variability of the COI-COII mtDNA locus in honeybees from apiaries of the Krasnoyarsk Krai and the Altai Krai, two variants of the COI-COII locus specific for Middle Russian race were identified: only variant PQQ was registered in honeybees of Krasnoyarsk Krai (Yenisei population) and two variants (PQQ and PQQQ) were found in honeybees from the Altai Krai. No a variant Q specific for southern races of bee was detected.
\nDue to the fact that mtDNA analysis allows assessing only the maternal component in the genome of the honeybee, bee colonies were investigated by the morphometric analysis to identify the characteristics of both the maternal and paternal lines, and to assess the level of hybridization.
\nThe results of the morphometric study of honeybees from examined regions of Siberia (the Tomsk region, the Krasnoyarsk Krai, and the Altai Krai) were different.
\nAccording to the morphometric study, the majority of the studied bee colonies of the Tomsk region are hybrids between the Middle Russian race of bees and bees of southern origin (predominantly Carpathian race). Data on the distribution of subspecies and hybrids in the apiaries of the Tomsk region on the basis of cubital index are shown in Figure 4. Some of the apiaries, which cultivate the Middle Russian bees, were found in the northern and southern parts of the Tomsk region.
\nDistribution of subspecies and hybrids in the apiaries of the Tomsk region on the basis of cubital index of bee workers. Studied settlement are indicated by numbers: 1, s. Parabel; 2, vicinity of g. Kolpashevo; 3, Podgornoe; 4, s. Leboter; 5, d. Strelnikovo; 6, s. Gorelovka; 7, s. Vysoky Yar, d. Krylovka; 8, s. Mogochino; 9, s. Krivosheino, Sokolovka; 10, s. Kargala; 11, ur. Kuzherbak; 12, d. Krutolozhnoe; 13, s. Teguldet; 14, s. Okuneevo; 15, s. Zyryanskoe; 16, s. Dubrovka; 17, s. Novorozhdestvenskoe; 18, s. Kornilovo, s. Semiluzhki, p. Zarechnyi (Malinovskoe rural settlement); 19, p. Sinii Utes, d. Magadaevo, d.6 Prosekino, s. Kolarovo, vicinity of Tomsk; p. Zarechnyi (Mezheninovskoe rural settlement); 20, s. Mezheninovka; d. Arkashovo; 21, s. Zorkaltsevo, s. Rybalovo, d. Kudrinsky uchastok, d. Gubino; 22, s. Kurlek; 23, s.Yar; 24, d. Elovka. Apiaries located at a distance less than 15 km from each other are marked as a single point.
Bee colonies obtained from isolated apiaries of the Krasnoyarsk Krai are of considerable interest. The area with these isolated apiaries was not influenced by other subspecies of honeybee for many years, and all studied bees had only variant PQQ of the locus COI-COII mtDNA. However, when comparing the data of the morphometric study of bees from isolated apiaries with the Russian and European standards of the Apis. m. mellifera, the decrease of the lower limit values of cubital index was observed in the studied bees, and, as a result, for most bee colonies the deviation from the mean values of cubital index was shown (Table 1). There are several possible explanations for the results. First, this may be the result of genetic drift, the effect of which may be because of the fact that these apiaries are isolated and there are a limited number of bees. Second, the large scale of variability of the cubital index is the result of adaptation to the environment in more severe climatic conditions. Nevertheless, these isolated apiaries in the Krasnoyarsk Krai may be considered as a unique population of the Middle Russian bee that exists for a long time without affecting other subspecies of honeybee.
\nGeographical location: settlement | \nBee colony, № | \nCubital index, standard units | \nHantel index, standard units | \nDiscoidal shift, % | \n||||
---|---|---|---|---|---|---|---|---|
Lim:\n \n max\n | \nM ± m\n | \nLim:\n \n max\n | \nM ± m\n | \n– | \n0 | \n+ | \n||
Ostyatskoe | \n1 | \n\n 2.00 | \n1.61±0.04 | \n\n 0.892 | \n0.795±0.011 | \n100.0 | \n0 | \n0 | \n
2 | \n\n 1.74 | \n1.51±0.02 | \n\n 0.912 | \n0.849±0.012 | \n83.3 | \n16.7 | \n\n | |
3 | \n\n 1.74 | \n1.51±0.03 | \n\n 0.883 | \n0.837±0.008 | \n83.3 | \n16.7 | \n0 | \n|
4 | \n\n 1.67 | \n1.45±0.02 | \n\n 0.900 | \n0.837±0.009 | \n97.0 | \n3.0 | \n0 | \n|
5 | \n\n 1.79 | \n1.46±0.03 | \n\n 0.923 | \n0.842±0.010 | \n87.0 | \n13.0 | \n0 | \n|
Kolmogorovo | \n1 | \n\n 2.10 | \n1.60±0.05 | \n\n 0.900 | \n0.820±0.009 | \n97.0 | \n3.0 | \n0 | \n
2 | \n\n 1.76 | \n1.51±0.03 | \n\n 0.919 | \n0.845±0.008 | \n93.0 | \n7.0 | \n0 | \n|
3 | \n\n 1.86 | \n1.56±0.04 | \n\n 0.985 | \n0.810±0.011 | \n97.0 | \n3.0 | \n0 | \n|
4 | \n\n 1.76 | \n1.45±0.04 | \n\n 0.945 | \n0.830±0.011 | \n97.0 | \n3.0 | \n0 | \n|
Yaksha | \n1 | \n\n 1.85 | \n1.59±0.02 | \n\n 0.846 | \n0.775±0.008 | \n100.0 | \n0 | \n0 | \n
Standard for Apis mellifera mellifera | \n||||||||
I | \n\n | \n 2.10 | \n1.70 | \n\n 0.923 | \nNo data | \nNo data | \n||
II | \n\n | \n 1.90 | \n1.5 to 1.7 | \n\n 0.923 | \n91–100 | \n5–10 | \n0.00 | \n
Morphometric parameters (wing venation) of honeybee workers from 10 bee colonies of the Krasnoyarsk Krai (Yenisei population).
Minimum 30 samples from each bee colony were studied.
Lim, limits of value of the sing; M ± m, average value of the sign ± the standard error of the mean; I, European breed standard based on values of cubital and hantel indexes [12]; II, Russian breed standard.
The results of morphometric analysis confirmed the origin of bee colonies of Altai population from the Middle Russian race, but some influence of the southern races have been shown. For example, the parameter “Discoidal shift” deviates from the Russian breed standard: individuals with a positive value and zero of discoidal shift were found in bee colony No. 7 (Table 2).
\nIf bee colonies from the Krasnoyarsk Krai were obtained from the territory distant from the center and located in sparsely populated areas, in the taiga, the bee colonies from the Altai Krai inhabit the territory, characterized by high development of beekeeping and a constant active importation of bees of different origins.
\nThe results of the outward morphological characters-based diagnostics of honeybees (the cubital index, the hantel index, and the discoidal shift) received from 11 bee colonies differing in the variants of the COI-COII mtDNA locus are presented (Table 2). Only for 4 of the 11 bee colonies, a full compliance with the criteria of the breed according to the morphometric and mtDNA analysis (the three Apis mellifera mellifera colonies and one family of Apis mellifera carpatica) was shown. The remaining seven colonies are hybrid, and for three colonies a significant imbalance between genetic and morphometric parameters was shown. Hence, in order to determine the breeds in the conditions of mass bee hybridization, it is important to consider not only the features of mtDNA, but morphometric parameters as well, among which the discoidal shift is probably the most important.
\nThese data are consistent with the results of the research of hybrid apiary, where for many years (over 30) the Middle Russian bee was bred, but the last 10 years, the southern races have been actively imported [6]. More than 50% of individuals refer to the southern races according to mtDNA analysis (variant Q of the locus COI-COII; “southern” mitotype). But none of these individuals corresponded to the southern race according to morphometric analysis (Table 3). In 33% of cases, individuals with “southern” mitotype had two morphometric features characteristic to the Middle Russian race.
\nFor bees, originating from the Middle Russian race (variant PQQ of the locus COI-COII), full compliance between mitotype and morphometric parameters was found in approximately 6% of the individuals. 18% of bees had mitotype and two morphometric parameters which specific to the Middle Russian bees.
\nThis indicates a process of cross-breeding of Middle Russian and southern races on this apiary. However, the process of “ousting of genes” is derived differently for bees of different origin: for bees of Middle Russian race the process of “ousting of genes” is smaller in scale, as among individuals with variant PQQ a smaller percentage of bees with “southern” morphometric characters was registered in comparison with the same data shown for bees with “southern” mitotypes.
\nGeographical location | \nBee colony, № | \nNumber of studied bees | \nSequence composition of the COI- COII mtDNA locus | \nCubital index, standard units | \nHantel index, standard units | \n||||||
---|---|---|---|---|---|---|---|---|---|---|---|
region | \nDistrict | \nSettlement | \n|||||||||
Lim:\n \n max\n | \nM\n | \nsd\n | \nLim:\n \n max\n | \nM\n | \nsd\n | \n||||||
\n | Apis mellifera mellifera* | \n||||||||||
Tomsk region | \nTomsky | \np. Zarechnyi | \n1 | \n30 | \nPQQQ | \n\n 2.23 | \n1.66 | \n0.216 | \n\n 0.932 | \n0.826 | \n0.052 | \n
s. Kurlek | \n2 | \n28 | \nPQQQ | \n\n 3.29 | \n2.14 | \n0.376 | \n\n 1.053 | \n0.937 | \n0.055 | \n||
Zyryansky | \ns. Dubrovka | \n3 | \n30 | \nPQQ | \n\n 2.47 | \n1.69 | \n0.232 | \n\n 0.933 | \n0.849 | \n0.060 | \n|
Molchanovsky | \ns. Mogochino | \n4 | \n30 | \nPQQ | \n\n 2.56 | \n1.92 | \n0.290 | \n\n 1.000 | \n0.879 | \n0.055 | \n|
5 | \n43 | \nPQQ | \n\n 2.00 | \n1.73 | \n0.181 | \n\n 0.926 | \n0.821 | \n0.038 | \n|||
Altai Krai | \nZmeinogorsky | \nVicinity of c. Zmeinogorsk | \n6 | \n29 | \nPQQ | \n\n 2.00 | \n1.55 | \n0.232 | \n\n 0.967 | \n0.858 | \n0.062 | \n
7 | \n30 | \nPQQQ | \n\n 2.50 | \n1.80 | \n0.245 | \n\n 0.984 | \n0.845 | \n0.059 | \n|||
Krasno- yarsk Krai | \nYeniseisky | \np. Yaksha | \n8 | \n30 | \nPQQ | \n\n 1.85 | \n1.59 | \n0.132 | \n\n 0.846 | \n0.775 | \n0.044 | \n
\n | Southern breeds* | \n||||||||||
Tomsk region | \nTomsky | \ns. Semiluzhki | \n9 | \n50 | \nQ | \n\n 3.64 | \n2.51 | \n0.374 | \n\n 1.210 | \n1.050 | \n0.047 | \n
s. Kurlek | \n10 | \n29 | \nQ | \n\n 2.29 | \n1.66 | \n0.220 | \n\n 0.965 | \n0.878 | \n0.060 | \n||
p. Sinii Utes | \n11 | \n30 | \nQ | \n\n 2.87 | \n2.37 | \n0.334 | \n\n 1.053 | \n0.931 | \n0.065 | \n||
Standart of breeds | \nA. m. mellifera** | \nPQQ, PQQQ and other | \n\n 2.10 | \n1.70 | \n– | \n\n 0.923 | \n– | \n– | \n|||
A. m. mellifera\n*** | \n\n 1.90 | \n1.6 | \n– | \n\n 0.923 | \n– | \n– | \n|||||
A. m. carnica\n** | \nQ | \n\n 3.00 | \n2.7 | \n– | \n≥ 0.925 | \n– | \n– | \n||||
A. m. caucasica\n** | \nQ | \n\n 2.30 | \n2.0 | \n– | \nNo data | \n– | \n– | \n
Morphometric parameters (wing venation) of honeybee workers of 11 bee colonies from apiaries of Siberia.
*Breed indicated according to the data of mtDNA analysis.
**European breed standard based on values of cubital and hantel indexes [12].
***Russian breed standard. Discoidal shift are given according to Russian standards.
Lim, limits of values; М, arithmetic mean; sd, standard deviation.
Geographical location | \nBee colony, № | \nNumber of studied bees | \nSequence composition of the COI- COII mtDNA locus | \nDiscoidal shift, % | \n||||
---|---|---|---|---|---|---|---|---|
region | \nDistrict | \nSettlement | \n||||||
– | \n0 | \n+ | \n||||||
\n | Apis mellifera mellifera\n\n*\n | \n|||||||
Tomsk region | \nTomsky | \np. Zarechnyi | \n1 | \n30 | \nPQQQ | \n73.30 | \n26.70 | \n0.00 | \n
s. Kurlek | \n2 | \n28 | \nPQQQ | \n32.10 | \n53.60 | \n10.70 | \n||
Zyryansky | \ns. Dubrovka | \n3 | \n30 | \nPQQ | \n73.33 | \n26.67 | \n0.00 | \n|
Molchanovsky | \ns. Mogochino | \n4 | \n30 | \nPQQ | \n70.00 | \n30.00 | \n0.00 | \n|
5 | \n43 | \nPQQ | \n100.0 | \n0.00 | \n0.00 | \n|||
Altai Krai | \nZmeinogorsky | \nVicinity of c. Zmeinogorsk | \n6 | \n29 | \nPQQ | \n94.00 | \n6.00 | \n0.00 | \n
7 | \n30 | \nPQQQ | \n46.70 | \n46.70 | \n6.60 | \n|||
Krasnoyarsk Krai | \nYeniseisky | \np. Yaksha | \n8 | \n30 | \nPQQ | \n100.0 | \n0.00 | \n0.00 | \n
\n | Southern breeds* | \n|||||||
Tomsk region | \nTomsky | \ns. Semiluzhki | \n9 | \n50 | \nQ | \n4.00 | \n20.00 | \n76.00 | \n
s. Kurlek | \n10 | \n29 | \nQ | \n72.40 | \n27.60 | \n0.00 | \n||
p. Sinii Utes | \n11 | \n30 | \nQ | \n6.70 | \n76.70 | \n16.70 | \n||
Standart of breeds | \nA. m. mellifera\n\n**\n | \nPQQ, PQQQ and other | \n– | \n– | \n– | \n|||
A. m. mellifera\n*** | \n91–100 | \n5–10 | \n0.00 | \n|||||
A. m. carnica\n\n**\n | \nQ | \n0–5 | \n0–20 | \n80–100 | \n||||
A. m. caucasica\n\n**\n | \nQ | \n60–70 | \n20–30 | \n3–5 | \n
Continued.
Lim, limits of values; М, arithmetic mean; sd, standard deviation.
*Breed indicated according to the data of mtDNA analysis.
**European breed standard based on values of cubital and hantel indexes [12].
***Russian breed standard. Discoidal shift are given according to Russian standards.
mtDNA | \nVariant PQQ | \nVariant Q | \n|||
---|---|---|---|---|---|
Number of studied bees, % | \n44.44 | \n55.56 | \n|||
Race | \nApis mellifera mellifera | \nSouthern race | \nApis mellifera mellifera | \nSouthern race | \n|
The combination of features characteristic for different races | \n3 parameters х1 + х2 + х3 | \n5.6 | \n7.4 | \n7.4 | \n0.0 | \n
2 parameters, total, including х1 + х2 х1 + х3 х2 + х3 | \n18.5 1.9 3.7 13.0 | \n13.0 1.9 11.1 0 | \n33.3 1.9 0 31.5 | \n14.8 11.1 3.7 0 | \n|
1 parameter, total | \n13.0 | \n18.5 | \n14.8 | \n33.3 | \n
The accordance of morphometric parameters in individuals with different genetic variants of the COI-COII mtDNA locus (see details in reference [6]).
х1, х2, х3 are parameters of cubital index, hantel index, and discoidal shift, respectively.
Thus, the result of study of hybrid apiaries and bee colonies indicate, on the one hand, the importance and the necessity of a comprehensive approach to the exact characterization of honeybee races. On the other hand, the results are of scientific interest for the study of genetic processes during hybridization of different subspecies of honeybee and for analyzing the process of “ousting of genes” of one race by genes of other race. For example, hybridization between the Middle Russian bee and Carpathian bee is of interest because the races belong to different evolutionary branches.
\nFor such studies, microsatellite loci are the most informative molecular genetic markers. Microsatellite markers can be useful for the study of genetic structure of different honeybee populations and bee colonies, evaluation of genetic diversity and introgressive hybridization, differentiation of different subspecies (ecotypes), the establishment of evolutionary relationships and adaptive features of four evolutionary branches (A, M, C, and O), mapping quantitative trait loci (QTL), and search of genetic markers associated with economically significant characteristics [3,7,13–46].
\nCharacterization of the allele spectrum of microsatellite loci and analysis of their variability in subspecies, colonies, and individuals in the honeybee populations is the initial stage of any of the above research.
\nVariability of eight microsatellite loci (А008 (=A8), Ap049, AC117, AC216, Ap243, H110, A024, and A113) in honeybee from Siberian region was studied. Seven loci were polymorphic and only for AC216 locus one homozygous genotype was registered in all the studied bees (allele 91 bp). For each locus, the range and frequency of genotypes and alleles were determined (Table 4).
\n\nLocus | \nGenotype | \nFrequency of genotype | \nAllelic frequency with an error | \n
---|---|---|---|
A008\n | \n152–152 | \n0.006 | \nР152=0.0311±0.0054 Р162=0.8049±0.0123 Р166=0.0010±0.0031 Р168=0.0010±0.0031 Р170=0.0213±0.0045 Р172=0.0243±0.0048 Р174=0.0825±0.0086 Р176=0.0029±0.0017 Р178=0.0262±0.0050 Р180=0.0039±0.0019 | \n
152–162 | \n0.049 | \n||
152–170 | \n0.002 | \n||
162–162 | \n0.736 | \n||
162–168 | \n0.002 | \n||
162–170 | \n0.016 | \n||
162–172 | \n0.039 | \n||
162–174 | \n0.033 | \n||
166–172 | \n0.002 | \n||
170–170 | \n0.006 | \n||
170–174 | \n0.016 | \n||
172–172 | \n0.004 | \n||
174–174 | \n0.037 | \n||
174–176 | \n0.004 | \n||
174–178 | \n0.031 | \n||
174–180 | \n0.008 | \n||
176–178 | \n0.002 | \n||
178–178 | \n0.010 | \n||
\n | n=515 | \n\n | \n |
Ap049 | \n118–127 | \n0.002 | \nР118=0.0010±0.0001 Р121=0.0069±0.0025 Р127=0.6581±0.0149 Р130=0.1759±0.0120 Р139=0.1403±0.0109 Р142=0.0010±0.0001 Р152=0.0168±0.0040 | \n
121–127 | \n0.002 | \n||
121–130 | \n0.006 | \n||
121–139 | \n0.006 | \n||
127–127 | \n0.529 | \n||
127–130 | \n0.187 | \n||
127–139 | \n0.053 | \n||
127–152 | \n0.019 | \n||
130–130 | \n0.055 | \n||
130–139 | \n0.045 | \n||
130–152 | \n0.002 | \n||
139–139 | \n0.081 | \n||
139–152 | \n0.013 | \n||
142–152 | \n0.002 | \n||
152–152 | \n0.002 | \n||
\n | n=506 | \n\n | \n |
АC117 | \n175–175 | \n0.008 | \nР175=0.0910±0.0092 Р179=0.0879±0.0090 Р183=0.8211±0.0123 | \n
175–179 | \n0.020 | \n||
175–183 | \n0.145 | \n||
179–179 | \n0.012 | \n||
179–183 | \n0.131 | \n||
183–183 | \n0.683 | \n||
\n | n=489 | \n\n | \n |
H110 | \n162–162 | \n0.567 | \nР162=0.7522±0.0167 Р166=0.0627±0.0093 Р170=0.1851±0.0150 | \n
162-166 | \n0.116 | \n||
162–170 | \n0.254 | \n||
166–166 | \n0.003 | \n||
166–170 | \n0.003 | \n||
170–170 | \n0.057 | \n||
\n | n=335 | \n\n | \n |
Characterization of variability of seven microsatellite loci in honeybees from Siberia.
n, number of studied samples is indicated in bold.
Locus | \nGenotype | \nFrequency of genotype | \nAllelic frequency with an error | \n
---|---|---|---|
Ap243 | \n255–255 | \n0.401 | \nР255=0.5278±0.0222 Р263=0.3175±0.0207 Р269=0.0833±0.0123 Р272=0.0635±0.0109 Р275=0.0079±0.0039 | \n
255–263 | \n0.167 | \n||
255–269 | \n0.056 | \n||
255–272 | \n0.028 | \n||
255–275 | \n0.004 | \n||
263–263 | \n0.175 | \n||
263–269 | \n0.075 | \n||
263–272 | \n0.040 | \n||
263–275 | \n0.004 | \n||
269–269 | \n0.004 | \n||
269–272 | \n0.028 | \n||
272–272 | \n0.012 | \n||
272–275 | \n0.008 | \n||
\n | n=252 | \n\n | |
A024 | \n94–94 | \n0.344 | \nР94=0.4736±0.0186 Р96=0.1014±0.0112 Р98=0.0375±0.0070 Р100=0.0194±0.0051 Р102=0.2097±0.0152 Р104=0.1528±0.0134 Р106=0.0056±0.0028 | \n
94–98 | \n0.036 | \n||
94–100 | \n0.033 | \n||
94–102 | \n0.175 | \n||
94–104 | \n0.014 | \n||
96–96 | \n0.067 | \n||
96–104 | \n0.058 | \n||
96–106 | \n0.011 | \n||
98–98 | \n0.019 | \n||
100–100 | \n0.003 | \n||
102–102 | \n0.089 | \n||
102–104 | \n0.067 | \n||
104–104 | \n0.083 | \n||
\n | n=360 | \n\n | |
А113 | \n208–212 | \n0.003 | \nР208=0.0013±0.0013 Р210=0.0144±0.0043 Р212=0.2350±0.0153 Р214=0.0026±0.0018 Р218=0.5953±0.0177 Р220=0.1084±0.0112 Р222=0.0013±0.0013 Р224=0.0013±0.0013 Р226=0.0183±0.0048 Р228=0.0196±0.0050 Р232=0.0026±0.0018 | \n
210–210 | \n0.003 | \n||
210–218 | \n0.021 | \n||
210–220 | \n0.003 | \n||
212–212 | \n0.177 | \n||
212–214 | \n0.005 | \n||
212–218 | \n0.078 | \n||
212–220 | \n0.013 | \n||
212–222 | \n0.003 | \n||
212–226 | \n0.005 | \n||
212–228 | \n0.003 | \n||
212–232 | \n0.005 | \n||
218–218 | \n0.475 | \n||
218–220 | \n0.117 | \n||
218–226 | \n0.021 | \n||
218–228 | \n0.003 | \n||
220–220 | \n0.018 | \n||
220–224 | \n0.003 | \n||
220–226 | \n0.010 | \n||
220–228 | \n0.034 | \n||
\n | n=383 | \n\n | \n |
Continued.
Microsatellite loci differed in variability: the minimum number of alleles was detected for loci AC117 and H110 (3 alleles) and the maximum number of alleles was registered for loci A008 (10 alleles) and A113 (11 alleles). At the same time, for six of the seven polymorphic loci (except locus A024), one major allele with a frequency of more than 0.5 (from 0.5278 for allele “255”of locus Ap243 to 0.8211 for allele “183” of locus AC117) was registered regardless of the number of detected alleles.
\nTo identify the features of honeybee from different geographical areas, the comparative analysis of the variability of the studied loci was carried out for the bees of Apis mellifera mellifera (= dark-colored forest bee, Middle Russian race) of four populations (Siberia, the Urals, and Europe) using our own data (Tomsk region and Krasnoyarsk Krai) and literature data [15,16,47] (Tables 5 and 6). The Ural population (Bashkir population) located in the nature reserve is a unique population of the dark-colored forest bee (Burzyan bee).
\nLocus | \nAlleles (pb) | \nAllelic frequency | \n|||||
---|---|---|---|---|---|---|---|
Russia | \nEurope** | \n||||||
Krasnoyarsk Krai | \nTomsk region | \nUral* (Bashkor tostan) | \nBelgium (Chimay) | \nSweden (Umea) | \nFrance (eight geographic areas) | \n||
A008 | \n148 | \n\n | \n | \n | 0.783 | \n0.727 | \n0.267–0.969 | \n
152 | \n\n | 0.006 | \n\n | \n | \n | \n | |
154 | \n\n | \n | 0.897 | \n\n | \n | 0–0.083 | \n|
155 | \n\n | \n | \n | \n | \n | 0–0.033 | \n|
156 | \n\n | \n | 0.053 | \n0.133 | \n0.227 | \n0.017–0.300 | \n|
157 | \n\n | \n | \n | \n | \n | 0–0.050 | \n|
158 | \n\n | \n | 0.053 | \n\n | 0.023 | \n0–0.117 | \n|
159 | \n\n | \n | \n | \n | \n | 0–0.017 | \n|
160 | \n\n | \n | \n | 0.050 | \n\n | 0–0.100 | \n|
162 | \n1.000 | \n0.912 | \n\n | 0.033 | \n\n | 0–0.034 | \n|
164 | \n\n | \n | \n | \n | 0.023 | \n0–0.020 | \n|
166 | \n\n | 0.003 | \n\n | \n | \n | 0–0.017 | \n|
170 | \n\n | 0.003 | \n\n | \n | \n | \n | |
172 | \n\n | 0.032 | \n\n | \n | \n | \n | |
174 | \n\n | 0.044 | \n\n | \n | \n | \n | |
N | \n120 | \n170 | \n48 | \n60 | \n44 | \n634 | \n|
A024 | \n\n | \n | \n | \n | No data | \nNo data | \n\n |
94 | \n0.216 | \n0.741 | \n\n | \n | |||
96 | \n0.358 | \n\n | \n | \n | |||
98 | \n0.132 | \n\n | 0.896 | \n0.804 | \n|||
100 | \n0.034 | \n0.020 | \n\n | \n | |||
102 | \n0.025 | \n0.227 | \n\n | \n | |||
104 | \n0.216 | \n0.012 | \n\n | \n | |||
106 | \n0.020 | \n\n | 0.104 | \n0.130 | \n|||
108 | \n\n | \n | \n | 0.065 | \n|||
N | \n102 | \n172 | \n48 | \n\n | \n | 46 | \n|
A113 | \n202 | \n\n | \n | \n | 0.083 | \n0.024 | \n0.017–0.267 | \n
204 | \n\n | \n | \n | \n | \n | \n | |
208 | \n\n | \n | \n | \n | \n | 0–0.017 | \n|
210 | \n0.021 | \n0.009 | \n\n | \n | \n | \n | |
212 | \n\n | 0.174 | \n\n | \n | \n | 0–0.030 | \n|
214 | \n\n | 0.006 | \n\n | 0.033 | \n\n | 0.010–0.500 | \n|
216 | \n\n | \n | 0.063 | \n\n | \n | 0–0.017 | \n|
218 | \n0.898 | \n0.540 | \n0.865 | \n\n | \n | 0–0.020 | \n|
220 | \n0.081 | \n0.183 | \n0.042 | \n0.833 | \n0.857 | \n0.433–0.810 | \n|
222 | \n\n | 0.003 | \n0.032 | \n\n | 0.024 | \n0–0.041 | \n|
224 | \n\n | 0.003 | \n\n | 0.017 | \n0.048 | \n0–0.060 | \n|
226 | \n\n | 0.040 | \n\n | \n | 0.048 | \n0–0.034 | \n|
228 | \n\n | 0.043 | \n\n | 0.017 | \n\n | 0.017–0.071 | \n|
230 | \n\n | \n | \n | \n | \n | 0–0.052 | \n|
232 | \n\n | \n | \n | \n | \n | 0–0.017 | \n|
234 | \n\n | \n | \n | 0.017 | \n\n | 0–0.017 | \n|
236 | \n\n | \n | \n | \n | \n | 0–0.020 | \n|
238 | \n\n | \n | \n | \n | \n | 0–0.017 | \n|
240 | \n\n | \n | \n | \n | \n | 0–0.010 | \n|
N | \n118 | \n175 | \n48 | \n60 | \n44 | \n634 | \n
Allele frequency at three loci in honeybees from different geographic areas of Russia and Europe.
*data from reference [47].
**data from references [15,16].
N, number of studied samples.
The minimum and maximum values of allelic frequencies represented for loci A008 and A113 in honeybees of France populations; allelic frequencies for locus A024 are given for bees of only Northern France population.
Locus | \nAlleles (pb) | \nAllelic frequency | \n||
---|---|---|---|---|
Siberia | \nUral | \n|||
Krasnoyarsk Krai | \nTomsk region | \nBashkortostan | \n||
Ap049 | \n118 | \n0.005 | \n\n | \n |
121 | \n0.005 | \n0.003 | \n\n | |
123 | \n\n | \n | 0.917 | \n|
127 | \n0.810 | \n0.711 | \n\n | |
130 | \n0.138 | \n0.249 | \n0.063 | \n|
138 | \n\n | \n | 0.021 | \n|
139 | \n0.014 | \n0.037 | \n\n | |
152 | \n0.029 | \n\n | \n | |
Number of studied samples | \n105 | \n175 | \n48 | \n|
Ap243 | \n254 | \n\n | \n | 0.646 | \n
255 | \n0.280 | \n0.524 | \n\n | |
257 | \n\n | \n | 0.354 | \n|
263 | \n0.542 | \n0.254 | \n\n | |
269 | \n0.140 | \n0.056 | \n\n | |
272 | \n0.037 | \n0.143 | \n\n | |
275 | \n\n | 0.024 | \n\n | |
Number of studied samples | \n107 | \n63 | \n48 | \n|
H110 | \n160 | \n\n | \n | 0.615 | \n
162 | \n0.624 | \n0.837 | \n\n | |
163 | \n\n | \n | 0.302 | \n|
166 | \n0.376 | \n0.056 | \n\n | |
168 | \n\n | \n | 0.083 | \n|
170 | \n\n | 0.107 | \n\n | |
Number of studied samples | \n117 | \n135 | \n48 | \n
Allele frequency at three loci in honeybees from different populations of Russia.
N, number of studied samples.
Siberian populations (Tomsk region and Krasnoyarsk Krai) are closest in spectrum and allele frequencies of most studied loci (A008, Ap049, A113, Ap243, H110). The Ural population located to the west of Siberian region differs from Siberia for some loci: for locus A008 differences were registered in the spectrum of alleles, for the locus A024—in the frequency of alleles, for the loci Ap049 and Ap243—in both the spectrum and frequency of alleles. It is remarkable that the Ural population has a greater similarity in the spectrum of alleles of loci A024 and A008 to European populations.
\nThe differences in the spectrum of alleles and the frequency of allele registration for locus A008 were revealed in honeybees of Siberia, the Ural, and European populations. For honeybees of the Ural and Europe, shorter alleles of locus A008 were predominant (154 bp and 148 bp, respectively), whereas for bees from Siberia allele “162” was the most specific. Probably this locus should be considered as a marker related to geographic and environmental conditions (specific adaptation to local conditions) [1,3,48,49] because the different populations of dark-colored forest bee (European, Ural, and Siberian populations) were compared in this study.
\nFor some loci, for example A113, allelic spectrum overlaps, but the frequency of the alleles was different in honeybees of different populations. Different factors of population dynamics (such as founder effect, genetic drift, natural selection) can be causes of this phenomenon.
\nThus, it is shown that for some loci the specific distribution of genotypes and alleles were detected in the bees, which differ by geographical location. Further research is needed and the expansion of gene-geographic studies of honeybee is relevant.
\nTo assess the informativeness of studied loci for the differentiation of different subspecies of honeybee, the comparison of the spectrum of predominant alleles in bees of different evolutional branches (M and C) and from different geographical localization was conducted (Table 7). Comparison of the data on the variability of microsatellite loci studied in bees of different origin and different geographical location allows making some conclusions and adjustments with respect to informativeness of these loci as markers for differentiation of subspecies of honeybee.
\nFor locus A008, the differences in the spectrum of the most common alleles are registered between the Apis m. mellifera living in different geographical regions (as shown above), and between the two southern races (Apis m. caucasica and Apis m. carpatica).
\nFor locus A113 clear differences in length of the most frequently detected allele were not detected both among bees of a common origin and between bees belonging to different races. Probably this locus cannot be considered informative for determining of the subspecies.
\nLoci A024 and Ap049 are of considerable interest for further study as candidate markers for inclusion in the diagnostic panel, differentiating subspecies. So, in general, for the locus A024 the majority of bees and bee colonies Apis m. mellifera, regardless of their habitat, are characterized by shorter length of alleles. Perhaps, for locus Ap049 the differences exist in the allelic spectrum between bees belonging to different races.
\n\n | Geographical location | \nSequence composition of the COI-COII mtDNA locus (breed) | \nPredominant allele | \nAllelic frequency | \n
---|---|---|---|---|
Locus A008 | \nTomsk region | \nPQQ/PQQQ | \n162 | \n0.71–1.00 | \n
Krasnoyarsky Krai | \nPQQ | \n162 | \n1.00 | \n|
Ural (Bashkir population)1 | \nPQQ | \n154 | \n0.63–1.00 | \n|
Tomsk region2 | \nQ | \n174 | \n0.58–0.61 | \n|
Sochi area3 | \nQ | \n158 | \n0.88–1.00 | \n|
Europe4 | \nA.m.mellifera | \n148 | \n0.27–0.97 | \n|
Locus A113 | \nTomsk region | \nPQQ/PQQQ | \n218 212 220 | \n0.67–0.82 0.61 0.50 | \n
Krasnoyarsky Krai | \nPQQ | \n218 | \n0.85–0.95 | \n|
Ural (Bashkir population)1 | \nPQQ | \n218 220 | \n0.50–1.00 0.50 | \n|
Tomsk region2 | \nQ | \n212 | \n0.94–1.00 | \n|
Sochi area3 | \nQ | \n222 | \n0.50 | \n|
Europe4 | \nA.m.mellifera | \n220 | \n0.433–0.857 | \n|
Locus A024 | \nTomsk region | \nPQQ/PQQQ | \n94 102 | \n0.60–0.90 0.54 | \n
Krasnoyarsky Krai | \nPQQ | \n98 96 | \n0.50 0.50–0.71 | \n|
Ural (Bashkir population)1 | \nPQQ | \n98 106 | \n0.50–1.00 0.50 | \n|
Tomsk region2 | \nQ | \n104 | \n0.65 | \n|
Sochi area3 | \nQ | \n106 | \n0.88–1.00 | \n|
Europe4 | \nA.m.mellifera | \n98 | \n>0.80 | \n|
Locus АР049 | \nTomsk region | \nPQQ/PQQQ | \n127 130 | \n0.62–0.92 0.77 | \n
Krasnoyarsky Krai | \nPQQ | \n127 | \n0.50–0.96 | \n|
Ural (Bashkir population)1 | \nPQQ | \n129 130 | \n0.50–1.00 1.00 | \n|
Tomsk region2 | \nQ | \n139 | \n0.66–1.00 | \n|
Sochi area3 | \nQ | \n139 | \n1.00 | \n
Comparative analysis of the frequency of the most common alleles* of microsatellite loci in honeybees of different maternal origins and geographic localization.
*Data on allelic frequencies, the frequency of which = or > 0.5 are shown.
1Data on the Ural (Bashkir population) are taken from reference [47].
2Our own data for the Carpathian breed (Apis m. carpatica) imported into the territory of the Tomsk region from Carpathian breed nursery (d. Mukachevo, Ukraine).
3Data on the Caucasian honeybee (Apis m. caucasica) from the Sochi area are taken from reference [47].
4Data on the European population are taken from references [15,16].
In order to determine the subspecies status of an individual honeybee, a honeybee colony, or a honeybee population, it is important to compare allelic counts and genotypes across different studies. However, no standard reference material, such as a standard allelic ladder, is available for honeybees [3]. In addition, the spectrums of analyzed microsatellite markers often do nоt overlap and primary data on the allele spectrum and allele frequencies are not always presented in publications. In general, the present stage of the study of variability of microsatellite loci in Apis mellifera can be considered as a period of accumulation of information. At this stage of the study of honeybee it should be with caution relate to the use autosomal loci to determine the subspecies of honeybee.
\nImportation of races of southern origin to the territory of Siberia, where the Middle Russian breed for a long time lived, on the one hand, led to a massive hybridization of bees, a loss of purebred, decreased immunity, and increased incidence of bees. On the other hand, the import of bee families from other areas (the European part of Russia, Uzbekistan), disadvantageous in the epidemiological situation, led to the spread of diseases that have not previously registered in the territory of Siberia.
\nThis situation was evaluated for nosemosis: the distribution Nosema infection throughout Siberia was studied, the species of microsporidia were determined, and the origin of bee colonies infected with Nosema was investigated.
\nNosemosis is a parasitic disease of adult honeybees (Apis mellifera L.) caused by two described species of microsporidia, Nosema apis [50] and Nosema ceranae [51]. The disease occurs throughout the world, causes significant detriment to honey production, and results in economic losses. The original assumption was that N. apis specifically infects the European honeybee A. mellifera, causing nosemosis, and that N. ceranae is a specific pathogen of the Asian honeybee, A. cerana. Recently, it became evident that N. ceranae is also widespread in the A. mellifera population throughout the world and is already found in North and South America, across Europe and Asia [52–58]. It has been subsequently detected across Canada and the United States [59,60] and has been confirmed in Central America [61], Australia [62], and North Africa [63].
\nThe geographical distribution of Nosema in Russia is not well known [64,65]. In addition, information on the prevalence of N. ceranae in Russia, including Siberia, is not complete [66]. Previously, nosemosis in honeybees in Siberia was attributed exclusively to N. apis. The problem of the distribution of Nosema and the consequences of infection for honeybees has not yet been resolved. The effects of the Nosema infection on survival and productivity of honeybees are not well studied.
\nFor the period of 2012–2015, a screening study of 132 bee colonies from 68 apiaries of Siberia for the presence of Nosema spores was carried out [65]. For an objective evaluation, the different methods were used: microscopy and PCR. We found that honeybees of 33 colonies from 132 studied (25.0%) and 21 apiaries from 68 studied (30.9%) had spores detectable by light microscopy. As it is difficult to distinguish N. ceranae and N. apis morphologically, a PCR assay based on 16S ribosomal RNA has been used to differentiate N. apis and N. ceranae. To characterize further the identity of which species of Nosema was present, we performed PCR using primers specific for either N. apis or N. ceranae. Nosema positive samples (determined from light microscopy of spores) of adult worker bees from 33 bee colonies (21 apiaries) were tested to determine Nosema species using PCR primers of the 16S rRNA gene specific for N. ceranae or N. apis.
\nThe samples of 28 bee colonies from 33 infected colonies (84.8%) from 19 apiaries were positive by PCR using N. apis specific primers, and the samples from three colonies (3/33, 9.1%) were positive for N. ceranae (only two of apiaries). Samples co-infected with both N. ceranae and N. apis were registered in two bee colonies (2/33, 6.0%) from two apiaries. To confirm the PCR findings, the DNA fragments were sequenced. Sequence analysis revealed a complete sequence identity for N. apis (GenBank Accession No U97150) and N. ceranae (GenBank Accession No DQ486027).
\nNosema-infected bees were found in samples collected from five districts and mainly in the southern climatic areas (temperate continental parts of Siberia) (Figure 5). In the northern district (Figure 5, C – Chainsky) bees infected by Nosema apis are imported from Uzbekistan. It was established that Nosema ceranae revealed in bees from the southern districts of the Tomsk region (Figure 5, Shegarsky and Tomsky districts) was introduced with infected bees from southern regions of Russia.
\nDistribution of Nosema in the honeybee colonies (Apis mellifera L.) throughout the Tomsk region (Western Siberia) (dots A–I). Bee colonies not infected by Nosema are indicated in yellow; bee colonies corresponding to infection by N. apis or N. ceranae are indicated in black and green, respectively. Sectors in circles indicate representation cases (existence/absence) of an infection without frequency. Literas (A–I) indicate the districts of the Tomsk region: A, Parabelsky; B, Kolpashevsky; C, Chainsky; D, Molchanovsky; E, Krivosheinsky; F, Asinovsky; G, Zyryansky; H, Shegarsky; I, Tomsky.
The studied bees from apiaries of Krasnoyarsk Krai and Altai Krai were not infected with Nosema.
\nReports on the impact of N. ceranae infections on honeybee health and colony survival are contradictory, and various symptoms of the disease have been described [4,48,52,54–56,59,60,67–76]. Adult bees become infected by ingesting Nosema spores, which germinate in the midgut and infect cells of the midgut epithelium. Nosema infection caused by N. apis is characterized mainly by dysentery, whereas N. ceranae is described as causing death of individuals and colonies not preceded by any visible symptoms [9,68]. Nosema apis infection is restricted to the midgut epithelium [77], whereas N. ceranae has also been detected by molecular methods in other bee tissues such as malpighian tubules and hypopharyngeal glands [78].
\nPerhaps, N. ceranae is the most aggressive of the two Nosema species in relation to the host and appears to be replacing N. apis in some populations of honeybees.
\nCurrently, several reasons for the widespread presence of the parasite N. ceranae in the world and its displacement of N. apis are discussed in the literature. On the one hand, nosemosis produced by N. ceranae is considered a global problem because this parasite has wide prevalence in multiple hosts [79]. Nosema ceranae is a more aggressive parasite compared with N. apis, and consequently, it is more widespread than N. apis. On the other hand, the killing of honeybee colonies by N. ceranae could be a regional problem rather than a global phenomenon [80], and the virulence of N. ceranae could be influenced by climatic conditions [81–85] or might actually depend on honeybee race and honeybee genetic diversity [4,48,74,75,86–88].
\nIt is assumed that the level of infestation in honeybees can be associated with the race and the origin (local or non-local) of the bees. Some differences in the resistance to Nosema have been shown in Russian bee breeds [86]. Levels of N. ceranae infestation differed significantly between lineages and colonies for both Russian and Italian workers [87]. Unlike genetically homogeneous Italian lines [87], Russian bee lineages have a high genetic diversity and are characterized by high resistance to disease. Differences in infection levels were significant between local and introduced bee colonies [4,74]. The use of local honeybees provides a higher chance of colony survival because of their adaptation to regional environmental factors such as climate and vegetation [48,75].
\nTo determine if the infection incidence of bees by Nosema is associated with the races of bees, we analyzed breeds of bee derived from Nosema-positive colonies using morphometric (wing venation) and molecular-genetic (mtDNA) analyses (Table 8). The results of molecular genetic analysis (COI-COII locus) of honeybees have been published in reference [11]. According to the mtDNA analysis, PQQ and PQQQ variants of the locus COI-COII (A. m. mellifera, evolutionary branch M) were detected in two colonies (families No. 2 and 4), and Q variant (evolutionary branch C) was registered in four colonies (families No. 1, 3, 5, and 7). Family No. 6 had bees with different variants of the COI-COII locus (PQQ and Q) and apparently was formed by mixing two colonies having different origins. As a result, morphometric studies have shown that colonies No. 1, 3, 4, and 6 can be considered as subspecies of A. m. mellifera and that colonies No. 2, 5, and 7 are hybrids. However, according to the combined morphometric and mtDNA analysis, only family No. 4 can be considered as A. m. mellifera, whereas six Nosema-infected bee colonies did not correspond to any of the standards but were honeybee hybrids (Table 8). Furthermore, some colonies that were observed not only differed in morphometric parameters compared with the standards but in a mismatch of morphometric data and results of the mtDNA analysis for the two honeybee colonies. Honeybees infected with N. apis (colony No. 3) and bees infected with N. ceranae (colony No. 1) correspond to the A. m. mellifera race (branch M) according to the morphometric analysis, whereas the results of the mtDNA analysis confirmed the origin of these bees from branch C. Thus, our results indicate that examined honeybees infected with Nosema could be of hybrids of the two races (Apis m. mellifera and Apis m. carpatica).
\n№ colonies | \nNosema species | \nSequence composition of the COI-COII mtDNA locus | \nMorphometric parameters | \n|||
---|---|---|---|---|---|---|
Cubital index, standard units | \nHantel index, standard units | \n|||||
Lim: max | \nM ± m | \nLim: max | \nM ± m | \n|||
1 | \nN. ceranae | \nQ | \n\n 2.29 | \n1.66 ± 0.04 | \n\n 0.965 | \n0.878 ± 0.011 | \n
2 | \nN. ceranae | \nPQQQ | \n\n 3.29 | \n2.14 ± 0.07 | \n\n 1.053 | \n0.937 ± 0.010 | \n
3 | \nN. apis | \nQ | \n\n 2.11 | \n1.70 ± 0.03 | \n\n 0.917 | \n0.804 ± 0.011 | \n
4 | \nN. apis | \nPQQ | \n\n 2.80 | \n1.78 ± 0.06 | \n\n 1.0 | \n0.846 ± 0.013 | \n
5 | \nN. apis | \nQ | \n\n 2.82 | \n1.90 ± 0.06 | \n\n 1.176 | \n0.880 ± 0.018 | \n
6 | \nN. apis | \nPQQ/Q | \n\n 2.80 | \n1.73 ± 0.06 | \n\n 1.0 | \n0.834 ± 0.015 | \n
7 | \nN. apis | \nQ | \n\n 2.35 | \n1.86 ± 0.04 | \n\n 1.057 | \n0.885 ± 0.011 | \n
Standard breeds (subspecies)** | \n||||||
A. m. mellifera | \nPQQ, PQQQ and other | \n\n 2.1 | \n1.7 | \n\n 0.923 | \nNo data | \n|
A. m. carpatica | \nQ | \n\n 3.0 | \n2.65 | \n≥0.925 | \nNo data | \n
Characterization honeybee colonies infested by Nosema*.
*Thirty samples of bees were examined in each family.
**Definition of subspecies was carried out based on European standard honeybee [12].
M ± m, average value of the sign ± the standard error of the mean.
For comparison, the assessment of the origin of the bee colonies not infected with Nosema (24 families from 38 analyzed) was carried out using morphometric and mtDNA analysis. Among the 24 bee colonies not infected with Nosema, 18 bee colonies were identified as A. m. mellifera (75.0 %), 3 colonies were identified as A. m. carpatica (12.5 %), while 3 colonies were identified as hybrids (12.5 %).
\nAt present, the cold climate is considered as one of the limiting factors of N. ceranae distribution. It appears that the spread of N. ceranae across the globe is reduced in colder climates [81,82], as N. ceranae spores are capable of surviving high temperatures (60 °C) and desiccation, but they are intolerant of cold (4 °C) [81,82,89]. The marked decrease in N. ceranae spore germination was observed after even a short exposure to low temperatures (4 °C) [82]. In warmer climates, N. ceranae is more competitive than N. apis [48,82], but the spores of N. ceranae appear to be much more vulnerable than the spores of N. apis, in particular, to freezing, and the apparent replacement of N. apis for N. ceranae remains enigmatic [83].
\nThe different prevalence of N. ceranae may simply reflect its time of arrival, by natural spread or by the importation of infected honeybees, and mobility of bees within a country. Reduced or inhibited N. ceranae spore germination at low temperatures should hamper the infectivity and spread of this pathogen in climatic regions characterized by a rather cold winter season [82]. The presence of N. ceranae in the Tomsk region (Western Siberia, Russian) was reported previously by us [65,66] confirms the fact of a widespread N. ceranae infection in honeybee population throughout the world. However, we found N. ceranae-infected bee colonies in cold climate with long winters and humid summers, and this parasite is not associated with colony depopulation or honeybee collapse. We established that these previously infected colonies had been imported from other areas of Russia. The fact that N. ceranae is registered in the territory of Siberia with its severe climatic conditions does not agree with data on a weak survival of spores at low temperatures. At the same time, the colonies infected with Nosema (N. apis or N. ceranae) are found predominantly in the southern areas of the Tomsk region, which is characterized by more developed beekeeping and active delivery of breeds of southern origin (A. m. caucasica and A. m. carpatica) that leads to massive honeybee hybridization. Introgressive hybridization modifies the genetic pool of local honeybee populations, leading to the loss of their genetic identity [4]. The process of hybridization of different subspecies of honeybees can cause a destruction of evolutionarily developed gene complexes, leading to a decrease in the adaptive properties of organisms and populations and to a change in biological and economically significant characteristics of honeybees. The observed widespread hybridization of honeybees and the formation of hybrid bees will certainly contribute to the spread of disease.
\nIn our research, the majority of bee colonies infected by Nosema were hybrids. This finding is consistent with the view that hybrid forms are poorly adapted to changing environmental conditions and less resistant to the disease. Therefore, our results on the Nosema infestation of bee colonies are not surprising. At the same time, it is impossible to make a conclusion about the pathogenicity of a parasite based on our data. Perhaps, hybrids are characterized by other developmental conditions of the parasite in comparison with pure breeds that do not realize the pathogenicity of N. ceranae in the host. Also, there is an open question about the distribution of a Nosema in the northern part of the Tomsk region (influence of a cold climate, insignificant number of hybrids, etc.) where the colonies infected with Nosema were not detected except Chainsky district (N. apis-infected bees were imported from Uzbekistan). Siberia can be an ideal location to study how the spread of this disease correlates with climatic conditions and how the disease moves to particularly remote areas. This is an especially intriguing thought since changes in disease prevalence and pathogen virulence because of climatic change are widely discussed [80]. Obviously, more research is needed to elucidate the full effect of N. ceranae infection in A. mellifera colonies in different geographical areas and to understand if individual virulence levels and colony virulence levels differ between the two parasites.
\nThis study of honeybees in Siberia shows the need for a comprehensive approach to the study of various aspects of the honeybee, such as differentiation of subspecies, the role of environmental (geographical) factors in the formation of the genetic diversity of bees, and the incidence of bees.
\nThe primary task of the study of the genetic diversity of honeybees is to determine their subspecies composition. When performing gene-geographical research, it is important to consider the assessment of adaptive and selective significance of genetic markers. This is also important for the planning and conducting of works having applied nature.
\nAlong with exterior characters used for a long time to identify the breed of honeybees, molecular genetic techniques are actively applied. However, in connection with the high level of hybridization of bees, when about one-third of bee colonies show an imbalance between genetic and morphometric parameters, and in some cases, their complete mismatch occurs, a comprehensive analysis of the bees is necessary.
\nThe presence of hybrid forms in an area where the genetic diversity is studied, on the one hand, creates unfavorable background for conservation of gene pools of unique subspecies (for example, dark-colored forest bee), on the other hand, makes it difficult to search for adaptively significant and economically valuable traits (possible distortion of results and their interpretation). Therefore, it should be taken into account in conducting such studies. The above data also indicate that only the exterior or just genetic traits may be insufficient to determine the origin of bees and only the simultaneous analysis of morphometric parameters and data on the variability of locus COI-COII of mtDNA allow to evaluate the breed and cases of hybridization objectively.
\nIn the conditions of widespread crossbreeding of bees, genetic methods to control the purity of bee colonies must also be improved. Research in this direction is carried out by international and Russian researchers [43,47,90]. Therefore, on the basis of extensive research carried out on the territory of Eastern Europe (search of informative markers was conducted among more than 1,000 SNP using five different analytical methods), five panels, consisting of 48, 96, 144, 192, and 284 markers informative for determining the ancestral origin of species have been developed. The authors propose to use the results of this study to identify and evaluate the impurity of C-lines (in particular, Apis m. ligustica and Apis m. carnica) to the M-line (Apis m. mellifera) [43]. Russian researchers have only begun such studies, but the results obtained at this stage suggest that populations of honeybees living on the territory of Russia are characterized by wide genetic variability, and it is unlikely to develop a uniform panel of markers for the entire territory of the Russia for differentiation of the various breeds of bees. It is necessary to integrate the scientific achievements and results of the various laboratories and scientific groups of all over the world to establish general regularities of the genetic variability of the bees and to assess the adaptive and selective potential of honeybees.
\nThis study was supported by the Russian Foundation for Basic Research (research grant No 13-04-98116-r-siberia-а) and by the Tomsk State University Academic D.I. Mendeleev Fund Program in 2015 (research grant No 8.1.66.2015).
\nFormation of organisation that represents countries with similar interests or likeminded goals can be traced many decades ago. Some of those organisations are continentally focused (i.e. African Union, former Organisation of African Unity in 1963 and European Union in 1958-its original roots) while other are global (i.e. United Nations in 1945). Recently, we have seen organisations that are Transatlantic-Brazil, Russia, India, China and South Africa (BRICS hereafter) countries. South Africa (SA) joined BRIC countries in 2009 through invitation by other member states while the four founding members originate from a term coined by Jim O’Neil (former Managing Partner of Goldman Sachs). While the origination of the BRIC term is influenced by the economic similarities, there are other interesting similarities about BRIC countries. The similarities of BRICS nation are (i) political structure-ruling parties stay in power for least 10 years without much challenge; although, we have recently seen the rising of opposition parties or citizens, (ii) country governance-ruling elites combine free market policies with socialism, and privatisation of government owned entities is extremely rare and (iii) economic policies-ruling parties champion economic direction and by extension economic of countries [1]. Some market commentators called that approach statism. However, statism is beyond the scope of this study. Those three traits have strong influence of the capital markets of those countries. The key question is what relationship exists between investments and associated risks. For this article, the special focus is on volatility spills.
That concept is commonly known as volatility transfer hypothesis (VTH). VTH is well documented across and within different traditional asset classes (i.e. stocks, bonds and money market instruments especially cash). Fundamentally, VTH argue that as one become familiar with a firm, the volatility of that firm decreases due to decrease in information asymmetry. However some scholars argue that VTH does not hold in every situation. On the practical side, specifically in among alternative asset classes, there are virtually no studies on VTH. This is the main gap that this article fills in. In analysis, the study draws data on bonds, commodities, equities and listed real estate from the BRICS countries. The analysis is essentially empirical. Both empirical and theoretical studies offer little, if any, insight on how volatility spillovers behave and their effects in the BRICS countries. The closest study that explores this theme is [2]. In [2], multivariate general autoregressive conditional heteroscedasticity (GARCH) and disaggregated value-at-Risk (VaR) are used to study traditional asset classes. This study goes beyond traditional asset classes and uses other models such as the regime-switching models. Similarly to [2], international diversification and risk management is central to volatility spillovers in BRICS countries.
A lot of policy documents show that jointly BRICS account for over billions of dollars investments including listed investments-in 2012 BRICS received over $1 billion in foreign aid. The population is highly consumptuous with a high percentage of population eligible to work for foreseeable future. In all those countries, ruling governments encourage their working force to save some of their earnings for later use in their life. Among the type of investments that potential future retiree can invest in include bonds, commodities, equities and listed real estate investments. Besides the type of investments that potential future retiree investments in, BRICS have their own special economic traits. South Africa offers one of the highly sophisticated capital markets in the world and China is the second biggest economy after the Unites States (U.S.). More, China has been moving at least 30 million people out of the poverty over the last 20 years. Given those massive investment opportunities in BRICS countries, how do investors maximise their returns and minimise their risks? One of the ways of minimising risks in the BRICS is by mitigating against volatility investment movements in the BRICS countries.
The consensus emerging from literature on asset co-movements is that asset markets are linked internationally, and volatility is transmitted from one market to another. Earlier studies of market linkages were habitually focused on developed countries however due to the financial liberalisation and trade openness of emerging economies, research has also focused on investigating cross-border links in emerging economies from developed countries. Emerging markets have increasingly played an important role in financial markets and were not spared from the impact of the global financial crisis. A better understanding of how emerging markets respond to exogenous shocks can assist investors and portfolio managers better understand if there are any diversification possibilities.
This article explores volatility spillovers in the BRICS countries based on alternative investment strategies. That is, alternative investment strategies involve investment in bonds, commodities, equities and real estate. For this study, seems real estate is listed because on one hand, the relationship between listed real estate and unlisted real estate is a mixed bag [3] and other the other hand, real estate is seen as a proxy for macroeconomic risks [4]. The macroeconomic risk proxy is also evident in other industries such as commodities. Moreover, diversification plays part in influencing commodity prices. Listed real estate is either real estate investment trusts (REITs) or real estate operating companies (REOCs). Further, those studies illustrate that those effects are trans-Atlantic. The reason why cash is not analysed in this article is because cash and money asset classes have been extensively researched. For example, over 60% of international trade is done on U.S. dollars and currency markets are the most liquid of all capital markets [5]. For this study, it is important to drive risk management strategies, especially when information is asymmetric.
The article similar to this study is one by Liow [6]. That study analysed spillovers of four major asset classes (public real estate, general equity, currency and bond) during 2007–2009 period. Given the longer period for this study, one foresees more interesting results than ones of Liow [6]. He used regime-switching, VAR and GARCH (1;1) models. This uses models used in [6] plus the regime-switching model. Liow [6] draws data from four continents; (i) Asia emerging countries, (ii) European emerging markets, (iii) Latin American emerging countries and (iv) South Africa. Other than being emerging countries, the BRICS are similar in the sense that ruling political elites stay in power for long periods (i.e. 15 years), more, those governments have come up with organisations that are most likely to compete with established institutions, i.e. the BRICS Bank is most likely to compete with the World Bank in future. Further, there is close political will among the BRICS which is not prevalent among all emerging countries. As volatility spills are driven by financial integration, liberalisation and crises contagion [6] among other factors, the former factors are likely to be key drivers for volatility spills among the BRICS countries. So far, it seems there are no major crises contagion reported in any of the BRICS countries.
To sum up, the results show that the indices (bonds, commodities, equities and real estate) illustrate that volatility spills are within and in between emerging countries. The volatility movements between countries are sporadic without any specific pattern(s)-most volatility spills are within countries. Those spills are evident in both out and in-sample data. Thus, lagged data of indices have evident volatility spillovers. Consistent with prior studies, the volatility spills move between different volatility regimes. Interestingly, liquid indices have less persistent regimes than illiquid indices. That would imply that illiquid indices are suitable for investments by intraday investors such as hedge funds while liquid indices are suitable for long-term investments-a rare finding. In [7], Markov-Switching-GARCH model is used, while this study uses general Markov regime switching model. The former model is univariate and discrete in nature while the latter is ‘multivariate’ and continuous in nature. Hedging was effectively reduced by 64% in [8] while in this study volatility risk is appropriately modelled.
The balance of this article is structured as follows: Section 2 is on literature review. Section 3 is on data and modelling, and Section 4 presents the analysis. Section 5 concludes the study.
In criticising the prior studies this article divides literature review as per the four asset classes; (iii) bonds, (i) commodities, (ii) equities, and (iv) listed real estate. In this way, specific traits of each asset class are disentangled.
In [9], it is explored volatility spills and return between equity and bond markets for Australia during the period of 1992–2006. They argue that volatility spills are important for diverse purposes; (i) asset allocation, (ii) portfolio management, (iii) financial risk management, and (iv) capital market regulation. In this article, volatility spills are important largely for financial risk management. Among confirmed concepts on volatility spills (i) hedging demands increase with prices changes, (ii) positive news increases stock prices while prices fall when the discount rate rises. Normally, asymmetric price adjustment hypothesis (APAH) state that bad news affect bonds and stocks equally than good news. For modelling, they used joint process of conditional means, asymmetric Baba, Engle, Kraft and Kroner (BEKK) model, dynamic conditional correlation (DCC) model and bivariate GARCH model.
The data sample is on Australian equity and government bond markets, and the equity index was on 500 companies listed on Australian Stock Exchange. The preliminary results of [9] illustrate that equity volatility is lowest when returns of both markets are positive, and highest equity (bond) returns are negative (positive). More, when equity returns are negative, conditional correlation is stronger. As expected, distribution of returns are skewed and leptokurtic. Bond (equity) markets seem to react predominantly to negative (positive) news than positive (negative) ones. When the bond shock moves from negative angle to positive side, then equity variance surface tilts. Most volatility spills for equities are evident when returns are negative and visa verse for bonds. None of the used models were fully able to explain observed spills.
In [10], co-movements of volatilities in the international equity and bond markets were explored. They argue that genitive returns are more common and dependent than positive returns in international equity markets. In investigating volatility spills [10], the issue of fat tails was taken into account. The data presents the dependence between two leading markets in North-America (U.S. and Canada) and two major markets of the Euro zone (France and Germany). The U.S. equity index is based on the S&P500 index and Canadian equity index returns are based on DataStream index. The bond series are from 5-year government bond indices. The statistical tools used are exceedance correlation, extreme value theory (EVT) in order to capture fat tails and Gaussian bivariate GARCH or regime-switching models, specifically M-GARCH because of its ability to capture many variables. Copulas are used to increase the ability to capture asymmetric dependence.
The preliminary results of [10] show that there is a large, extreme dependence in international equity and bond markets while bond-equity dependence has a negative effect. The latter statement encourages international diversification and switching form equities into the domestic bonds. Historically, correlation between Canadian equity and bond markets has been relatively high. Further, results show that asymmetric regime of dependence and negative shocks are more likely to be transmitted to other markets than positive shocks. After the introduction of the Euro, France and Germany became more dependent. Broadly, high volatilities are associated with asymmetric dependence.
Ehrmann et al. [11] disentangled complexity of financial transmission process across different assets-domestically and internationally. They focus spillovers on two largest economies in the world-the U.S. and Euro area. The period covered is from 1998 to 2008 for two-daily returns over a 20-year period for seven asset prices: short-term interest rates, bond yields and equity market returns. For the U.S., data includes the 3-month Treasury bill rate for the short rate, the 10-year Treasury bond rate for the long rate and the S&P500 index for the stocks. For the Euro area, data is 3-month interbank rate-the FIBOR rate before 1999, the EURIBOR after 1999-for short rate, the German 10-year government bond for the long rate, and the S&P Euro index for the equity market and the U.S. dollar-euro since 1999. Every data is expressed as a percentage.
To model those spills [11], it was used a behavioural model that incorporated seven variables which had a 7
In [12], volatility spills were investigated in commodity markets since 1700. They argue that some authors raised questions regarding the volatility of commodity prices been more than manufacturing ones, the secular trend since 1700 and relationship between globalisation and commodity volatilities. However, none of the scholars have addressed those questions using a long term series indeed. For poor countries [12], it was argue that volatilities for those countries should be high because those countries specialise in agriculture and mineral production. The data used in [12] is for the world and various trends are outlined during specific periods. This is to consolidate reasons that drove commodity prices during those periods. They calculated log prices for their study, and used Dickey-Fuller and Phillips-Perron tests to validate their illustrate volatilities. Prebisch-Singer hypothesis was central to their analysis. Preliminary results of [12] show that volatilities among different commodities are different. In poor countries, volatilities tend to be higher because those countries are dependent on agriculture and mineral production. Sauerbeck-Statist shows no evidence of secular patterns from 1800 onwards. Further analysis illustrates that French and American Revolutionary Wars, the Napoleonic Wars and the War of 1812 contributed to increase in volatilities. In order to test the robustness of their results [12], GARCH (1;1) model and GARCH (1;1) was used and it was confirmed that results are robust. Seasonality also played a role in driving higher volatilities.
Antonakakis and Kizys [13] investigated dynamic spills between commodity and currency markets. In [13], it is argued that precious metals (gold, silver, platinum and palladium) have been seen as safe havens during final crisis. Further, they state that inclusion of precious metals in equity portfolios decreases systematic risk of investments; therefore, diversification accrues in those investments. They research is centred on these questions; (i) how time-varying spills differ among commodity and currency markets, and (ii) what is the relationship between returns and volatilities during financial transmission. In answering those questions, Antonakakis and Kizys [13] used the spillover index which is performed by using rolling-window forecast error variance decomposition (FEVD) by transmitters and receivers of shocks.
The weekly data in [13] is made up of the spot prices of the four precious metals, crude oil spot prices, euro (EUR/USD), Japanese yen (JPY/USD), British pound (GBP/USD) and the Swiss franc (CHF/USD) spot exchange rate, each versus U.S. dollar. They use weekly daily in order to synchronise data and error elimination [13]. The period of the data is from January the 6th, 1987 to July the 22nd, 2014, totalling 1438 observations. The usage of the four precious metals is well documented by numerous studies. The preliminary analysis of data illustrate that volatilities increased dramatically especially from 2000/2001 period for the precious metals and oil, while currencies volatility decreased from 2000/2001 onwards. Moreover, preliminary analysis shows that spot prices are positively skewed with exception of GBP/USD and CHF/USD. The absolute returns (volatility) for all parameters are positively skewed. And the Jarque-Berra tests confirm non-normality of distributions. Further analysis includes using vector autoregressive (VAR) model to illustrate return transmission across all the parameters. One of the advantages of VAR model is that it can cater for many variables.
The results of the VAR model illustrate volatility spills across all variables. Total spillovers index indicates 42.41% average contribution. Most transmission was from gold, followed by silver and then platinum. Crude oil had lowest transmissions. On the other hand, crude oil’s demand is linked to four commodities as for production of those metals, crude oil is used. One of notable thing about [13] is that negative skewness has higher probabilities. Normally, the opposite should be true because positive skewness constituent more risk than a negative one. For all variables, the curves are positively skewed and leptokurtic. The latter statement would imply that prices spreads are significantly probably due to high volatilities. According [13], volatilities in commodity and currency markets are likely to occur during less volatile episodes. For robustness test, they used h-step-ahead forecast error variance decompositions and alternative rolling windows, and robustness tests confirmed that results main qualitatively similar.
Basak and Pavlova [14] modelled financialization for commodities markets. Prior studies have documented index and non-index commodities; however, the theory of financialization which is far-reaching implications had limited synthetisation [14]. The latter point is central to study of [14]. The main variables that were analysed in the study are (i) commodity supply shocks, (ii) commodity demand shocks, and (iii) (endogenous) changes in wealth shares of the two investor classes. The theoretical model that they built is a closed form. Fundamentally, in [14], it was argued that value assets pay off more in high-index states. In building the model, they assumed that the model follow Brownian motion (BM). The model included a parameter that signal arrival of news, supply news of uncorrelated commodities, model distinguish between index and non-index commodities, and the inventors were accounted for; (i) normal investors and (ii) institutional investors. Moreover, equilibrium effects of financialization of commodities were accounted for. Centrally to the last statement, instead of the model behaving like a trading model, it behaved like one for normal investor. Other equilibrium issues included (i) equilibrium commodity futures prices shaped on corollary, (ii) futures volatilities and correlations, and (iii) economy with demand shocks. Further, the illustrated commodity prices and inventories. For the commodity prices and inventories, they (i) incorporating storage where additional economic agents (i.e. consumers and firms) were added, (ii) equilibrium commodity prices and inventories. The second proposition is on how the discount factor is affected by institutional inventors. And finally, (iii) cross-commodity spillovers and the import of income shocks. The latter proposition is about how institutional demand increases for all assets are positively correlated with index, especially demand for commodity storage.
The results for [14] illustrated those volatilities in futures markets do spillover into other commodities. Further, there is a trade-off between investors due to relative performance fluctuates. The latter phenomenon is consistent with what is illustrated by VIX volatility index [14]. In addition, the model information is ‘asymmetric and investors have the same beliefs’.
In [15], excess co-movements of commodity prices in developed (118 variables from Australia, Canada, France, Germany, Japan, the UK and the U.S.) and emerging markets (six variables from China, Brazil, Brazil, Taiwan, Mexico, etc.) were investigated. They argue that prior studies illustrate that financialization in the commodities markets lead to excess price volatility. One possible reason for that is that commodities especially of currency nature such as gold are characterised by spikes in prices. Central to their investigation is that (i) co-movements imply that ‘demands and supplies are affected by unobserved forecast of the economic variable’ and (ii) portfolio management strategies are affected by co-movements. The latter phenomenon resonates with this study. The variables that [15] are (i) the U.S. index of industrial production, (ii) consumer price index (CPI), (iii) effective $US exchange rate, (iv) three-month Treasury bill interest rate, (v) M1 monetary measure and (vi) S&P500 stock index.
One thing which is evident in [15] is that they are dealing with a large database which has numerous variables. And in order to probably account for those variables, you need a model that accounted for such variables. For the commodity prices, they used wheat, copper, silver, soybeans, raw sugar, cotton, crude oil and live cattle. Further, arbitrariness and computational difficulties should be minimised. One of the ways of how to avoid arbitrariness and computational difficulties is to use principal component analysis (PCA) and stepwise regression, although stepwise is time consuming when one uses many variables. In their analysis [15] focused on filtering commodity returns using large approximate factors models. And for that [15] used (i) static factor model and (ii) ARCH-LM for illustrating spillovers and (iii) SUR model to test whether residuals are unrelated.
The preliminary analysis of [15] the skewness of all commodities except of wheat is negatively skewed. Thus, wheat should have high volatilities than the rest of the commodities. And the Jarque-Berra test confirms non-normality for all commodities. The latter illustration is consistent with other studies on commodities. The correlation matrix shows that all commodities are correlated with one another except with live cattle. That is, live cattle in when compared with the seven commodities might offer diversification benefits. The results of returns show that crude oil and copper are costly correlated with variables of emerging markets. Monetary measures have more influence in emerging markets than developed countries. When they test for excess co-movement of commodity returns, results exemplify that commodity co-movements are common and influencing across all markets. Moreover, those co-movements are sampling dependent. In [15], it stated that given that the speculation is rife in commodity markets, some co-movements might be driven by speculation. The OLS model confirms the presence of endogeneity.
The Black Monday of October 1987, the U.S. born global financial crisis of 2008 and 2009, as well as the European debt crisis that occurred in late 2009 are known as the some of the few financial crisis in the past three decades that have resulted in the volatility of financial markets and further resulted in wide spread international crisis. These are known as co-movements of financial markets defined as volatility spillovers from one market to another. Volatility spillover studies have come to the vanguard as they are largely associated with risks that have implications on (i) optimal portfolio construction, (ii) financial stability and (iii) implementation of policies that may render harmful shock transmissions in financial markets. Recent studies that address the issue of volatility dynamics indicate that volatility spillover effects among countries or financial markets are time varying, most importantly during times of crisis. This has particularly significant consequences for investors and policy makers. Consequently, understanding the changing aspects of volatility spillovers is imperative.
In [16], both implied and realised volatility linkages were analysed through a rolling correlation analysis across global equity markets. This covers the U.S., European, German, Japanese, and Swiss markets during the sample period of 1999 to 2009. Implied volatility indices provide information regarding future uncertain expectations of stock price movements. Using the VAR method, the study indicates that both unconditional and conditional correlations for implied and realised volatility exhibit large fluctuations during that sample period. These results coincide with market fluctuations that occurred during the period of the global financial crises.
The consensus emerging from literature on asset co-movements is that asset markets are linked internationally, and volatility is transmitted from one market to another. Earlier studies of market linkages were habitually focused on developed countries however due to the financial liberalisation and trade openness of emerging economies, research has also focused on investigating cross-border links in emerging economies from developed countries. Emerging markets have increasingly played an important role in financial markets and were not spared from the impact of the global financial crisis. A better understanding of how emerging markets respond to exogenous shocks can assist investors and portfolio managers better understand if there are any diversification possibilities.
On another standpoint [2], volatility spillover effects were identified on a sectorial basis (industrial and financial sectors) from the U.S. as a developed country to BRICS nations as emerging markets using a VAR(1)-GARCH (1,1) framework. In the industrial sector, overall results indicate that the volatility transmission from the U.S. predominantly affects Brazil, Russia and India, while in the financial sector; it predominantly affects Brazil and Russia. In [17], the volatility impact is also indicated from developed markets by looking at regional spillovers across transitioning emerging markets and frontier equity markets, particularly in the Middle East and Africa together with the U.S. as the developed market. The study examines the stock markets of Saudi Arabia, UAE, South Africa and Israel from the period of 1994 to 2010 using a multi-timescale analysis using a wavelet-based time and frequency distributions compositions. The study finds that the Middle Eastern countries were more susceptible to the U.S. subprime crisis as compared to South Africa, however indication of short-term shocks that produced additional vulnerability in the South African equity market prior to the global financial crisis are noted, which could have potentially been due to investor sentiment.
Despite the increased studies of volatility spillover analyses from developed to emerging markets, there continues to be limited cross-market studies that are undertaken in equity markets of emerging nations. The possible integration of emerging markets continues to be of great concern as theory suggests that expected returns might be expected to reduce, following a greater integration of emerging markets in the world economy. Ref. [8] contributes to the empirical literature of volatility spillover dynamics between equity markets by examining the returns and volatility dynamics of Ghana, Kenya, Nigeria and South Africa for the period 2005–2010. The study employs a multivariate VAR-EGARCH framework and finds that Nigeria is the dominant in volatility transmission to Ghana, Kenya and South Africa and while it is not a receiver of volatility from these markets. The study however finds that the domestic volatility indices of these markets are the highest coefficients for all these markets, which implies that domestic shocks may impact these markets more than external shocks.
In [2], it was positioned that a more effective way of better understanding efficient asset pricing, volatility forecasting, efficient cross-market allocation and hedging decisions along with optimal international portfolio strategies is through understanding the stock market dynamics and volatility spillover effects of listed asset sectors individually in particular markets. Several literatures have focused on volatility spillovers in financial markets on a global, regional and country level. This section particularly focuses on volatility spillovers among equity stocks in financial markets. Cross-market volatility linkages in global developed equity markets attracted much attention in research. An earlier study of [18] studied the return volatility dynamics and transmission among the G-7 countries’ equity markets using both the GARCH and VAR models. They find that while in these markets, domestic market shocks are the largest single source of domestic volatility variation for other markets, (apart from the U.K. and U.S.) shocks to foreign markets account for a significant portion of domestic market volatility. The study provides empirical evidence of volatility spillover effects in the equity markets of these industrialised countries. The results also indicate that volatility spillovers in these equity markets for this period had significant changes due to the global financial crises.
Studies such as [19] find that during tranquil times there are particular countries that are net transmitters of risk and others are net receivers of risk in global financial markets. The study particularly analyses the global financial shifts of volatility spillovers by employing the [20] forecast-error variance decomposition and incorporating a Markov switching framework which considers economic regime changes, into the generalised vector autoregressive (VAR) model. The study uses the following daily stock market volatility indices as proxies of market risk; the VIX (S&P 500 volatility, U.S.), VFTSE (FTSE 100 volatility, U.K). VCAC (CAC 40 volatility, France), VDAX (DAX 30 volatility, Germany). VAEX (AEX 25 volatility Netherlands), VSMI (SMI 20 volatility, Switzerland), VHSI (HIS 50 volatility, Hong Kong) and JNIV (Nikkei 225 volatility, Japan) for the period 2001 to 2017. The results of the study support the theory of shock transmissions and volatility spillovers by finding that all markets are more intense and are at the frequent risk of shock transmission and reception during turbulent times.
The co-movement of real estate stocks and financial markets has been studied extensively. Previous literature has documented the theory that low correlation of an asset with other capital markets, international and domestic portfolios provides the opportunity for risk reduction and diversification in an investment [21]. In [22], the local, regional and global linkage of securitized real estate and stock markets and possible integration in nine developed markets from the three regions of North America (the U.S.), Europe (Germany, France, Netherlands and the U.K.) and Asia-Pacific (Japan, Hong Kong, Singapore and Australia) in the period 1990–2011 were investigated.
The study employs the spillover index of [20] that produces variance decompositions that are insensitive to variable ordering by allowing correlated shocks and historically observed distribution of the errors to account for the shocks. The spillover index is further based on a multivariate VAR that can capture market fluctuation of more than two countries concurrently rather than bivariate models. Liow [22] finds evidence of the following: (i) time-varying return co-movement and volatility spillovers in all markets and positive association with the global financial crisis (ii) a bi-directional and regime-dependant relationship of cross-volatility spillover effects, (iii) synchronisation between co-movements of volatility spillovers and correlation spillover cycles. Liow [23] studied time-varying co-movements of Asian real estate and the linkages of local, regional and global stock markets over the period of 1995 to 2009. Correlations of assets are interpreted to indicate co-movement and integration across financial markets. The integration of markets is also interpreted to indicate interdependence of markets which can lead to transmission crises.
Liow [23] demonstrates through an Asymmetric Dynamic Conditional Correlation (ADCC) model, also a specific class of multivariate GARCH models. Liow [23] finds time-varying conditional real estate-stock correlations at local, regional and global stock markets and some asymmetry and furthermore real estate-global stock correlation is impacted significantly by volatilities at local, regional and global levels. In this period, Liow [23] also finds that real estate and stock volatilities are more substantial in influencing co-variances more than correlations during and post the global financial crises. Hoesli and Reka [24] provided evidence on a national and international basis by investigating volatility spillovers between the U.S. and the U.K. real estate market, The U.S. and Australian real estate market as a national analysis and the U.S equity and real estate markets as an international analysis. The period of the study extends from 1990 to 2010 and the volatility spillovers are studied using the covariance matrix of the asymmetric t-BEKK (Baba-Engle-Kraft-Kroner) specification. On a national basis, the U.S is the net transmitter of volatility spillovers; this can be expected as the subprime crisis originated in the U.S. On an international basis, the three markets have more influence of volatility of the global market than the reverse, indicating quite the importance of these developed markets.
Liow and Ye [25] employed both univariate and multivariate switching regime beta models in the period of 2000–2015 to illustrate regime-dependant excess return distribution and volatility spillovers pre and post the global financial crises. The study examines the developed markets of the U.S., the U.K., France, Germany, Australia, Japan, Hong Kong and Singapore and their linkages with the world stock market and world real estate markets. The study uses switching regime models to allow for different economic conditions as well to capture the changes in the stochastic volatility process driving the real estate markets. The study reports a higher volatility parameter in response to the global financial crises compared to the ‘normal’ period. The real estate market linkages with the world market were affected differently by the global financial crises however they are amplified post-crises particularly for the European region, while the Asian real estate markets displayed reduced volatility spillovers with world markets in low volatility state post-crises.
Regime changes are associated with significant shifts in the fundamental relation between the risks and return trade-off and the probability that a switch can be initiated from one regime to another [26]. In [26], it was incorporated multiple regimes changes by modelling the return-volatility transmissions of real estate through the multivariate regime-dependent asymmetric dynamic covariance (MRDADC) model. They study the real estate markets of the U.S., the U.K, Japan, Hong Kong and Singapore for the period of 1990 to 2009. Firstly, the study finds that asymmetry, variance and covariance, associated with multiple regime changes, jointly influence return-volatility transmission in real estate markets and secondly the study finds that the five markets generally interact well with one another by finding significant mean-volatility linkages under different volatility regimes. Consequently, this has implications on diversification benefits that these markets can offer.
The weekly data is for the five BRICS countries (general equities, real estate, commodities and bonds) for the period 1 January 2007–31 December 2017 obtained from Bloomberg. The out-sample is from 2007 to 2017 and in-sample from 2012 to 2017. The in-sample is for parameters estimation and out-sample for evaluating forecasting performance. The use of weekly data ameliorates concerns over non-synchronicities and bid-ask effects in daily data [13]. The phenomenon of using returns to illustrate the descriptive nature of volatility spillovers is synonymous with [6, 27]. The returns are logarithm returns and they are consistent with VAR model. All returns are calculated based on the indices of those countries. The indices are as follows; (i) general equities, Brazil IBRX 50 for Brazil, Moex Russian index for Russia, Nifty 50 for India, SSE50 for China and JSE top 40 index for South Africa, (ii) listed real estate, IMOB for Brazil, for Russia the index is created based on PIKK Group, PJSC LSR Group, World Trade Centre ‘ordinary shares’ and World Trade Centre ‘preferred shares’ because Russia does not have a listed real estate index-the market capitalisations of those firms where aggregated over time, Nifty Realty for India, SHROP for China and all Property index (J803) for South Africa, (iii) commodities, BM&F BOVESPA for Brazil, MICEX Oil and Gas Index-from the Moscow exchange for Russia, Nifty Commodities for India, CCI for China and JCGMSAG (gold mining index) for South Africa and (iv) bonds, for Brazil-Brazil 8 7/8 04/15/24 bond, Russia-RFLB 08/29/18 bond, India-Nifty 10 yr. benchmark, China-GT USDCN 15yr bond and South Africa-SAGB 10 ½ 12/26 bond. Skintzi and Refenes [28] used indices to investigate regional and country shocks. This article is the first one that uses indices to illustrate shocks in the BRICS countries. According to [28], one of the advantages of modelling volatility shocks using indices is that shocks are captured both as endogenous and exogenous variables. Just like [6, 27], this article presents diagnostic analysis based on graphs as part of volatilities transmission investigation.
For every index per a row, the first country is Brazil, followed by Russia, then India; thereafter, China and finally South Africa. A close inspection of Figure 1 illustrate that the log returns of BRICS countries as shown by different graphs, BRICS returns were characterised by spikes during 2007–2017 period. The latter statement might be interpreted as the presence of changing volatility patterns and probably spillovers. Similar arguments were put forward by [6, 27] on return patterns. The years are on the x-axis and the log-returns on the y-axis. During 2008/2009, there was a global financial crisis that mainly affected western countries-western Europe and U.S. were the hardest hit by that subprime crisis. According to the Bank of International Settlements (BIS) Brazil only reacted to the global subprime crisis after Lehman Brothers collapsed. Due to that reaction, there was panic in Brazil lead to property market falling but IBOVESPA rose by 20%-in local currency; local capital issuance stood around $165 billion around 5.6% of Brazilian GDP. And bank credit increased to 36% from 32% during that period. Although, there still spikes after 2009, but they hoovered around same levels until 2017. For Russia, one sees similar pattern to Brazil. For both countries-Brazil and Russia, during subprime crisis, real estate reacts more than other indices. Does that imply that during subprime crisis volatilities are much higher in real estate?
BRICS log returns.
Volatility modelling will provide answer(s) to that. Similar patterns are observable about Indian and Chinese indices. However, India and China have very strong capital markets and those countries are self-reliant on financing countries infrastructure. It seems that India and China tend to be insulated from external capital shocks [29]. South Africa is a unique member of the BRICS which joined through invitation. During year 2008, South African indices reacted to global capital markets movement; however, there was no subprime crisis effects felt in South Africa [30]. A study by PWC South Africa in 2016 illustrate that there was (i) a decline in new equity capital raised in South Africa, (ii) active and growing bond market in South Africa and (iii) number of corporate transactions decreased in South Africa. The decline in commodities index during 2014–2016 can be attributed to decline in commodities price and demand in commodities by South Africa trading partners. All those graphs illustrated diagnostic analysis on volatility spills. Now, the article takes the analysis further and it explores formative assessment of global transmission in the BRICS countries. The next section presents descriptive statistics of indices of the BRICS countries.
Table 1 provides the descriptive statistics of the returns of general equities, real estate, commodities and bonds.
Descriptions | Mean | Minimum | Maximum | SD | Kurtosis | Skewness | JB |
---|---|---|---|---|---|---|---|
Panel 1: general equity | |||||||
Brazil | 0.0007 | −0.3547 | 0.2385 | 0.051 | 7.0571 | −0.6941 | 1235.05 |
Russia | 0.0009 | −0.4031 | 0.2841 | 0.052 | 9.1638 | −0.0484 | 1987.64 |
India | −0.001 | −0.1906 | 0.1956 | 0.037 | 3.2816 | 0.2699 | 264.06 |
China | −0.001 | −0.1704 | 0.168 | 0.04 | 2.1323 | 0.0068 | 106.28 |
South Africa | −6E − 04 | −0.2606 | 0.1984 | 0.043 | 5.1509 | −0.0227 | 633.49 |
Panel 2: real estate | |||||||
Brazil | −0.002 | −0.5044 | 0.3097 | 0.066 | 8.8189 | −1.0306 | 1780.53 |
Russia | −0.001 | −0.7145 | 0.5282 | 0.077 | 22.483 | −1.3087 | 11613.2 |
India | 0.003 | −0.3752 | 0.3719 | 0.072 | 4.0292 | 0.2754 | 384.67 |
China | −0.002 | −0.2161 | 0.2894 | 0.054 | 2.6673 | 0.3788 | 179.68 |
South Africa | −0.001 | −0.1961 | 0.1781 | 0.037 | 4.2404 | 0.4225 | 428.42 |
Panel 3: commodities | |||||||
Brazil | 0.0002 | −0.529 | 0.5435 | 0.177 | 2.0477 | 0.0046 | 100.11 |
Russia | −8E − 04 | −1.6035 | 1.6337 | 0.199 | 22.8521 | −0.2209 | 12.385 |
India | 0.0009 | −0.2369 | 0.2432 | 0.042 | 3.8767 | −0.0754 | 359.35 |
China | 0.0005 | −0.1096 | 0.0768 | 0.02 | 4.0293 | −0.7607 | 442.87 |
South Africa | −0.002 | −0.2866 | 0.286 | 0.065 | 2.0176 | 0.3055 | 106.1 |
Panel 4: bonds | |||||||
Brazil | 0.0001 | −0.0845 | 0.1474 | 0.016 | 21.5203 | 1.6562 | 7763.32 |
Russia | −2E − 04 | −0.2019 | 0.1777 | 0.029 | 20.4736 | −0.7622 | 9466 |
India | −3E − 04 | −0.5151 | 0.5113 | 0.065 | 26.9833 | −1.1651 | 17455.1 |
China | −3E − 04 | −0.1126 | 0.0661 | 0.015 | 7.2105 | −0.5118 | 1266.32 |
South Africa | −1E − 04 | −0.2019 | 0.1777 | 0.029 | 20.4549 | −0.7619 | 9431.2 |
Descriptive statistics.
Note: SD stands for standard deviation and JB for Jarque-Bera test for the return normality.
Panel 1 indicates the equity information across all countries. Russia leads with the highest return at 28.41% while China has the lowest maximum return of 16.80%. Over the full period, Russian equities are also the most volatile with a standard deviation of 5.20% and the lowest volatile equities being that of China at 3.72%. The distribution of returns over time is negatively skewed with the exception of India and China. In addition, for all countries, the excess kurtosis exceeds 3, indicating that the return series is leptokurtic which is inconsistent with a normal distribution. The real estate data in panel 2 for the five countries indicate Russia with the highest return of 52.82% while the South Africa closed off with a lowest maximum return of 17.81% return. Russia is the most volatile with a weekly standard deviation of 7.65% while South Africa reports the lowest standard deviation of 3.68%. All five countries exceed the kurtosis of 3 and with the exception of Brazil and Russia, the data is positively skewed.
For commodities indicated in panel 3, Russia reports the highest maximum of 163.37% in returns, while India reports the lowest at 7.68%. Russia commodity stocks are more volatile with a standard deviation of 19.87% and the Chinese stocks are the least volatile at the standard deviation of 2.02% All countries exceed the kurtosis of 3 and the data is negatively skewed with the exception of Brazil and South Africa. In the bonds market indicated in panel 4, India has the highest return at 51.13% while China has the lowest maximum return at 6.61%. India is also the most volatile with a standard deviation of 6.50% and China. The data is also leptokurtic and is negatively skewed with the exception of Brazil. JB values in all panels (i.e. 1–4) illustrate that the four indices are abnormal and that can be interpreted as the presence of shocks. In [6], the same view on JB values was stated. The skewness values show that some countries have negative skews while others have positive skews for different capital markets. That mixture of different skewness assist in hedging volatility while positive skewness assist in generating high alpha and/or arbitrage opportunities. The former phenomenon is ideal for risk managers while the latter phenomenon is suitable for intraday investors-traders.
Volatility and volatility transmission can be illustrated using most econometric models including VAR model. The formula for VAR model is:
Where the
where
and
where
where
while
In presenting the empirical results, the article starts with VAR calculations. Thereafter, Markov regime-switching results are presented in order to explore if one can infer interdependence of volatilities regimes. In order to verify which VAR is suitable, the first and second order tests (i.e. residual serial correlation) testing validity are used. Thereafter, a lag-length criterion is used. All those tests confirmed the appropriateness of VAR (1) model. Further, in order to interpret the results Cholesky decomposition is used. Generally, when using Cholesky decomposition the order of VAR parameters order matters. The BRICS countries are inputted in alphabetical order because that order is consisted with normal writing order. Although, VAR results might be different when one inputs them in a different format, one views normal order as an appropriate one. It can be inferred from [31] alphabetical order modelling leads to better estimates. In Tables 2 and 3 all variables highlighted in grey are statistically significant for VAR values as they are at least 2 irrespective of being negative or positive. The F-statistic is basically Anova values and one reads the in the following manner. Assume the following inequality
Panel 5: bonds | |||||
---|---|---|---|---|---|
Brazil | China | India | Russia | South Africa | |
Brazil | 0.1833 (4.4095) | −0.0202 (−0.2429) | 0.2881 (1.5718) | −0.0201 (−0.24301) | 0.0332 (0.7153) |
China | 0.0077 (0.0038) | 1.7959 (−0.4477) | −0.8782 (−0.0993) | −1.4175 (−0.3533) | 0.2652 (0.1185) |
India | −0.0004 (−0.0440) | 0.0050 (0.2812) | −0.4140 (−10.5584) | 0.0049 (0.2801) | 0.0114 (1.1494) |
Russia | −0.0226 (−0.0113) | 1.4237 (0.3549) | 0.8546 (0.0966) | 1.0447 (0.2605) | −0.2573 (−0.1150) |
South Africa | 0.1055 (2.7839) | −0.0280 (−0.3701) | 0.0383 (0.2292) | −0.0280 (−0.3697) | −0.0003 (−1.9232) |
F-statistic | 5.2479 | 17.9005 | 23.0227 | 17.9331 | 1.1701 |
Akaike AIC | −5.8292 | −4.4438 | −2.8618 | −4.4433 | −5.6106 |
Schwarz SC | −5.7830 | −4.3973 | −2.8157 | −4.3907 | −5.5643 |
Panel 6: commodities | |||||
Brazil | China | India | Russia | South Africa | |
Brazil | −0.4092 (−10.5168) | −0.0125 (−2.6652) | −0.0063 (−0.6138) | 0.0339 (0.7887) | −0.0089 (−0.5621) |
China | 0.3609 (0.9718) | 0.2844 (6.3448) | 0.0436 (0.4442) | −0.5516 (−1.3461) | −0.2129 (−1.4044) |
India | 0.0028 (0.0162) | 0.0159 (0.7539) | 0.0670 (1.4443) | 0.1208 (0.6232) | −0.0092 (−0.1288) |
Russia | −0.0701 (−1.9894) | 0.0059 (1.3848) | 0.0048 (0.5099) | −0.2919 (−7.5058) | 0.0033 (0.2270) |
South Africa | −0.0943 (−0.8626) | −0.0078 (−0.5876) | 0.0627 (2.1706) | 0.1935 (1.6049) | −0.0114 (−0.2545) |
F-statistic | 22.7883 | 11.7035 | 2.3085 | 12.2434 | 0.7016 |
Akaike AIC | −0.8059 | −5.0351 | −3.4670 | −0.6091 | −2.5984 |
Schwarz SC | −0.7597 | −4.9888 | −3.4208 | −0.5629 | −2.5522 |
Panel 7: equities | |||||
---|---|---|---|---|---|
Brazil | China | India | Russia | South Africa | |
Brazil | −0.1469 (−2.0726) | −7.4597 (−1.3644) | 1.2495 (0.2433) | 2.5543 (0.3575) | 7.1695 (1.2132) |
China | 0.0000 (−0.0920) | 0.0178 (0.4235) | 0.1028 (2.5982) | −0.0552 (−1.0039) | −0.0616 (−1.3525) |
India | −0.0002 (−0.3129) | −0.0531 (−0.8988) | −0.0257 (−0.4625) | 0.1499 (1.9421) | 0.0724 (1.1336) |
Russia | −0.0008 (−2.0001) | −0.0213 (−0.6589) | −0.0082 (−0.2684) | −0.0094 (−0.2233) | 0.0520 (1.4883) |
South Africa | −0.0004 (−0.4955) | 0.0826 (1.2342) | 0.1263 (2.0106) | 0.0982 (1.1239) | −0.0959 (−1.3261) |
F-statistic | 1.9912 | 2.5000 | 2.7793 | 2.7791 | 2.7064 |
Akaike AIC | −12.2983 | −3.6080 | −3.7334 | −3.0728 | −3.4525 |
Schwarz SC | −12.2520 | −3.5618 | −3.6871 | −3.0266 | −3.4062 |
Panel 8: real estate | |||||
Brazil | China | India | Russia | South Africa | |
Brazil | −0.0197 (−0.3642) | −0.1362 (−2.9988) | −0.0720 (−1.2489) | 0.0276 (0.4412) | 0.0031 (0.1009) |
China | 0.0236 (0.4708) | −0.0399 (−0.9456) | 0.1344 (2.5099) | −0.0325 (−0.5584) | −0.0011 (−0.0391) |
India | −0.0259 (−0.5738) | −0.0286 (−0.7487) | 0.0162 (0.3345) | −0.1256 (−2.3889) | 0.0097 (0.3717) |
Russia | −0.0229 (−0.6359) | −0.0504 (−1.6582) | 0.0466 (1.2075) | 0.1679 (4.0062) | 0.0332 (1.6033) |
South Africa | 0.0107 (0.1145) | −0.0780 (−0.9927) | 0.1783 (1.7871) | 0.0010 (0.0093) | −0.0233 (−0.0449) |
F-statistic | 0.2134 | 2.6753 | 3.8118 | 5.8949 | 0.6335 |
Akaike AIC | −2.6706 | −3.0149 | −2.5382 | −2.3731 | −3.7809 |
Schwarz SC | −2.6244 | −2.9687 | −2.4919 | −2.3269 | −3.7347 |
VAR (1): out-sample period (2007–2017).
Note: in each cell, the first number is the coefficient and the number in brackets is the t-test. All variables highlighted in grey are statistically significant for VAR values as they are at least 2 irrespective of being negative or positive. The interpretation of results is based on Cholesky decomposition.
Panel 9: bonds | |||||
---|---|---|---|---|---|
Brazil | China | India | Russia | South Africa | |
Brazil | 0.1876 (3.8632) | −0.0120 (−0.1567) | 0.2688 (1.1956) | −0.0121 (−0.1578) | 0.0278 (0.6435) |
China | −0.0817 (−0.0304) | −2.0308 (0.4779) | −1.6449 (−0.1322) | −1.5335 (−0.3608) | −0.7866 (−0.3289) |
India | 0.0001 (0.0114) | 0.0056 (0.3399) | −0.4205 (−8.7141) | 0.0056 (0.3386) | 0.0099 (1.0653) |
Russia | 0.0553 (0.0259) | 1.6457 (0.3874) | 1.6068 (0.1292) | 1.1477 (0.2701) | 0.7689 (0.3216) |
South Africa | 0.1664 (3.0025) | 0.08445 (0.9643) | −0.1679 (−0.6544) | 0.0837 (0.9552) | −0.1354 (−2.7462) |
F-statistic | 4.5645 | 14.0761 | 15.7002 | 14.1098 | 2.0675 |
Akaike AIC | −5.5172 | −4.6011 | −2.4522 | −4.6006 | 5.7506 |
Schwarz SC | −5.4585 | −4.5423 | −2.3935 | −4.5419 | −5.6919 |
Panel 10: commodities | |||||
Brazil | China | India | Russia | South Africa | |
Brazil | −0.3855 (−7.4290) | −0.0166 (3.4736) | −0.0097 (−0.9339) | 0.0746 (1.2989) | (−1.0249) |
China | 0.8517 (1.3579) | 0.1761 (3.0423) | 0.0542 (0.4329) | −1.2171 (−1.7521) | (−1.6324) |
India | −0.3983 (−1.3512) | 0.0242 (0.8910) | 0.0728 (1.2363) | −0.1864 (−0.5711) | (1.2772) |
Russia | −0.1453 (−3.0541) | 0.0010 (0.2295) | 0.0015 (0.1561) | −0.3677 (−6.9804) | (1.1867) |
South Africa | 0.0280 (0.1981) | 0.0177 (1.3538) | 0.0216 (0.7648) | 0.1967 (1.2551) | (1.2906) |
F-statistic | 12.6282 | 5.3503 | 0.7579 | 11.8936 | 0.0651 |
Akaike AIC | −0.8229 | −5.5888 | −4.0445 | −0.6187 | −2.6074 |
Schwarz SC | −0.7506 | −5.5165 | −3.9722 | −0.5464 | −2.5350 |
Panel 11: equites | |||||
---|---|---|---|---|---|
Brazil | China | India | Russia | South Africa | |
Brazil | 0.0795 (1.0222) | −6.1630 (−0.9579) | −3.1423 (−0.6267) | −15.9395 (−2.1685) | −4.7373 (−0.8339) |
China | 0.0011 (1.6271) | 0.0537 (0.9391) | 0.0342 (0.7611) | −0.1100 (−1.6849) | −0.1395 (−2.7636) |
India | 0.0009 (0.8208) | −0.0566 (−0.6371) | 0.1208 (1.7324) | −0.0125 (−0.1229) | −0.0386 (−0.4921) |
Russia | −0.0002 (−0.2543) | −0.1442 (−2.3803) | 0.0363 (0.7626) | −0.1418 (−2.0488) | 0.0223 (0.4165) |
South Africa | 0.0009 (0.7747) | 0.0287 (0.3102) | −0.0790 (−1.0876) | 0.1526 (1.4447) | −0.0512 (−0.6272) |
F-statistic | 0.8648 | 1.5195 | 1.2508 | 3.1221 | 1.6589 |
Akaike AIC | −12.7912 | −3.9589 | −4.4415 | −3.6926 | −4.2078 |
Schwarz SC | −12.7188 | −3.8867 | −4.3692 | −3.6203 | −4.1355 |
Panel 12: real estate | |||||
Brazil | China | India | Russia | South Africa | |
Brazil | 0.0041 (0.0635) | 0.0284 (0.4825) | −0.0729 (−0.9954) | −0.1126 (−2.1609) | −0.0207 (−0.4699) |
China | 0.1213 (1.8983) | −0.0306 (−0.5308) | 0.1037 (1.4455) | 0.0393 (0.7707) | −0.0794 (−1.8378) |
India | −0.0482 (−0.8873) | −0.0046 (−0.0943) | 0.0426 (0.6990) | −0.0174 (−0.4013) | −0.0282 (−0.7678) |
Russia | −0.0309 (−0.4209) | −0.0771 (−1.1644) | −0.0512 (−0.6213) | −0.0362 (−0.6179) | −0.0266 (−0.5363) |
South Africa | −0.0128 (−0.1304) | 0.0293 (0.3308) | 0.1061 (0.9652) | −0.1355 (−1.7336) | −0.0415 (−0.6271) |
F-statistic | 1.0215 | 0.3552 | 0.0529 | 1.5103 | 0.8958 |
Akaike AIC | −3.2510 | −3.4551 | −3.0200 | −3.7027 | −4.0345 |
Schwarz SC | −3.1787 | −3.3828 | −2.9477 | −3.6305 | −3.9622 |
VAR(1;1): In-sample period (2012–2017).
Note: in each cell, the first number is the coefficient and the number in brackets is the t-test. All variables highlighted in grey are statistically significant for VAR values as they are at least 2 irrespective of being negative or positive. The interpretation of results is based on Cholesky decomposition.
The results panel 5 of Table 2 illustrates that one-lag in Brazilian indexed volatility of bonds cause one-lag in Brazilian indexed volatility of bonds by 0.18 units. The letter statement is sensible given that what happens in one market should have similar effect in the short run-regimes show that regimes time is just over 2 weeks. Similarly, a one-lag in Brazilian indexed volatility of bond cause one-lag in South African indexed volatility of bonds-this probably that of similarities between the two countries, i.e. ruling political parties stay in power much longer; historically, Brazil and South Africa have good trade relations and further, the BRICS formation is strengthening that relationship even more. The one-lag in Indian volatility of bonds cause one-lag in Indian volatility of bonds. The phenomenon is similar with the one of Brazil lags; however, Indian one is negative while Brazil one is positive. One possible explanation for India negative lag is that in India the government is highly involved in driving economic growth than in Brazil. All other indexed volatilities of bonds in other BRICS countries are statistically insignificant. However, those latter results should be read with caution as using Cholesky decomposition for curves for those countries to start at zero. Panel 6 of Table 2 illustrates results for commodities indexed volatilities.
The statistically significant results are for Brazil and Brazil-this is for the same reasons as in panel 5, Brazil and China-Brazil is the producer of commodities while China is a consumer. This implies that the one-lag in producer of commodities indexed volatilities causes one-lag in consumer indexed volatilities but not visa verse. More, the coefficient is negative because the effects spillover to the consumer from the producer. The results for China lags can be explained by same reasons as the Brazil lags. Similarly, the one-lag in Indian index volatility cause a one-lag in South African indexed commodities volatility-the same as the Brazil and China one lags. The Russian lags are the same as China lags. Note that China lag with itself is positive while Russia lag with itself is negative. The positive lag for China lag with itself is probably due the economic influence that China has on the major world issues. The influence of Russian on major economic issues is limited. Thus, it might imply that South Africa needs to establish itself globally before the South African government can play a major on South African economic issues.
Panel 7 shows that spillovers which are statistically significant are for Brazil lags with itself-this pattern has been explained before, Brazil lag with Russian lag-in both countries, commodities firms are the main constituents of equities indices. And the causal relationship is slightly negative. Thus, 1 unit lag in Brazilian indexed volatilities emanating from equities cause −0.0008 lag in Russian indexed volatility of the same index. The latter strategy is synonymous with hedging and speculation in equity markets. More, straddles work in a similar manner. Panel 8 shows the results of lags in real estate indices. The statistically significant lags are for Russia with itself-that pattern has been explained before, China and Brazil-Brazil is probably the most powerful economy in South American while China is the second biggest economy after the United States. China has been on major infrastructure projects including real estate and many academics and practitioners have questioned whether the bubble is in the horizon in China. The negative coefficient is probably due to ‘overbuilding’ in China. Indian lagged volatility cause a positive lag in China. The latter finding is probably due to ruling parties’ influences in managing their economies. Interestingly, one-lag in Russian volatility causes one-lag in India. Normally, collapse of currencies and commodities markets precede other capital markets products. Overall, one can see that volatility spillovers in the BRICS countries based on four indices during 2007–2017 period, exemplify opportunities to diversification opportunities-when indexed volatilities move in different directions and risk management opportunities-when indexed volatilities move the same direction.
The influence of Brazil lag to South Africa lag during period of 2012–2017 is the same as during the 2007–2017 period as illustrated in panel 9. The period of 2012–2017 was largely a bull market while 2007–2017 had some bearish years, i.e. 2008/2009 period. This implies that indexed volatilities of bonds during out-sample reflect similar patterns as in-sample period. The sample phenomenon can be advocated on the influence the one-lag of Indian volatility on the lag of India. The interesting result in panel 9 is the one-lag of South Africa with itself-during the in sample period the lag is influential. During 2012–2017, the South African long-and short-term yields were on an upward trajectory. This is probably why one-lag for South Africa in during the in-sample period had a casual effect. For commodities indices-panel 10, the rests are the same as in Table 2 except the one-lag of Brazilian volatility on one-lag of Russia. During 2012–2017, commodities prices were stable. In panel 11, one-lag of China has influence on one-lag of Russia and one-lag of South Africa had one-lag on China-all the lags are negative. This is probably to declining consumption on commodity products by China. The rest of results are in panel 11 are the same as in panel 7. For panel 12, only one-lag of has a negative influence one-lag of Brazil. In short-run volatilities tend to be spiky than in the long run. That is, the volatility spillovers might be temporary.
For every index type in Figure 2 in every row, the first country is Brazil followed by China and then India; thereafter Russia. The last country is always South Africa. For equities indices, all the five countries have main shocks in 2007–2008 period as illustrated by residuals. This is the period of the last subprime crisis. However, the actual date reveals a similar picture. The upward regimes in all countries were during 2007–2009 period. It can be inferred from [6, 27] that when indices move in the same direction, the volatilities should follow a similar pattern. But, those graphs do not tell one from and to which are the volatilities. The equities volatilities in Brazil and South Africa seem to hover around the same level during the entire out sample. In [30], it was illustrated the subprime effects of 2007–2009 in South Africa were minimal. From what was reported in media, Brazil never suffered much from the subprime effects of 2007–2009. Real estate indices show the similar patterns as equities indices except for China and India. Sometimes during subprime crises, equities movements preceded real estate movements. The real estate indices of China and India show similar and strong patterns. One of the reasons for that is the BRIC relation between those two countries precedes the establishment of the BRIC countries. More, they have large populations and their respective governments are at the heart of driving those economies.
Filtered regime probabilities-out sample: 2007–2017.
For the commodities indices, Brazil and South Africa have the most and similar volatile indices patterns. One of the reasons of that is that Brazil and South Africa are rich in mineral resources. On the other hand, China and India consume most of commodities products. Surprisingly, Russia had the most stable commodity index during 2007–2017 period. Unlike Brazil and South Africa, Russia is mainly rich in oil while the other two countries are rich in minerals. The bonds indices show similar patterns to real estate indices. Numerous studies illustrate that listed real estate exhibit traits of other capital markets, especially bonds. The patterns of bonds indices are dissimilar except for China and Russia. It can be inferred that bonds volatilities of those two countries follow in the same direction. The graphs show diagnostic patterns and in order to have more depth, this article illustrates Markov transitions as shown in Table 4. In most studies, transition probabilities and expected durations, are used to illustrate Markov transitions.
Panel 13: equities | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Brazil | China | India | Russia | South Africa | ||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.5703 | 0.4297 | 0.5009 | 0.4991 | 0.4943 | 0.5057 | 0.5058 | 0.4942 | 0.5661 | 0.4339 |
2 | 0.4993 | 0.5107 | 0.5126 | 0.4874 | 0.5034 | 0.4967 | 0.5216 | 0.4784 | 0.6033 | 0.3967 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
2.3274 | 2.0436 | 2.0034 | 1.9507 | 1.9775 | 1.9864 | 2.0233 | 1.9171 | 2.3047 | 1.6577 | |
Panel 14: real estate | ||||||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.4983 | 0.5017 | 0.0000 | 1.0000 | 0.9827 | 0.0173 | 0.4689 | 0.5311 | 0.3442 | 0.6558 |
2 | 0.4893 | 0.5107 | 0.0244 | 0.9756 | 0.8896 | 0.1004 | 0.0210 | 0.9789 | 0.0074 | 0.9926 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1.9932 | 2.0439 | 1.0000 | 40.9669 | 57.7020 | 1.1116 | 1.8827 | 47.5343 | 1.5248 | 134.6585 | |
Panel 15: commodities | ||||||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.5206 | 0.4795 | 0.9093 | 0.0907 | 0.4737 | 0.5263 | 0.4965 | 0.5035 | 0.0000 | 1.0000 |
2 | 0.5024 | 0.4976 | 0.0021 | 0.9979 | 0.4544 | 0.5456 | 0.4998 | 0.5002 | 0.0141 | 0.9859 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
2.0857 | 1.9903 | 11.0248 | 485.6439 | 1.8999 | 2.2007 | 1.9859 | 2.0007 | 1.0000 | 70.8325 | |
Panel 16: bonds | ||||||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.4959 | 0.5040 | 0.2862 | 0.7138 | 0.9919 | 0.0081 | 0.4817 | 0.5185 | 0.5067 | 0.4933 |
2 | 0.0018 | 0.9985 | 0.4598 | 0.5402 | 0.7747 | 0.2253 | 0.3799 | 0.6201 | 0.4853 | 0.5147 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1.9841 | 556.1991 | 1.4009 | 2.1748 | 123.8068 | 1.2908 | 1.9285 | 2.6323 | 2.0270 | 2.0605 |
Markov transition-out sample: 2007–2017.
Note: CTP and CED stand for constant transition probabilities and expected duration, respectively.
Panel 13 (14) illustrates Markov transitions for equities (real estate) while panel 15 (16) shows Markov transitions for commodities (bonds). For equities indices, for the four countries; Brazil, China, India and Russia, there is considerable transition dependence between the two regimes as the original regimes start from as low 0.50 and increase to as high as 0.57. The non-original regimes are as low as 0.50. Although the original regime for South Africa 0.56 (relative high) but the non-original regime seems less dependent on the original regime. The expected durations of all countries are approximately 2 weeks. The quickly changing patterns in equities would be excepted given that equities markets are quite volatile that other capital markets. For real estate indices, China and South Africa show an interesting pattern-the original regimes are very low but the non-original regimes are highly dependent of the original regimes. That rare scenario is hardly observable in most countries in the world. That could be possibly due to the influence of governments which translate into financial markets in those countries. For Brazil, India and Russia, the two regimes seem to be dependent on each other. The excepted durations for real estate indices show interesting results-the expected durations are shorter their equities counter-parts mostly for first regimes. That is high unexpected. One possible explanation is that real estate indices in those countries are quite thin and represent a few constituencies. For commodities indices, the original regimes and non-original regimes are dependent. South Africa is the only country that illustrate a unique regime-non original regime is not some much dependent on original regime. All the regimes with exception of China and South Africa last for a few weeks. The reason why China and South Africa have longer accepted durations is because China consumes most commodities in the world while South Africa is a country rich in minerals. The regimes of bonds indices of all countries seem to be dependent. One possible explanation for that is that bonds are the oldest market in the capital markets. More, bonds are used mostly in those countries to finance private and public infrastructure. The expected durations of Brazil and China are entirely longer. Probably those two countries use their bond markets frequently for their capital markets offerings.
For every index type in Figure 3 in every row, the first country is Brazil followed by China and then India; thereafter Russia. The last country is always South Africa. For equities indices, the later periods of China, Russia and South Africa show similar regimes patterns. Thus, there is a possibility that equities indexed volatilities of those move from and to with each other. For all the five countries, in year 2014, equities indexed volatilities show similar movements. Most of the 2014 year was characterised by bull markets most countries throughout the world. At that time, probably volatilities are spillover each other. The real estate indices for of all for countries with exception of Brazil exemplify the same pattern. One possible reason is that listed real estate mimics similar movements. So far, the diagnostic assessments illustrate that there is some relationship between indexed volatilities of equities (real estate). This might imply that volatilities of indices move together during bullish periods than bearish periods.
Filtered regime probabilities-in sample: 2012–2017.
The indexed commodities volatilities of Brazil, China, India and South Africa exemplify similar movements. Brazil and South Africa are some of the main producers of minerals while China and India are some of the main consumers of mineral products. The bonds volatilities show different all countries show different movements. The indexed volatilities for in-sample period seem to be spiky than ones of out-sample period. It can be inferred from [32] that volatilities flatten out in the long-run because of diversification benefits which are more prevalent in the long-run. Broadly, the graphs of in-sample regimes are similar to ones of out-sample. Just like in the out-sample analysis for indexed volatilities, Markov transitions are calculated in order to deepen the insights on how indexed volatilities during in-sample period behave.
Panel 17 of Table 5 illustrates that non-original regimes are dependent for indexed volatilities; however, the original regimes are not necessarily trend setters. One of the reasons that might explain that pattern is that during 2012–2017 period most equities market experience bull phase. The expected durations for all equities volatilities are fairly short with exception of the Russian market. Panel 18 illustrates the same pattern as panel 17 except in the case of South Africa. Surprisingly, excepted durations of real estate are far shorter than ones of equities. The patterns of regimes in panels 19 and 20 show similar patterns as in Table 5. The interesting part is that excepted durations for Russia-excepted durations of Russia are fairly long. Normally, currencies markets lead movements in stock markets, followed by equities, then bonds and final the real estate. Based on the latter principle, Russian commodities Markov transitions are longer because of long excepted duration of Russian bond index which was preceded by equities volatilities. Similar, real estate volatilities follow the same pattern. The Russian commodities volatilities are higher because Russian is major player in the commodities market in the world.
Panel 17: equities | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Brazil | China | India | Russia | South Africa | ||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.5147 | 0.4853 | 0.4494 | 0.5506 | 0.9731 | 0.0269 | 0.9756 | 0.0244 | 0.0000 | 1.0000 |
2 | 0.5159 | 0.4841 | 0.5897 | 0.4102 | 0.9977 | 0.0023 | 0.6888 | 0.3112 | 0.3045 | 0.6955 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
2.0605 | 1.9382 | 1.8161 | 1.6955 | 37.1527 | 1.0023 | 40.9446 | 1.4518 | 1.0000 | 3.2839 | |
Panel 18: real estate | ||||||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.0000 | 1.0000 | 0.9847 | 0.0153 | 0.0000 | 1.0000 | 0.1127 | 0.8873 | 0.9934 | 0.0066 |
2 | 0.0544 | 0.9456 | 0.5366 | 0.4634 | 0.0343 | 0.9657 | 0.0744 | 0.9256 | 1.0000 | 0.0000 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1.0000 | 18.3795 | 65.2176 | 1.8635 | 1.0000 | 29.1281 | 1.1271 | 13.4442 | 153.0187 | 1.0000 | |
Panel 19: commodities | ||||||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.0000 | 1.0000 | 0.3956 | 0.6044 | 0.1097 | 0.8903 | 0.0189 | 0.9811 | 0.4853 | 0.5147 |
2 | 1.0000 | 0.0000 | 0.0466 | 0.9534 | 0.9999 | 0.0000 | 0.0000 | 0.9997 | 0.3379 | 0.6621 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1.0000 | 1.0000 | 1.6545 | 21.4513 | 1.1232 | 1.0000 | 1.0193 | 3059.4310 | 1.9430 | 2.9595 | |
Panel 20: bonds | ||||||||||
CTP | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 | 0.9738 | 0.0262 | 0.0000 | 0.9999 | 0.5174 | 0.4826 | 0.0000 | 1.0000 | 0.0000 | 1.0000 |
2 | 1.0000 | 0.0000 | 1.0000 | 0.0000 | 0.5776 | 0.4224 | 0.0197 | 0.9802 | 0.0033 | 0.9967 |
CED | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
38.2366 | 1.0000 | 1.0000 | 1.0000 | 2.0720 | 1.7314 | 1.0000 | 50.6053 | 1.0000 | 303.1733 |
Markov transition-in sample: 2012–2017.
Note: CTP and CED stand for constant transition probabilities and expected duration, respectively.
To sum up, this study illustrates that; firstly, there are spillovers that happen across, in-between and within bonds, commodities, equities and real estate indices. Secondly, sometimes the illiquid indices contribute more to volatility spillovers than liquid indices. Thirdly, expected durations of illiquid indices have shorter time spans than liquid indices. Fourth, in most cases, the volatility spillovers patterns during the out-sample period are similar to ones emanating during the in-sample period. Finally, periodical movement patterns vary across, in-between and within bonds, commodities, equities and real estate indices.
The implications from this study as follows. Firstly, similar governmental formations should be encouraged throughout the world provided that there economic benefits associated with those formations. Secondly, investing in different indices should be encouraged-diversification pays. Thirdly, there are risk management strategies that one can design based on volatility spillovers across, in-between and within bonds, commodities, equities and real estate indices. Fourth, the BRICS formation has indirectly influenced how capital markets (i.e. bonds, commodities, equities and real estate indices) behave. Finally, there are numerous investment strategies that investment managers can build based on volatility spills.
We are grateful to Carlos Campani for providing data on Brazilian commodities index. The remaining errors are our own.
The authors declare no conflict of interest.
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