\r\n\tGlobalization does not represent a pure and generous process for humanity or other species, but rather it implies social exclusion and also provokes situations of vulnerability in groups of people, forced exclusion, and apartheid: poor job opportunities, lack of access to education, worse socio-sanitary conditions. Specifically, it can be said that social segregation entails the apartheid of social groups of different ages, genders, and ethnicities; these groups live a reality manifested through the deepening of poverty, in terms of increased vulnerability of the poor and groups with little economic, social, cultural, labor and health stability.
\r\n
\r\n\tThis book aims to talk about some topics that are neglected in the discourses of academic communities and political elites. The inequality process is deeply rooted among humans and is part of many people's lives in the form of modern apartheid, gender segregation, lack of health access, and cultural gap. All those structural inequality processes are the product of the biopower perpetuated and produced in the macrosystem, exosystem, mesosystem, and microsystem. For many people from the academy, the information-consuming public, and the society in general, it is a problem to talk about these processes, since they have either lost interest or have normalized the structural and social inequity. For this reason, we see it as transcendental to explain how this situation occurs from the most internal fibers to the most evident processes, intending to make it more visible and thus expose the situation for possible solutions.
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1. Introduction
A sliding inguinal hernia is a protrusion of a retroperitoneal organ through an abdominal wall defect. Frequency of sliding hernias is estimated at 3-8% of all elective operations of inguinal hernias. Sliding hernias are supposed to be more anatomically challenging for a surgeon than an uncomplicated non-sliding inguinal hernias. The anatomical and physiological concept of sliding inguinal hernia is frequently misunderstood by surgeons of all levels of experience. Not infrequently, any inguinal hernia that is big enough or has any organ (e.g. small intestine) inside its sac is referred to as sliding hernia. In this chapter we will try to clarify the pathology behind the sliding inguinal hernia and explain its correct management.
2. History and current classification
Sliding hernias are known to surgeons for almost three centuries. Since the first description by Italian surgeon and anatomist from Pavia, Antonio Scarpa in 1809 [1] they were feared as a complicated surgical conditions. The main obstacle in surgical approach to this type of hernias was-and still is-the fact that part of the hernia sac is in reality a retroperitoneal organ thus, during opening of the sac an inadverent damage to a vital organ can be made. The advance of anatomical knowledge and evolution of surgical technique allowed for a better understanding of this entity. With the better understanding of pathological anatomy of the sliding hernia various classification systems have been introduced. Currently the best and most frequently used classification of sliding hernias is the one by Robert Bendavid [2]. Bendavid divides the sliding inguinal hernia into three anatomical variants depending on the size of the sac and its relation to the retrperitoneal „slided” organ. Type I is defined as any hernia in which part of the peritoneal sac is made up by the wall of a viscus (Figure 1). Type II is defined as any hernia containing a retroperitoneal viscus and its mesentery, in which the mesentery forms part of the wall of the peritoneal sac (Figure 2). In type III the sliding hernia consists on a protrusion of a viscus itself, and the peritoneal sac is very small or even absent. This last variant is an extremely rare finding and accounts for approximately only 0.01% of all inguinal hernias. Figure 1 and Figure 2 depict in a schematic way two most common types of sliding inguinal hernias according to Bendavid. We strongly advocate the use of this classification in everyday practice as it enables surgeons to better understand the concept and hence better plan the operation of a sliding inguinal hernia.
Figure 1.
Schematic drawing of Bendavid Type I sliding inguinal hernia. The posterolateral aspect of the hernia sac is made up of the caecum and ascending colon. This type of sliding inguinal hernia accounts for almost 95% of all sliding inguinal hernia cases. The most common contents are: sigmoid, caecum, appendix (by dr Jerzy W. Mituś based on Bendavid [2]).
Figure 2.
Schematic drawing of Bendavid Type II sliding inguinal hernia. In this hernia the mesentery forms part of the posterior wall of the sac and part of the anterior wall of the caecum forms part of the posterior wall of the sac. This type of sliding inguinal hernia accounts for about 5% of all sliding inguinal hernia cases. The most common contents is sigmoid (by dr Jerzy W. Mituś based on Bendavid [2]).
3. Clinical presentation
Sliding hernia is quite a common finding in infant girls: up to 20% of all hernias in this group of patients are sliding hernias containing ovary and fallopian tube [3]. In the adult population almost all cases of sliding hernias are seen in men [4, 5] with only isolated reports of sliding inguinal hernias in women [6]. Frequency of the sliding hernia in adults was historically estimated at around 6–8% of all hernia cases [4] but more recent report by our group estimate its frequency at 3.4% [5]. Most probably it is due to the fact that today’s hernia patient present with smaller hernias with shorter duration of symptoms or even before the onset of symptoms. The sliding hernias tend to occur in older patients that develop symptoms for quite a long time. In one of the biggest series of inguinal sliding hernias, published by Ryan in 1956, the average age of patients with sliding hernia was 60 years[4]. On the other hand, in our recently published series the average age of patients presenting with sliding inguinal hernia was estimated at 70 [5] and is higher that some current series reporting a mean age of 63 years [7]. The mean duration of symptoms in the series by Ryan was 12 years, 6 years in our series and in some reports it can be as low as 9 months [8]. As one can see the age of patients and duration of symptoms have dropped over the time, but still are higher than the same numbers describing current general population of inguinal hernia patients. The mean age of an inguinal hernia patient is estimated at 53 years and the mean duration of symptoms at 2.8 years for UK patients [9]. These numbers can be quite different in some selected groups of patients as in the aforementionned report by Adams which cited only 9 months median duration of symptoms of a sliding inguinal hernia patients in his Australian patient population [8].
It is very rare to establish preoperative diagnosis of a sliding inguinal hernia as there are no particular clinical signs indicating the possibility of sliding hernia. Older patients with big hernias, presenting with a long history of inguinal lump are the group most likely to have a retroperitoneal organ protruding into the hernia sac [5]. In the literature there are rare case reports of preoperative diagnosis of a sliding inguinal hernia containing urinary bladder based on a plain abdominal x-ray showing urinary bladder calculi within the groin [10]. However, in the vast majority of cases the diagnosis is made after the hernia sac is opened (as seen on Figure 3). If a surgeon does not open a hernia sac, a small sliding hernia can be easily overlooked. If the sac is manipulated gently this should not have any influence on the outcome of surgery in therms of early and late complications. As in the current practice it is becoming increasingly rare for surgeons to routinely open an inguinal hernia sac, a number of sliding hernias can undergo surgery without being recognized as such.
Interestingly, important percentage of sliding inguinal hernias can present as complicated cases with almost 12% of all sliding hernias presenting with intestinal obstruction to the emergency department [7]. This finding is obviously absent in reports on sliding hernias that are dealing only with elective inguinal hernia cases [5].
Another possible form of acute presentation of a sliding inguinal hernia is a perforation of the cancer of the sigmoid colon forming part of the hernia sac. This complication, although extremely rare implies a change in opertive strategy as correction of the hernial defect falls behind the priorities of resolving perforation and performing adequate oncologic colon resection [11].
Figure 3.
Opening of an inguinal sliding hernia sac. (Servicio de Cirugia General, Hospital Virgen del Camino, Sanlucar de Barrameda-Cadiz, Spain)
4. Management
Traditionally sliding hernias were considered difficult to operate on. For an inexperienced or non-prepared surgon even today it can pose quite a challenge [8]. One of the main reason why sliding inguinal hernia used to be considered such a difficult operation, was that in previous decades it was customary to aim at the excision of a whole peritoneal hernia sac and high ligation of a remaining stump. Given that in a sliding hernia a part of the sac is formed by the retroperitoneal organ the risk of injury of that organ was indeed higher. Currently the excision of a sac is not considered mandatory. Gentle dissection of the sac allows to perform tension free repair as in any inguinal hernia operation [2].
In the recently published paper our group have tried to identify all sliding inguinal hernias in a series of almost 500 elective hernia operations [5]. During the study period we have recorded 16 cases of a sliding inguinal hernias (incidence 3.4%). All patients with sliding hernias were male. The dominant hernia side was left (69%) similarly to the majority of the published series e.g. in the series by Patle [7] it reached 76%, with sigmoid colon being the most common slided organ. Other slided organs were retroperitonel appendix, caecum and urinary bladder and all were Bendavid type I hernias (Figure 1).
The opening of the sac and control of its content was performed in all our cases to confirm the diagnosis of a sliding hernia. However, some authors suggest that in case of a doubt as to the nature of the sac it is advisable not to open it, as the sac itself may prove to be intestinal or urinary bladder wall [2]. As mentioned before it is not necessary to open all hernia sacs. In our series the opening of all suspected sacs was indeed performed but only due to the experimental nature of the study. All patients in our experience were operated using prosthetic mesh with or without plug. This approach can be seen as a bit risky but once again we should stress that gentle dissection and clear anatomy are the keys to the success of the operation and the safe use of the same prosthetic materials that we routinely use for our inguinal hernia patients. We have indeed not seen any intestinal complications in our group of sliding inguinal elective hernias. The only postoperative complications that might be attributable to the use of mesh in this series are wound infection and seroma formations. However, none of these complications required explanations of the mesh [5]. In the long term follow-up we have not observed any rise in the abdominal symptoms that could be attributable to the use of prosthetic material close to the serous surface of the retroperitoneal organ that formed the hernia sac.
The laparoscopic repair of a sliding inguinal hernia is possible, however it requires important technical skills. Even in the hands of the most experienced laparoscopic hernia surgeons the conversion to open procedure can be necessary in as much as 10% of all cases [7].
5. Conclusion
The fundaments of sliding inguinal repair are meticulous, gentle dissection and identification of all anatomical structures. Opening of the sac is not necessary. A surgeon operating according to these principles and supported by a wide range of prosthetic material is in a far better situation than his surgical predecessors. The good operating technique and the use of modern prosthetic materials should allow us to have the same risk of early and late complications after operating on a sliding and non-sliding inguinal hernia.
Acknowledgments
The authors wish to thank dr Jerzy W.Mituś from Kraków, Poland for the preparation of the Figures 1 and 2.
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Department of Surgical Oncology, Maria Skłodowska-Curie Memorial Cancer Centre, Kraków, Poland
Department of General Surgery, Hospital San Juan Grande, Jerez de la Frontera, Cadiz, Spain
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\n
1. Introduction
\n
\n
1.1 Main study purpose
\n
Easley et al. [1] designed a tool, nicknamed volume-synchronized probability of informed trading (VPIN), with the aim to predict flash crashes. It appeared it could predict the “flash crash” of May 6, 2010, a few hours before it happened [2]. A lot of papers were published [3, 4, 5], and it was proposed to use it for regulation through a VPIN contract [2, 6]. However, critics pointed out some flaws, questioning its reliability [7, 8, 9, 10, 11] but without providing a quantitative evaluation of the prediction quality (e.g., in terms of precision and recall rates). In this study, we design a framework to detect flash crashes and thereby assess the behavior of the VPIN tool enabling as well as comparing and benchmarking with other predictive algorithms.
\n
\n
\n
1.2 Motivation
\n
The amount of trading data has exploded in finance thanks to the continuing progress of high-frequency techniques. It constrains practitioners to use more and more state-of-the-art algorithms to deal with this overwhelming amount of information. Computers and algorithms are more and more efficient, but still decision-making is highly dependent on both the quantity and the quality of information. Thus, errors and speculations that can make the financial market toxic, i.e., conducive to crashes, are possible. Examples in the past, such as the “flash crash” of May 6, 2010, have shown that this new paradigm in finance has made it possible to introduce a new kind of crashes characterized by their suddenness. Such quick crashes seem dangerous because of a kind of inherent unpredictability. However, predictive models to model this new framework do exist.
\n
\n
\n
1.3 Model
\n
Easley et al. [12] designed a model of the high-frequency financial market based on flows of informed and uninformed traders. They showed that information is a key parameter of the spread between ask and bid of prices. The model works as follows. Each day, general conditions and circumstances may or may not result in events that can help predict the evolution of the price of a future. More precisely, for each day, nature decides whether or not there is an event that can help predict the evolution of the price of a future. This is modeled with a Bernoulli law of parameter α. If an event occurs, nature also decides with a Bernoulli law of parameter δ if this event is a low signal. With these conditions, buys and sells for this future come then from flows of informed and uninformed traders. They are modeled by Poisson processes of respective parameters μ and ϵ. This framework can be summarized by the following tree in Figure 1 [13].
\n
Figure 1.
A tree summarizing the trading process.
\n
The whole trading process studied is thus a mixture of Poisson processes. It enabled authors to compute ask and bid and then the spread. They showed that for reasonable cases the spread is linearly linked with the following probability they named probability of informed trading (PIN) [12]:
\n
\n\nPIN\n=\n\nαμ\n\nαμ\n+\n2\nϵ\n\n\n.\n\nE1
\n
Later, Easley et al. [2] designed a new framework to easily compute this probability. Indeed PIN numbers come from a parametrized framework, and one does not have access to all these parameters. They showed however that PIN can be well approximated through a volume-clock paradigm [14], thanks to data of futures with a new formula. The approximated version of PIN was then called the volume-synchronized probability of informed trading (VPIN). It appeared that this new tool could predict the “flash crash” of May 6, 2010, a few hours before it happened [2].
\n
Nevertheless, the model has received a lot of critics. For example, Andersen and Bondarenko have shown [7] that VPIN is quite sensitive to the starting point of when one starts computing VPIN on a data set. It indeed questions VPIN prediction quality. Moreover, they have also shown that VPIN is sensitive to other parameters, such as the trade classification rule used [8] or how one defines the average daily volume of trades [9]. Changing the classification rule may drastically change VPIN behavior [9]. Pöppe et al. have reached the same conclusions with a different approach. Using a different classification rule can change VPIN prediction power toward a crash (in their paper a German blue-chip stock [11]). Besides, controlling ex ante parameters seems to give poorer prediction quality [8, 9]. This point has also been checked by Abad et al. [10]. Controlling ex ante realized volatility, and trading intensity, as did Andersen and Bondarenko [9], prediction quality seems to vanish. More deeply, they have also underlined that it is not obvious how one should define a VPIN prediction, analyzing more precisely toxic and nontoxic halts, as well as toxic events. Furthermore, Torben G. Andersen and Oleg Bondarenko interpret VPIN as being too sensitive to trading intensity. They have also explained that VPIN metric is sometimes unexpectedly correlated with other usual ones (such as VIX or RV) [7, 8]. More recently, it has been shown theoretically that the volume-clock paradigm of VPIN framework does not enable to really approximate fully the PIN value, although the proposed formula is close [15, 16].
\n
More generally, all these critics have pointed out that:
First, it is not obvious how one should use VPIN.
Second, prediction quality has not been studied sufficiently to assess it as being reliable.
Third, the study lacks objective benchmark.
\n
\n
\n
1.4 Goal
\n
The purpose of this chapter is to quantify the prediction quality of VPIN in order to enable practitioners to assess whether or not it can be used in the real world (e.g., for trading or regulation). That’s why:
First, we want to design a proper framework to compute precision and recall rates as well as prediction length of VPIN. This will be possible by providing a formal definition of flash crashes. To be more precise, we will use the maximum of intermediate return (MIR) [5] to define it.
Second, we want to study through this framework how sensitive VPIN is to the starting point of the data set.
\n
\n
\n
1.5 Plan
\n
In the following, we first recall VPIN model and propose a definition for flash crashes (Section 2). Second, we assess within this framework VPIN prediction quality (Section 3). Finally, we assess VPIN sensitivity to the starting point of the data set (Section 4).
\n
\n
\n
\n
2. VPIN software and formal flash crash definition
\n
In this section, we first recall the VPIN model. Second, we propose a definition of flash crashes used to compute precision and recall rates. Finally, we present the data used in our tests.
\n
\n
2.1 VPIN software
\n
Easley et al. [12] designed a model of the high-frequency financial market based on informed and uninformed traders. It is then possible to compute a probability of informed trading (PIN). Easley et al. [1] use these results and define an easy way to compute PIN only through the data of trades. We describe briefly VPIN model used in previous literature. The theoretic study of the model is treated in another research study.
\n
\n
2.1.1 Bars
\n
Following Easley et al. [1], a bar is a fixed volume of trades that are successive in time. With such a definition, one can associate the following quantities with each bar:
A nominal price, computed according to a given technique (mean price, median price, closing price, opening price, etc.)
A nominal time (first trade time, last trade time)
Local maximum and minimum values of trades
\n
In practice, the last few trades that do not fill up a bar are dropped to the next bar.
\n
\n
\n
2.1.2 Bulk volume classification
\n
The computation of VPIN requires to determine directions of trades, i.e., classifying each trade as a buy or a sell. The method used here is the following: bulk volume classification (BVC) [1, 5]. Let us note \n\n\nV\nb\n\n\n the volume of a bar and j the label of bar number j (\n\nj\n>\n0\n\n) and \n\n\nP\nj\n\n\n its price (closing, opening, median, mean). Then the number of buys \n\n\nV\nj\nb\n\n\n within bar j is determined according to this formula:
where \n\nZ\n\n is the cumulative function of a given law (usually student or normal distribution) and \n\nσ\n\n is the standard deviation of the numerator on successive number of bars. In our test, \n\nσ\n\n is computed once on all successive values of the data set, and the student law is of parameter one. Within bar j the number of sells \n\n\nV\nj\ns\n\n\n is obviously
A bucket is defined to be a fixed number of successive trades. Here to simplify, as bars are defined also as a fixed number of trades, a bucket will be m successive bars. Let us note \n\n\nV\nbucket\n\n\n the fixed volume of a bucket. We naturally have \n\n\nV\nbucket\n\n=\n\nmV\nb\n\n\n.
\n
\n
\n
2.1.4 VPIN formula
\n
VPIN formula is computed on n successive buckets, where n is VPIN support. A buffer is defined as n successive buckets. Here is VPIN formula, approximating (1) upon bucket number j (\n\nj\n≥\nn\n\n):
In order to distribute all VPIN values between 0 and 1, in practice, VPIN is normalized through a normal law. We thus consider \n\n\nVPIN\nnormalized\n\n\n in the following:
\n
\n
\n
2.1.5 VPIN event
\n
A VPIN event is declared when the following occurs:
\n
\n\n\nVPIN\nnormalized\n\n≥\n\nθ\nVPIN\n\n\nE5
\n
where \n\n\nθ\nVPIN\n\n\n is a given decision threshold. In practice [5] \n\n\nθ\nVPIN\n\n=\n0.99\n\n.
\n
\n
\n
\n
2.2 Defining flash crashes with MIR
\n
\n
2.2.1 Formal definition
\n
Let \n\n\n\n\np\nt\n\n\nt\n\n\n be a time series (e.g., of prices). Here is the definition of MIR:
The amplitude of the crash, which means extreme MIR values (e.g., 10%)
The shortness of the fall, which means the shortness of the time window within \n\nη\n\n that computes \n\n\nMIR\n\nt\n,\nη\n\n\n\n (e.g., 10 minutes), more precisely, noting \n\n\ni\n∗\n\n,\n\nj\n∗\n\n=\n\nargmax\n\ni\n≠\nj\n,\ni\n,\nj\n∈\n\nt\n\nt\n+\nη\n\n\n\n\n\n\n∣\n\np\ni\n\n−\n\np\nj\n\n∣\n\n\np\ni\n\n\n\n, the fall has length \n\n∣\n\nj\n∗\n\n−\n\ni\n∗\n\n∣\n\n
\n
\n
\n
2.2.2 Empiric definition
\n
We reported in this data set only one flash crash, i.e., on May 6, 2010, which lasted approximately 10 minutes according to media and financial institutions. Our definition of flash crash will obviously take into account this event.
\n
\n
\n
\n
2.3 The data
\n
\n
2.3.1 Futures used
\n
In this work, we use a comprehensive set of liquid futures trading data to illustrate the techniques to be introduced. More specifically, we will use 67 months’ worth of tick data of the five most liquid futures traded on all asset classes. The data comes to us in the form of 5 CSV files, one for each futures contract traded. The source of our data is TickWrite, a data vendor that normalizes the data into a common structure after acquiring it directly from the relevant exchanges. The total size of the comma-separated value (CSV) files is about 45.1 GB. They contain about millions of trades spanning from the beginning of January 2007 to the end of July 2012. The data set contains five of the most heavily traded futures contracts. Each has more than 100 million trades during this 67-month period. The most heavily traded futures, the file containing E-mini SP500 futures, symbol ES, has about 500 million trades involving a total number of about 3 billion contracts. The second most heavily traded futures is Euro exchange rates, symbol EC, which is 188 million trades. The next three are Nasdaq 100 (NQ), 173 million trades; light crude oil (CL), 165 million trades; and E-mini Dow Jones (YM), 110 million trades. In Figure 2, one can see an evolution of prices with time (here each tick corresponds to a bucket).
\n
Figure 2.
Bucket S&P 500 values with time.
\n
\n
\n
2.3.2 Definition of flash crash
\n
We want to define empirically a flash crash using the tools of VPIN framework, namely, bars and buckets. As volume-clock paradigm does not allow to control filling times of fixed volume of trades, here below is a summary of the steps we have followed to manage to detect flash crashes using MIR. As it is quite long and the main purpose of study is the prediction of results of the following section, we present principles and do not go into technical details:
To be sure not to miss a flash crash because of being too long in time bar or bucket, we have chosen a reasonable granularity level as in [5] (buckets per day, 200, and bars per bucket, 30).
For each financial instrument, we have recorded the number of bars necessary to capture the local 10 minutes of maximum fall of May 6, 2010, known as the “flash crash”; we refer to these numbers as “window lengths” below.
As the window lengths defined above do not have a stable distribution in time (because of the volume-clock paradigm), we have arbitrarily filtered out all events in which the time difference between minimum and maximum within a window length is longer than 20 minutes, in order to capture only quick events. Indeed, one given window length may be too big and thus allow at some date to measure a time difference between local minimum and maximum which is longer than 10 minutes whereas it would be a true flash crash with a smaller window length.1
For each instrument we recorded the amplitude of the “flash crash” and their respective MIR values.
\n
The results made it possible to classify the five financial instruments into two groups:
Data sets where the “flash crash” and other flash crashes are significantly present: ES, NQ, and YM.
Data sets where the “flash crash” and other flash crashes are not really present. More precisely, the “flash crash” is not a rare event in the data set, and generally magnitude levels of flash crashes are low compared to other instruments.
\n
\n
\n
\n
\n
3. Assessing VPIN prediction quality
\n
In this section, first we present our methodology to find VPIN optimal prediction quality (for which recall and precision rates are maximal and more useful for practice). Second, we present all the results: best parameters, associated remarks, and prediction lengths.
\n
\n
3.1 Methodology
\n
\n
3.1.1 Parameters to test
\n
Here are the parameters we will test:
Bar price: mean, median, last price, first price
MIR decision threshold θMIR to detect a flash crash
VPIN support n
VPIN classifier (student, normal)
Prediction window ω (described below)
VPIN decision threshold θVPIN to predict a flash crash
\n
\n
\n
3.1.2 Defining true positive events
\n
Here we describe how we define true positive, false-negative, and false-positive events. For a given prediction window length ω:
From a MIR flash crash detection (i.e., MIRj ≥ θMIR) at a bucket j (j ≥ ω), if in the window of buckets [j-ω,j-1] there is a VPIN event (i.e., VPINNormalized,i ≥ θVPIN, i ∈ [j-ω,j-1]), then we consider it as a true positive event.2 Otherwise it is a false-negative event.
From a VPIN event at a bucket j (i.e., VPINNormalized,j ≥ θVPIN, \n\nj\n+\nω\n≤\nend\n\nOf DataSet\n\n), if in the window of buckets [j + 1,j + ω] there is a flash crash ((i.e., MIRi ≥ θMIR, i ∈ [j+1,j+ω]), then we consider it as a true positive event.3 Otherwise it is a false-positive event.
\n
\n
\n
3.1.3 Choosing the maximum value of ω
\n
To make a useful deep search, we have computed the distribution of time difference between different amounts ω of buckets. Indeed, we want to control a temporal time window reasonable for practitioners and still sufficiently wide so that we can analyze which events VPIN can detect or not. We have focused this research to have a stable bounded distribution of time difference between ω buckets of about 1 month. Below one can see the respective distribution for the S&P500 instrument; the four other distributions of the instruments studied look the same (Figure 3).
\n
Figure 3.
Time difference distribution between 2500 S&P 500 buckets.
\n
In Table 1 one can see the medians of the different distributions.
\n
\n
\n
\n
\n\n
\n
Futures
\n
Days
\n
Number of bucket chosen
\n
\n\n\n
\n
ES
\n
14.8
\n
2500
\n
\n
\n
EC
\n
13.8
\n
2500
\n
\n
\n
CL
\n
15.0
\n
2500
\n
\n
\n
YM
\n
14.3
\n
2500
\n
\n
\n
NQ
\n
15.2
\n
2500
\n
\n\n
Table 1.
Median of time difference between 2500 buckets for the different instruments.
\n
For the next step, ω ≤ 2500.
\n
\n
\n
3.1.4 Describing deep search of flash crash prediction
\n
Here we describe how we intend to make a first deep search of VPIN prediction quality of events close to the “Flash Crash” of May 2010. In this algorithm described below θVPIN = 0.99.4
\n
For each VPIN classifier (student or Gaussian), for each bar price structure (last, first, median, average) do:
For each \n\n\nθ\nMIR\n\n∈\n\n\n5.2\n%\n,6.2\n%\n\n\n\n with step 0.1% for ES instrument, \n\n\nθ\nMIR\n\n∈\n\n\n2.2\n%\n,3.2\n%\n\n\n\n with step 0.1% for CL instrument, \n\n\nθ\nMIR\n\n∈\n\n\n0.4\n%\n,0.9\n%\n\n\n\n with step 0.1% for EC instrument, \n\n\nθ\nMIR\n\n∈\n\n\n8\n%\n\n\n9\n%\n\n\n\n with step 0.1% for NQ instrument, \n\n\nθ\nMIR\n\n∈\n\n\n5.4\n%\n,6.4\n%\n\n\n\n with step 0.1% for YM instrument5 do:
For each VPIN support \n\nn\n∈\n\n30\n60\n\n\n, with step 10, do:
For \n\nω\n∈\n\n100\n2500\n\n\n with step 100, do:
test prediction
store current parameters, precision and recall if and only if \n\nrecall\n+\nprecision\n≥\npreviousLocalMaximum\n\n
store prediction length (distance between VPIN event and MIR event).
\n
Remark: we first try to maximize precision+recall rate. If the local maximum found is interesting for practice (at least superior or equal to 1.2) and more powerful than a “naive” algorithm, then it sounds worth making a more serious search of precision and recall rates separately to find a good trade-off between them (e.g., thanks to a ROC curve).
\n
\n
\n
\n
3.2 Results
\n
\n
3.2.1 Best parameters found
\n
In Tables 2–5 one case see the best parameters that maximize precision+recall for each financial instrument and bar price structure studied.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.2171
\n
1.1908
\n
0.062
\n
60
\n
2400
\n
Gaussian
\n
Last
\n
\n
\n
EC
\n
0.9080
\n
0.9644
\n
1.8724
\n
0.006
\n
30
\n
2500
\n
Gaussian
\n
Last
\n
\n
\n
CL
\n
0.9406
\n
0.9045
\n
1.8451
\n
0.022
\n
60
\n
2500
\n
Student
\n
Last
\n
\n
\n
NQ
\n
1
\n
0.0034
\n
1.0034
\n
0.08
\n
30
\n
400
\n
Gaussian
\n
Last
\n
\n
\n
YM
\n
0.8421
\n
0.1512
\n
0.9933
\n
0.064
\n
60
\n
2500
\n
Gaussian
\n
Last
\n
\n\n
Table 2.
Best parameters maximizing precision+recall rate for different futures and last bar price structure in the first deep search.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.2024
\n
1.1761
\n
0.062
\n
60
\n
2400
\n
Gaussian
\n
First
\n
\n
\n
EC
\n
0.9127
\n
0.9681
\n
1.8808
\n
0.006
\n
30
\n
2500
\n
Student
\n
First
\n
\n
\n
CL
\n
0.9534
\n
0.9012
\n
1.8546
\n
0.022
\n
60
\n
2500
\n
Student
\n
First
\n
\n
\n
NQ
\n
1
\n
0.0038
\n
1.0038
\n
0.08
\n
30
\n
400
\n
Gaussian
\n
First
\n
\n
\n
YM
\n
0.8421
\n
0.1449
\n
0.9870
\n
0.064
\n
60
\n
2500
\n
Gaussian
\n
First
\n
\n\n
Table 3.
Best parameters maximizing precision+recall rate for different futures and first bar price structure in the first deep search.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.1996
\n
1.1733
\n
0.062
\n
60
\n
2400
\n
Gaussian
\n
Median
\n
\n
\n
EC
\n
0.9037
\n
0.9718
\n
1.8755
\n
0.006
\n
30
\n
2500
\n
Student
\n
Median
\n
\n
\n
CL
\n
0.9447
\n
0.8951
\n
1.8398
\n
0.022
\n
60
\n
2500
\n
Student
\n
Median
\n
\n
\n
NQ
\n
1
\n
0.0036
\n
1.0036
\n
0.08
\n
30
\n
400
\n
Gaussian
\n
Median
\n
\n
\n
YM
\n
1
\n
0.1911
\n
1.1911
\n
0.054
\n
30
\n
2500
\n
Student
\n
Median
\n
\n\n
Table 4.
Best parameters maximizing precision+recall rate for different futures and median bar price structure in the first deep search.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.1950
\n
1.1687
\n
0.062
\n
60
\n
2400
\n
Student
\n
Mean
\n
\n
\n
EC
\n
0.9058
\n
0.9691
\n
1.8749
\n
0.006
\n
30
\n
2500
\n
Student
\n
Mean
\n
\n
\n
CL
\n
0.9789
\n
0.8654
\n
1.8443
\n
0.022
\n
40
\n
2500
\n
Student
\n
Mean
\n
\n
\n
NQ
\n
1
\n
0.0036
\n
1.0036
\n
0.08
\n
30
\n
400
\n
Gaussian
\n
Mean
\n
\n
\n
YM
\n
1
\n
0.1921
\n
1.1921
\n
0.055
\n
30
\n
2500
\n
Student
\n
Mean
\n
\n\n
Table 5.
Best parameters maximizing precision+recall rate for different futures and mean bar price structure in the first deep search.
\n
\n
\n
3.2.2 Remarks and first interpretation
\n
We remark overall the following:
The choice of bar structure does not really affect the optimal choice of other parameters; nevertheless mean and median bar price structures have best precision+recall rate on average.
Recall rates are very close to 1.
Since ES, NQ, and YM precision rates are “low”, thus precision + recall rates are “low.”
Since EC and CL precision rates are “high,” thus precision + recall rates are “high” since recall is already “high.”
CL and EC had on May 6, 2010, a very low flash crash threshold, which increases a lot the number of crash of same magnitude detected in the data set.
CL and EC obtain their maximum value to the minimum bound of the deep search (respectively, a 2.2% fall and 0.6% fall). It is not the case for other instruments (in NQ cases, precision+recall optimal rate is constant from 0.8 to 0.9).
\n
The results give two first findings:
When the flash crash is significantly present for the instrument, i.e., of high magnitude and rare in the data set (ES, YM, and NQ cases), then recall is high, which means that VPIN makes a prediction before this happens, but precision is low: VPIN detects other events that are not flash crashes.
When the flash crash is not significantly present for the instrument, i.e., of low magnitude and not rare (there are a lot of events of 10–20-minute length of same magnitude), then recall and precision are high.
\n
This may suggest one of the following hypotheses:
VPIN seems to be a poor indicator of flash crash prediction with the usual recommended threshold 0.99.
VPIN can be a better indicator of another type of event (crashes of less important amplitude).
\n
We will compare the results of the same deep search with the one of a naive classifier, to see whether or not the good prediction results in CL and ES cases are relevant.
\n
\n
\n
3.2.3 Benchmark with a “naive classifier”
\n
We made a comparison of VPIN prediction quality result with a “naive classifier,” which randomly chooses whether or not there will be a crash from each bucket of the data set. In Table 6 one can see the results of the naive classifier for the first deep search set of parameters.6 As it is a naive classifier, results do not depend on direction of prices (bar price classifier) and bar price structure.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
\n\n\n
\n
ES
\n
1
\n
0.0355
\n
1.0355
\n
0.052
\n
50
\n
2500
\n
\n
\n
EC
\n
1
\n
0.9948
\n
1.9948
\n
0.004
\n
50
\n
2500
\n
\n
\n
CL
\n
1
\n
0.3413
\n
1.3413
\n
0.022
\n
50
\n
2500
\n
\n
\n
NQ
\n
1
\n
0.0076
\n
1.0076
\n
0.084
\n
60
\n
2500
\n
\n
\n
YM
\n
1
\n
0.0174
\n
1.0174
\n
0.055
\n
50
\n
2500
\n
\n\n
Table 6.
Best parameters maximizing precision+recall rate for different futures for the naive classifier.
\n
We remark the following:
“Naive classifier” has poor results comparable to those of VPIN for ES, NQ, and YM instruments; although poor, VPIN predictions are better than “naive algorithm” on ES cases.
“Naive classifier” has better results than VPIN on EC instrument.
“Naive classifier” has worse results than VPIN on CL instrument.
\n
We can interpret it as follows:
EC flash crash definition is barely inconsistent, with a MIR threshold of 0.006%; it is obvious that a naive algorithm does better results as the constraint is very small to detect a “flash crash” of such a magnitude.
On CL and ES cases though, VPIN predictions are better, and these results are obtained when θMIR threshold was on the lower bound of the deep search. It might indicate that VPIN software has a better predictive power than a “naive algorithm” not on a “flash crash” amplitude basis but on a lower amplitude level. Nevertheless, one may wonder whether or not this level of amplitude is useful for practitioners.
\n
Anyway, previous results may conclude that for “flash crash” prediction, VPIN has overall equivalent poor power prediction with the traditional threshold θVPIN = 0.99, as a “naive” algorithm.
\n
That’s why in the next paragraph, we benchmark predictive power of “naive” and VPIN algorithms:
First on higher θVPIN constraints
Second on lower bounds of crash amplitude θMIR while θVPIN = 0.99
Third on higher θVPIN constraints and at the same time lower bounds on θMIR
\n
Indeed, the first hypothesis is that there are too many false VPIN predictions, i.e., false-positive events, as precision rate is too low and recall rate is too high. That’s why one may hope that making θVPIN constraints higher may reduce the number of VPIN “useless” predictions while not reducing too much recall rate.
\n
\n
\n
3.2.4 Deep search allowing higher bounds for θVPIN
\n
In the following we have looked to higher bounds for θVPIN from 0.99 to 0.99999. All other parameters of the deep search are the same. Below, one can see the results in Tables 7–10. The results for the naive algorithm are indeed the same.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.4677
\n
1.4414
\n
0.062
\n
60
\n
1600
\n
Gaussian
\n
0.99999
\n
\n
\n
EC
\n
0.9080
\n
0.9644
\n
1.8724
\n
0.006
\n
30
\n
2500
\n
Gaussian
\n
0.99
\n
\n
\n
CL
\n
0.9406
\n
0.9045
\n
1.8451
\n
0.022
\n
60
\n
2500
\n
Student
\n
0.99
\n
\n
\n
NQ
\n
1
\n
0.0034
\n
1.0034
\n
0.08
\n
30
\n
400
\n
Gaussian
\n
0.99
\n
\n
\n
YM
\n
0.7091
\n
0.3160
\n
1.0251
\n
0.054
\n
60
\n
2500
\n
Student
\n
0.9999
\n
\n\n
Table 7.
Best parameters maximizing precision+recall rate for different futures and last bar price structure allowing higher bounds for θVPIN.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.3412
\n
1.3149
\n
0.062
\n
60
\n
1200
\n
Gaussian
\n
0.99999
\n
\n
\n
EC
\n
0.9127
\n
0.9681
\n
1.8808
\n
0.006
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
CL
\n
0.9534
\n
0.9012
\n
1.8546
\n
0.022
\n
60
\n
2500
\n
Student
\n
0.99
\n
\n
\n
NQ
\n
1
\n
0.0038
\n
1.0038
\n
0.08
\n
30
\n
2500
\n
Gaussian
\n
0.99
\n
\n
\n
YM
\n
0.7091
\n
0.3545
\n
1.0636
\n
0.054
\n
60
\n
2500
\n
Student
\n
0.9999
\n
\n\n
Table 8.
Best parameters maximizing precision+recall rate for different futures and first bar price structure allowing higher bounds for θVPIN.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.3306
\n
1.3043
\n
0.062
\n
30
\n
1700
\n
Gaussian
\n
0.99999
\n
\n
\n
EC
\n
0.9037
\n
0.9718
\n
1.8755
\n
0.006
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
CL
\n
0.9447
\n
0.8951
\n
1.8398
\n
0.022
\n
60
\n
2500
\n
Student
\n
0.99
\n
\n
\n
NQ
\n
1
\n
0.0036
\n
1.0036
\n
0.08
\n
30
\n
400
\n
Gaussian
\n
0.99
\n
\n
\n
YM
\n
1
\n
0.1911
\n
1.1911
\n
0.054
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n\n
Table 9.
Best parameters maximizing precision+recall rate for different futures and median bar price structure allowing higher bounds for θVPIN.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9737
\n
0.3786
\n
1.3523
\n
0.062
\n
60
\n
1600
\n
Gaussian
\n
0.99999
\n
\n
\n
EC
\n
0.9058
\n
0.9691
\n
1.8749
\n
0.006
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
CL
\n
0.9789
\n
0.8653
\n
1.8442
\n
0.022
\n
40
\n
2500
\n
Student
\n
0.99
\n
\n
\n
NQ
\n
1
\n
0.0036
\n
1.0036
\n
0.08
\n
30
\n
400
\n
Gaussian
\n
0.99
\n
\n
\n
YM
\n
1
\n
0.1921
\n
1.1921
\n
0.055
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n\n
Table 10.
Best parameters maximizing precision+recall rate for different futures and mean bar price structure allowing higher bounds for θVPIN.
\n
We remark the following:
Precision rate has increased for each bar price structure for ES instrument, maintaining recall rate constant to θVPIN = 0.99 case.
Precision + recall rate has increased for YM instrument only with a last or first bar price structure, but recall decreased a bit compared to θVPIN = 0.99 case.
Compared to the “naive” algorithm, VPIN results are effectively better in ES case. In YM case we still find comparable results.
On average, mean and median bar price structures have the best precision+recall rate.
\n
To verify whether or not we can get at least better results than a naive algorithm in data sets with a real flash crash, we study in the following first the results allowing lower bounds on θMIR while θVPIN = 0.99 and second the results allowing lower bounds on θMIR and higher constraints on θVPIN. Indeed, the intuition is that on NQ case, the “flash crash” amplitude constraints are far too high to have a good precision rate, because in this case there are too few events detected with MIR algorithm.
Results have changed for every instrument except the ES one which has kept the same local maximum as in the first deep search.
Precision is far higher than before, while recall is still high. Therefore, overall precision + recall rates are “high.”
Optimal θMIR is around 0.015 for ES, CL, NQ, and YM financial instruments, whereas for EC the previous local maximum around 0.006 remains higher.
On average, median bar price structure has the best precision+recall rate.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9421
\n
0.9541
\n
1.8962
\n
0.015
\n
30
\n
2500
\n
Student
\n
Last
\n
\n
\n
EC
\n
0.9080
\n
0.9644
\n
1.8724
\n
0.006
\n
30
\n
2500
\n
Gaussian
\n
Last
\n
\n
\n
CL
\n
0.9297
\n
0.9806
\n
1.9103
\n
0.016
\n
30
\n
2500
\n
Student
\n
Last
\n
\n
\n
NQ
\n
0.9179
\n
0.9019
\n
1.8198
\n
0.015
\n
30
\n
2500
\n
Gaussian
\n
Last
\n
\n
\n
YM
\n
0.9460
\n
0.9696
\n
1.9156
\n
0.015
\n
50
\n
2500
\n
Gaussian
\n
Last
\n
\n\n
Table 11.
Best parameters maximizing precision+recall rate for different futures and last bar price structure allowing higher bounds for θMIR.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9404
\n
0.9402
\n
1.8806
\n
0.015
\n
30
\n
2500
\n
Gaussian
\n
First
\n
\n
\n
EC
\n
0.9127
\n
0.9681
\n
1.8808
\n
0.006
\n
30
\n
2500
\n
Gaussian
\n
First
\n
\n
\n
CL
\n
0.9233
\n
0.9728
\n
1.8961
\n
0.016
\n
30
\n
2500
\n
Student
\n
First
\n
\n
\n
NQ
\n
0.8291
\n
0.9833
\n
1.8124
\n
0.01
\n
30
\n
2500
\n
Student
\n
First
\n
\n
\n
YM
\n
0.9517
\n
0.9673
\n
1.9190
\n
0.015
\n
50
\n
2500
\n
Student
\n
First
\n
\n\n
Table 12.
Best parameters maximizing precision+recall rate for different futures and first bar price structure allowing higher bounds for θMIR.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9499
\n
0.9498
\n
1.8997
\n
0.015
\n
30
\n
2500
\n
Student
\n
Median
\n
\n
\n
EC
\n
0.9037
\n
0.9717
\n
1.8754
\n
0.006
\n
30
\n
2500
\n
Student
\n
Median
\n
\n
\n
CL
\n
0.9265
\n
0.9718
\n
1.8983
\n
0.016
\n
30
\n
2500
\n
Student
\n
Median
\n
\n
\n
NQ
\n
0.9243
\n
0.9017
\n
1.8260
\n
0.015
\n
30
\n
2500
\n
Gaussian
\n
Median
\n
\n
\n
YM
\n
0.9829
\n
0.9427
\n
1.9256
\n
0.015
\n
30
\n
2500
\n
Gaussian
\n
Median
\n
\n\n
Table 13.
Best parameters maximizing precision+recall rate for different futures and median bar price structure allowing higher bounds for θMIR.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
Bar price
\n
\n\n\n
\n
ES
\n
0.9526
\n
0.9454
\n
1.8979
\n
0.015
\n
30
\n
2500
\n
Student
\n
Mean
\n
\n
\n
EC
\n
0.9058
\n
0.9691
\n
1.8749
\n
0.006
\n
30
\n
2500
\n
Student
\n
Mean
\n
\n
\n
CL
\n
0.9302
\n
0.9670
\n
1.8972
\n
0.016
\n
30
\n
2500
\n
Gaussian
\n
Mean
\n
\n
\n
NQ
\n
0.9407
\n
0.8796
\n
1.8203
\n
0.02
\n
60
\n
2500
\n
Gaussian
\n
Mean
\n
\n
\n
YM
\n
0.9446
\n
0.9779
\n
1.9225
\n
0.015
\n
60
\n
2500
\n
Student
\n
Mean
\n
\n\n
Table 14.
Best parameters maximizing precision+recall rate for different futures and mean bar price structure allowing higher bounds for θMIR.
\n
In the following, we will first compare the results to the case where we allow higher bound on θVPIN, to see if there is a difference. Second, we will benchmark both results to the one of a “naive” classifier.
\n
\n
\n
3.2.6 Deep search allowing lower bounds for θMIR and higher bounds for θVPIN
\n
We remark in Tables 15–18 that compared to previous deep search:
There are changes only for NQ and YM instruments in, respectively, last, median, and mean bar price structures and first bar price structure, where θVPIN equals 0.999.
There is no general trend for precision or recall rates with the increase of θVPIN.
On average median bar price structure has the best precision+recall rate.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9421
\n
0.9541
\n
1.8962
\n
0.015
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
EC
\n
0.9080
\n
0.9644
\n
1.8724
\n
0.006
\n
30
\n
2500
\n
Gaussian
\n
0.99
\n
\n
\n
CL
\n
0.9297
\n
0.9806
\n
1.9103
\n
0.016
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
NQ
\n
0.9076
\n
0.9217
\n
1.8293
\n
0.02
\n
50
\n
2500
\n
Student
\n
0.999
\n
\n
\n
YM
\n
0.9460
\n
0.9696
\n
1.9156
\n
0.015
\n
50
\n
2500
\n
Gaussian
\n
0.99
\n
\n\n
Table 15.
Best parameters maximizing precision+recall rate for different futures and last bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9404
\n
0.9402
\n
1.8806
\n
0.015
\n
30
\n
2500
\n
Gaussian
\n
0.99
\n
\n
\n
EC
\n
0.9127
\n
0.9681
\n
1.8808
\n
0.006
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
CL
\n
0.9232
\n
0.9728
\n
1.8960
\n
0.016
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
NQ
\n
0.8291
\n
0.9833
\n
1.8124
\n
0.01
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
YM
\n
0.9341
\n
0.9872
\n
1.9213
\n
0.015
\n
50
\n
2500
\n
Gaussian
\n
0.999
\n
\n\n
Table 16.
Best parameters maximizing precision+recall rate for different futures and first bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9499
\n
0.9498
\n
1.8997
\n
0.015
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
EC
\n
0.9037
\n
0.9717
\n
1.8754
\n
0.006
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
CL
\n
0.9265
\n
0.9718
\n
1.8983
\n
0.016
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
NQ
\n
0.8881
\n
0.9525
\n
1.8406
\n
0.02
\n
30
\n
2500
\n
Student
\n
0.999
\n
\n
\n
YM
\n
0.9829
\n
0.9427
\n
1.9256
\n
0.015
\n
30
\n
2500
\n
Gaussian
\n
0.99
\n
\n\n
Table 17.
Best parameters maximizing precision+recall rate for different futures and median bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
Classifier
\n
θVPIN
\n
\n\n\n
\n
ES
\n
0.9526
\n
0.9454
\n
1.8980
\n
0.015
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
EC
\n
0.9058
\n
0.9691
\n
1.8749
\n
0.006
\n
30
\n
2500
\n
Student
\n
0.99
\n
\n
\n
CL
\n
0.9302
\n
0.9670
\n
1.8972
\n
0.016
\n
30
\n
2500
\n
Gaussian
\n
0.99
\n
\n
\n
NQ
\n
0.9188
\n
0.9150
\n
1.8338
\n
0.02
\n
40
\n
2500
\n
Gaussian
\n
0.999
\n
\n
\n
YM
\n
0.9446
\n
0.9779
\n
1.9225
\n
0.015
\n
60
\n
2500
\n
Gaussian
\n
0.99
\n
\n\n
Table 18.
Best parameters maximizing precision+recall rate for different futures and mean bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
\n
\n
\n
3.2.7 Benchmark with a “naive” classifier
\n
We remark the following for the “naive” classifier (Table 19):
It has worse results than VPIN on ES and YM cases.
It has comparable results than VPIN on NQ case.
It has better results than VPIN on EC and CL cases, where the flash crash is not really effective.
It reaches obviously best local results on lowest MIR bound of the deep search.
We may partially conclude that:
VPIN has an interesting predictive behavior on flash events of magnitude far lower (around 1.5%) than what would be considered as a crash for specific financial instrument (relatively liquid such as NQ, YM, or ES).
But VPIN has poor results comparable to those of a “naive” classifier (precision+recall rate inferior to 1.2) on flash crash events for these financial instruments.
For other instruments such as CL or EC, VPIN behaves worse than a naive classifier for these flash events. On flash events of higher amplitude (at least 1.5%), VPIN behaves better than a “naive” classifier for CL instrument.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Futures
\n
Recall
\n
Precision
\n
Precision+recall
\n
θMIR
\n
n
\n
ω (buckets)
\n
\n\n\n
\n
ES
\n
1
\n
0.7483
\n
1.7483
\n
0.01
\n
40
\n
2500
\n
\n
\n
EC
\n
1
\n
0.9999
\n
1.9999
\n
0.001
\n
60
\n
2500
\n
\n
\n
CL
\n
1
\n
0.9995
\n
1.9995
\n
0.01
\n
40
\n
2500
\n
\n
\n
NQ
\n
1
\n
0.8465
\n
1.8465
\n
0.01
\n
30
\n
2500
\n
\n
\n
YM
\n
1
\n
0.6892
\n
1.6892
\n
0.01
\n
40
\n
2500
\n
\n\n
Table 19.
Best parameters maximising precision+recall rate for different futures for the naive classifier allowing lower bounds for θMIR.
\n
\n
\n
\n
\n
4. VPIN sensitivity to the starting point of a data set
\n
In this section, first we present the problem of VPIN’s sensitivity to the starting point of the bucketing process. Second, we present different calibrations to test its sensitivity. Third we make a summary of our results.
\n
\n
4.1 The problem
\n
VPIN received among critics one which is important to precisely assess. Indeed, Bodarenko and Anderson [7] pointed out in their work that VPIN is sensitive to the starting point of the bucketing process. More precisely, if one removes the first buckets of the data set, results change. It is indeed right. We would like to know to which extent one can or cannot mitigate this effect. One idea is to test the different price bar structures. Indeed a bar structure influences trade imbalance and thus influences the appearance of VPIN events.
\n
\n
4.1.1 Methodology
\n
There are at least two interesting ways of analyzing the sensitivity to the starting point of a data set:
Study the sensitivity of best precision+recall rate to the number of trades erased and to the bar price option.
Given one set of local optimal parameters, study the sensitivity of precision and recall rates to bar price option and data removed.
\n
We have removed \n\nl\n∈\n0,1000,2000,3000\n\n number of bars to study the sensitivity in the two previous cases, which corresponds to several hours of trading data removed. Indeed one does not want to erase first flash crash detected in the data set and erase more buckets than the average prediction length to detect it. Moreover we would like to study to which extent VPIN is locally sensitive.
\n
\n
\n
\n
4.2 Summary of results
\n
\n
4.2.1 Sensitivity of precision+recall rate
\n
We summarize in Table 20 for each bar price structure the average percentage change of local new best precision+recall rates with the number of bar erased.
\n
\n
\n
\n
\n
\n\n
\n
Bar price structure
\n
1000 bars erased
\n
2000 bars erased
\n
3000 bars erased
\n
\n\n\n
\n
Last
\n
3.089
\n
2.166
\n
0.939
\n
\n
\n
First
\n
2.410
\n
3.649
\n
6.727
\n
\n
\n
Median
\n
0.611
\n
0.801
\n
0.781
\n
\n
\n
Mean
\n
1.348
\n
2.149
\n
3.944
\n
\n\n
Table 20.
Average absolute percentage change of local best precision+recall rates with the number of bar erased for each bar price structure.
\n
We remark the following:
The sensitivity mentioned by Bodarenko and Anderson does exist.
Its amplitude is not very big, at least for best precision+recall rate, as the maximum change is about 6%.
Median bar price structure is far less sensitive than other price structure.
\n
\n
\n
4.2.2 Sensitivity to local best parameter choice
\n
In Table 21 we summarize for each bar price structure the average percentabe change of the initial local best precision+recall rates with the number of bar erased.
\n
\n
\n
\n
\n
\n\n
\n
Bar price structure
\n
1000 bars erased
\n
2000 bars erased
\n
3000 bars erased
\n
\n\n\n
\n
Last
\n
1.192
\n
1.171
\n
1.067
\n
\n
\n
First
\n
1.612
\n
1.725
\n
1.049
\n
\n
\n
Median
\n
3.514
\n
3.137
\n
1.396
\n
\n
\n
Mean
\n
2.648
\n
3.180
\n
2.489
\n
\n\n
Table 21.
Average absolute percentage change of local best precision+recall rates with the number of bar erased for each bar price structure.
\n
We remark the following:
Again the amplitude of the sensitivity is not very large as the maximum change is about 3.5%.
Last bar price structure is less sensitive than other price structure to this phenomenon.
\n
\n
\n
\n
\n
5. Conclusion
\n
In this last section, we present first a general summary of our findings. Then we propose new suggestion of research concerning this precise subject.
\n
\n
5.1 Summary of results
\n
We found that:
VPIN has interesting predictive power (i.e., better than a naive algorithm and at least of local prediction+recall maximum higher than 1.2) for flash events of lower amplitude than flash crashes (about 1.5%) for a certain class of instruments, where flash crashes are at least present (which is not the case for currency Euro FX or Energy Light Crude NYMEX).
VPIN is sensitive to the starting point of computation, but the amplitude of this sensitivity is not really high. For practice, which means not changing local best parameters while erasing some data, last bar price structure is the least sensitive to this phenomenon.
\n
\n
\n
5.2 Suggestion for further studies
\n
For further studies, this might be worth analyzing:
Define a bigger constraint to capture crashes taking into account, for example, their V-shape. It would indeed filter out more events and enable analyzing more accurately which kind of crash VPIN predicts better.
Benchmark within this framework other predictive tools between them (VIX with a naive algorithm, with VPIN, etc.).
Analyze VPIN time-clock version predictive power.
If previous predictive power of lower amplitude flash events is interesting for practitioners, analyze more precisely parameters that would be interesting for them.
Describe more precisely to which class of financial instrument VPIN predictive power is most effective (if such one is worth being more studied for practitioners).
Define a normalization of events defining crash events within a whole cluster of instruments. It is not easy to put in place as instruments are more or less correlated by crashes and response times are not trivial to analyze, but it would be also interesting indeed to assess prediction quality on common events shared by different instruments of a same cluster. It would make it possible to see whether or not VPIN predictive power is effective beyond different financial instruments embedding different aspects of the financial world to which VPIN is sensitive to.
This area of research studies a very particular class of events: those that are potentially very rare. Taking into account this setting and that the algorithms used are fed with previous information and are sensitive to the starting point of computation, is it possible to build a consistent cross-validation approach? This aspect has not been treated yet as others needed to be first addressed, but it is still important to be studied.
List of future contracts and their total volume of trades from January 2007 to July 2012.
\n\n
\n',keywords:"high-frequency data, probability of informed trading, VPIN, liquidity, flow toxicity, volume imbalance, flash crash, JEL codes: C02, D52, D53, G14, G23",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67499.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67499.xml",downloadPdfUrl:"/chapter/pdf-download/67499",previewPdfUrl:"/chapter/pdf-preview/67499",totalDownloads:865,totalViews:0,totalCrossrefCites:0,dateSubmitted:"December 3rd 2018",dateReviewed:"April 25th 2019",datePrePublished:"August 23rd 2019",datePublished:"November 13th 2019",dateFinished:"June 4th 2019",readingETA:"0",abstract:"VPIN is a tool designed to predict extreme events like flash crashes. Some concerns have been raised about its reliability. In this chapter we assess VPIN prediction quality (precision and recall rates) of extreme volatility events including its sensitivity to the starting point of computation in a given data set. We benchmark the results with the ones of a “naive classifier.” The test data used in this study contains 5.6 year’s worth of trading data of the five most liquid futures contracts of this time period. We found that VPIN has poor “flash crash” prediction power with the traditional 0.99 decision threshold. Increasing the decision threshold does not significantly improve overall prediction quality. Nevertheless we found VPIN has a more interesting predictive power for flash events of lower amplitude. Finally, we found that, for practice, the last bar price structure is the least sensitive to the starting point of computation.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67499",risUrl:"/chapter/ris/67499",signatures:"Antoine Bambade and Kesheng Wu",book:{id:"8523",type:"book",title:"Advanced Analytics and Artificial Intelligence Applications",subtitle:null,fullTitle:"Advanced Analytics and Artificial Intelligence Applications",slug:"advanced-analytics-and-artificial-intelligence-applications",publishedDate:"November 13th 2019",bookSignature:"Ali Soofastaei",coverURL:"https://cdn.intechopen.com/books/images_new/8523.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78984-639-3",printIsbn:"978-1-78984-638-6",pdfIsbn:"978-1-83962-770-5",isAvailableForWebshopOrdering:!0,editors:[{id:"257455",title:"Dr.",name:"Ali",middleName:null,surname:"Soofastaei",slug:"ali-soofastaei",fullName:"Ali Soofastaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"288207",title:"M.Sc.",name:"Antoine",middleName:null,surname:"Bambade",fullName:"Antoine Bambade",slug:"antoine-bambade",email:"bambade.antoine@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"288649",title:"Prof.",name:"Kesheng (John)",middleName:null,surname:"Wu",fullName:"Kesheng (John) Wu",slug:"kesheng-(john)-wu",email:"kwu@lbl.gov",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Lawrence Berkeley National Laboratory",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Main study purpose",level:"2"},{id:"sec_2_2",title:"1.2 Motivation",level:"2"},{id:"sec_3_2",title:"1.3 Model",level:"2"},{id:"sec_4_2",title:"1.4 Goal",level:"2"},{id:"sec_5_2",title:"1.5 Plan",level:"2"},{id:"sec_7",title:"2. VPIN software and formal flash crash definition",level:"1"},{id:"sec_7_2",title:"2.1 VPIN software",level:"2"},{id:"sec_7_3",title:"2.1.1 Bars",level:"3"},{id:"sec_8_3",title:"2.1.2 Bulk volume classification",level:"3"},{id:"sec_9_3",title:"2.1.3 Buckets",level:"3"},{id:"sec_10_3",title:"2.1.4 VPIN formula",level:"3"},{id:"sec_11_3",title:"2.1.5 VPIN event",level:"3"},{id:"sec_13_2",title:"2.2 Defining flash crashes with MIR",level:"2"},{id:"sec_13_3",title:"2.2.1 Formal definition",level:"3"},{id:"sec_14_3",title:"2.2.2 Empiric definition",level:"3"},{id:"sec_16_2",title:"2.3 The data",level:"2"},{id:"sec_16_3",title:"2.3.1 Futures used",level:"3"},{id:"sec_17_3",title:"2.3.2 Definition of flash crash",level:"3"},{id:"sec_20",title:"3. Assessing VPIN prediction quality",level:"1"},{id:"sec_20_2",title:"3.1 Methodology",level:"2"},{id:"sec_20_3",title:"3.1.1 Parameters to test",level:"3"},{id:"sec_21_3",title:"3.1.2 Defining true positive events",level:"3"},{id:"sec_22_3",title:"Table 1.",level:"3"},{id:"sec_23_3",title:"3.1.4 Describing deep search of flash crash prediction",level:"3"},{id:"sec_25_2",title:"3.2 Results",level:"2"},{id:"sec_25_3",title:"Table 2.",level:"3"},{id:"sec_26_3",title:"3.2.2 Remarks and first interpretation",level:"3"},{id:"sec_27_3",title:"Table 6.",level:"3"},{id:"sec_28_3",title:"Table 7.",level:"3"},{id:"sec_29_3",title:"Table 11.",level:"3"},{id:"sec_30_3",title:"Table 15.",level:"3"},{id:"sec_31_3",title:"Table 19.",level:"3"},{id:"sec_34",title:"4. VPIN sensitivity to the starting point of a data set",level:"1"},{id:"sec_34_2",title:"4.1 The problem",level:"2"},{id:"sec_34_3",title:"4.1.1 Methodology",level:"3"},{id:"sec_36_2",title:"4.2 Summary of results",level:"2"},{id:"sec_36_3",title:"Table 20.",level:"3"},{id:"sec_37_3",title:"Table 21.",level:"3"},{id:"sec_40",title:"5. Conclusion",level:"1"},{id:"sec_40_2",title:"5.1 Summary of results",level:"2"},{id:"sec_41_2",title:"5.2 Suggestion for further studies",level:"2"},{id:"sec_44",title:"Table 22.",level:"1"}],chapterReferences:[{id:"B1",body:'Easley D, de Prado ML, O’Hara M. Flow toxicity and liquidity in a high frequency world. Review of Financial Studies. 2012;25(5):1457-1493\n'},{id:"B2",body:'Easley D, de Prado ML, O’Hara M. The microstructure of the ‘flash crash’: Flow toxicity, liquidity, crashes and the probability of informed trading. The Journal of Portfolio Management. 2011;37(2):118-128\n'},{id:"B3",body:'Zheng Y. VPIN and the China’s circuit-breaker. International Journal of Economics and Finance. 2017;9(12)\n'},{id:"B4",body:'Abad D, Yagüe J. From PIN to VPIN: An introduction to order flow toxicity. The Spanish Review of Financial Economics. 2011;6(2):8-13. Johnson School Research Paper Series No. 10-2011\n'},{id:"B5",body:'Wu K, Bethel EW, Gu M, Leinweber D, Rübel O. A big data approach to analyzing market volatility. Algorithmic Finance. 2013;2(3–4):241-267\n'},{id:"B6",body:'Easley D, de Prado ML, O’Hara M. The exchange of flow toxicity. The Journal of Trading. 2011;6(2):8-13. Johnson School Research Paper Series No. 10-2011\n'},{id:"B7",body:'Andersen TG, Bondarenko O. VPIN and the flash crash. The Journal of Financial Markets. 2014;17:1-46\n'},{id:"B8",body:'Andersen TG, Bondarenko O. Reflecting on the VPIN dispute. Journal of Financial Markets. 2014;17:53-64\n'},{id:"B9",body:'Andersen TG, Bondarenko O. Assessing measures of order flow toxicity and early warning signals for market turbulence. Review of Finance. 2015;19:1-54\n'},{id:"B10",body:'Abad D, Massot M, Pascual R. Evaluating VPIN as a trigger for single-stock circuit breakers. Journal of Banking and Finance. 2017;86(C):21-36\n'},{id:"B11",body:'Pöppe T, Moss S, Schiereck D. The sensitivity of VPIN to the choice of trade classification algorithm. Journal of Banking and Finance. 2016;73:165-181\n'},{id:"B12",body:'Easley D, Engle RF, O’Hara M, Wu L. Time-varying arrival rates of informed and uninformed trades. Journal of Financial Econometrics. 2008;6(2):171-207\n'},{id:"B13",body:'Easley D, Kiefer NM, O’Hara M, Paperman JB. Liquidity, information, and infrequently traded stocks. Journal of Finance. 1996;51(4):1405-1436\n'},{id:"B14",body:'Easley D, de Prado ML, O’Hara M. The volume clock: Insights into the high frequency paradigm. Journal of Portfolio Management. Vol. 39, No. 1, (01 Sep Fall). 11\n'},{id:"B15",body:'Ke W-C, Lin H-WW. An Improved Version of the Volume-Synchronized Probability of Informed Trading (VPIN)\n'},{id:"B16",body:'Easley D, de Prado ML, O’Hara M. An Improved Version of the Volume-Synchronized Probability of Informed Trading (VPIN): A Comment\n'}],footnotes:[{id:"fn1",explanation:"This is not perfect because we can still miss some crashes (whereas in this data set, it will not be that much, and it will be with a smaller probability), but first we do not want to change too much the definition in time of a flash crash (we will not increase the tolerance level to 1 day), and second this problem is inherent to the fact that fixing volume of bars and of buckets prevents us from controlling precisely filling bar and bucket times. Finding a solution for this precise data set does not guarantee at all a general solution neither for one data set nor for a financial instrument."},{id:"fn2",explanation:"If j − ω < 0, the window of buckets considered is [0,j-1]."},{id:"fn3",explanation:"If \n\nj\n+\nω\n>\nendOfDataSet\n\n, the window of buckets considered is [j + 1,endOfDataSet]."},{id:"fn4",explanation:"Previous research, such as [5], showed that this threshold is a good one."},{id:"fn5",explanation:"As each MIR value for the flash crash is different, one must adapt the area of deep search to be precise and have a quicker calculation time."},{id:"fn6",explanation:"First tests conducted with EC instrument have been realized with an average to get more robust results. They are really close to the one obtained here with a single realization of randomness."}],contributors:[{corresp:"yes",contributorFullName:"Antoine Bambade",address:"antoine.bambade@polytechnique.edu",affiliation:'
Lawrence Berkeley National Laboratory, Scientific Data Management (SDM) group, USA
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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580",scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). 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