Parameter estimates for the model in Eq. (1) fitted on daily observations from January 2015 to June 2017.
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). 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Hence, Bitcoin is an independent digital currency, not subject to the control of central authorities and without inflation; furthermore, transactions in the network are pseudonymous and irreversible.
\nBitcoin and the underlying blockchain technology have gained much attention in the last few years. Research on Bitcoin often deals with cybersecurity and legitimacy issues such as the analysis of double spending possibilities and other cyber-threats; recently, high returns and volatility have attracted research toward the analysis of Bitcoin price efficiency as well as its dynamics (see, among others, [2, 3, 4]). Moreover, many contributions claim that Bitcoin price is driven by attention or sentiment about the Bitcoin system itself; see [5, 6, 7, 8]. Possible driving factors for the sentiment about the Bitcoin system are the volume of Google searches or Wikipedia requests as in [5], or more traditional indicators as the number or volume of transactions, as suggested in [6]. In [9], the author suggests a time series model in order to identify the dynamic relation between speculation activity and price.
\nIn this chapter, after having introduced the basic concepts underlying Bitcoin, we sum up and describe to a broader audience the recent outcomes of the research reported in [10], by avoiding unnecessary technicalities. Some new insights are also given by looking at possible extensions in order to take into account the presence of bubble effects or the special feature of Bitcoin being traded in different online platforms (exchanges) that will be further investigated in our future research.
\nWe recall that Bitcoin was first introduced as an electronic payment system between peers by Satoshi Nakamoto (pseudonym) in [1]. Opposite to traditional transactions, which are based on the trust in financial intermediaries, this system relies on the network, on the fixed rules and on cryptography. Bitcoins can be purchased on appropriate websites that allow to change usual currencies in the cryptocurrency.
\nThe Bitcoin network has several attractive properties for its users:
No central bank authority for money supply and no regulator;
Transactions are 24/7 and without any country border;
Transaction cost are almost negligible with respect to traded amount;
Transaction are anonymous;
The security of each transaction is guaranteed by cryptography and digital signature;
The security of the whole network is guaranteed by construction unless more than 50% of the network nodes agree on a deceptive action.
As a digital payment system, Bitcoins may be used to pay for several online services and goods. Special applications have been designed for smartphones and tablets for transactions in Bitcoins and some ATMs have appeared all over the world (see Coin ATM radar) to change traditional currencies in Bitcoins. Accepting Bitcoins as a payment method is also related to an advertisement opportunity for companies. However, the high returns achieved in the last few years have transformed Bitcoin in a speculative asset affecting its use as a form of payment.
\nThe Bitcoin system has been subject to many cracks but has proven to be very resilient as the value of the cryptocurrency was able to rise again after all the falls. Nevertheless, at the time of writing, Bitcoin was experiencing a fall in its exchange rate with main fiat currencies.
\nTwo of the main crackdowns were China enforcement in December 2013 and Mt. Gox bankruptcy in February 2014.
\nBesides technical and regulation issues, the Bitcoin system also faces reputational concerns.
\nIn fact, the ambiguity of anonymous transactions has blamed the network of allowing several criminal activities such as buying illegal goods, money laundering or the financing of terrorism actions. As a representative example, we recall that The Silk Road was a website that started selling narcotics and illegal drugs in 2011, payable in Bitcoins. The website was finally shutdown by 2013 and the owner was arrested and sentenced to life in prison. Again, anonymous transactions make it possible to use huge quantities of money, exchanged in Bitcoins, without declaring its origin, hence allowing for possible money laundering. However, according to a research performed by the UK government, the highest score related to money laundering is still cash, followed by the bank, accountancy and legal service providers (see
It is worth noticing that while counterparties are represented by secret addresses and are anonymous, all transactions are recorded and might be traced. Investigation is hence favored by this feature of the network.
\nDespite the flaws in the system, Bitcoin has achieved a notwithstanding rise in recent years.
\nIn Figure 1, we report Bitcoin price and returns from January 2012 to December 2017 (source
Bitcoin price (top) and returns (bottom) from January 2012 to December 2017.
The model we suggest in what follows is motivated by findings in [5, 6, 8, 11] where it is showed that Bitcoin price is related to investors’ attention measured by the trading volume and/or the number of searches in engines such as Google and Wikipedia. Bitcoin is treated as a financial stock as suggested in [12] and the suggested model may be applied in principle to other assets that are proven to depend on market attention.
\nConsider a probability space \n
Let us denote the Bitcoin price process as \n
where \n
It is well known that the above dynamics for the attention factor is a geometric Brownian motion, the solution of which is given by \n
We collect in this subsection the properties of the logarithmic returns obtained by the price process defined in Eq. (1).
\nConsider the discrete process \n
Theorem 2.1. The random vector \n
Proof. In order to prove the theorem it suffices to remind that, for i = 1, 2, …, n, the random variable \n
As for the unconditional distribution, it is easy to obtain, for \n
where \n
Proposition 2.2. The joint probability density of the vector \n
where \n
The proof follows from Bayes’ rule and application of Theorem 2.1.
\nIt is worth to remark that the probability density \n
Precisely, we have that \n
We apply the outcomes above in order to estimate model parameters according to the maximum-likelihood method (see for example [14, 15]) where the likelihood is approximated by applying the Levy approximation [13].
\nParameter estimates are obtained as
\nwhere
The first step in our procedure is to identify possible measures of investors’ attention. As already mentioned in the introduction, we consider the total trading volume on Bitcoin available from
The trading volume of exchange is a classical measure of the attractiveness of a traded asset for an investor; besides, in [16], the authors find evidence that the latter captures the attention of retail/uniformed investors.
\nWe consider daily data from January 1, 2015, to June 30, 2017, for the total volume and the SVI Index. As for the daily value of the Bitcoin, we have considered the average mean across main exchanges represented by the Index in
In Table 1, the outcomes for parameter estimates, obtained by maximizing the approximate likelihood given the observed time series, are summed up.
\n\n | \n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|
A = Vol | \n0.9571 | \n1.1346 | \n0.0218 | \n0.0829 | \n
A = SVI | \n1.3584 | \n1.0687 | \n0.0743 | \n0.1559 | \n
Parameter estimates for the model in Eq. (1) fitted on daily observations from January 2015 to June 2017.
In this section, we show how to characterize the price of European call options on Bitcoins in the underlying market model. Let us fix a finite time horizon \n
where \n
Lemma 3.1. Every equivalent martingale measure \n
where \n
The proof can be deduced from that of Lemma 1.4 in [10], where they also account for a possible delay between the attention factor and its effect on Bitcoin prices trend. The process \n
is an \n
Equivalently, we can write the discounted Bitcoin price process \n
Clearly, under the minimal martingale measure \n
where \n
Remark 3.2. Note that, under any equivalent martingale measure that keeps the drift of the attention factor dynamics linear in \n
Now, we compute the fair price of a Bitcoin European call option via the risk-neutral evaluation approach, so it can be expressed as expected value of the terminal payoff under the selected pricing measure, that is, the minimal martingale measure. Let CT = (ST − K)+ be the \n
where
\nand \n
Here, \n
The following result provides the risk-neutral price of the option under the minimal martingale measure \n
Proposition 3.3. The risk-neutral price \n
where the function \n
Hence, the resulting risk-neutral pricing formula when evaluated in \n
In order to appreciate the performance of the pricing formula in Eq. (19), we compute model prices for option traded on the online platform
T-K | \nMarket bid | \nMarket ask | \nModel volume | \nModel Google SVI | \nBenchmark BS | \n
---|---|---|---|---|---|
Aug-2200 | \n0.1662 | \n0.2318 | \n0.2029 | \n0.2282 | \n0.1967 | \n
Aug-2300 | \n0.1670 | \n0.2072 | \n0.1737 | \n0.2032 | \n0.1655 | \n
Aug-2400 | \n0.1390 | \n0.1845 | \n0.1469 | \n0.1802 | \n0.1369 | \n
Aug-2500 | \n0.1142 | \n0.1638 | \n0.1228 | \n0.1591 | \n0.1112 | \n
Aug-2600 | \n0.0922 | \n0.1376 | \n0.1014 | \n0.1399 | \n0.0887 | \n
Aug-2700 | \n0.0749 | \n0.1202 | \n0.0828 | \n0.1226 | \n0.0695 | \n
Aug-2800 | \n0.0572 | \n0.1047 | \n0.0684 | \n0.107 | \n0.0535 | \n
Aug-2900 | \n0.0442 | \n0.0983 | \n0.0549 | \n0.0931 | \n0.0405 | \n
Sept-2200 | \n0.1991 | \n0.2648 | \n0.2546 | \n0.3204 | \n0.2173 | \n
Sept-2300 | \n0.1766 | \n0.2432 | \n0.2321 | \n0.3019 | \n0.1906 | \n
Sept-2400 | \n0.1890 | \n0.2230 | \n0.2113 | \n0.2844 | \n0.1662 | \n
Sept-2500 | \n0.1375 | \n0.2042 | \n0.1919 | \n0.2679 | \n0.1439 | \n
Sept-2600 | \n0.1207 | \n0.1828 | \n0.1741 | \n0.2523 | \n0.1239 | \n
Sept-2700 | \n0.1120 | \n0.1668 | \n0.1576 | \n0.2377 | \n0.1060 | \n
Sept-2800 | \n0.0953 | \n0.1504 | \n0.1463 | \n0.2239 | \n0.0903 | \n
Sept-2900 | \n0.0848 | \n0.1422 | \n0.1325 | \n0.2109 | \n0.0764 | \n
Motivated by empirical evidences (see for example [21, 22]), we discuss a generalization of the model introduced in Section 3.1, which is capable to describe speculative bubbles in Bitcoin markets.
\nPrecisely, we fix a finite time horizon \n
Without loss of generality, we assume that the interest rate is fixed and equal to zero. In this setting, the discounted Bitcoin price trend and the market attention factor dynamics are described by
\nwhere we have set \n
By simulating trajectories for the asset price \n
Simulated trajectories with n = 250 daily observations for the attention process (red) and the corresponding Bitcoin price dynamics for \n\nρ\n=\n0\n\n (black), \n\nρ\n=\n0.5\n\n(green), and \n\nρ\n=\n1\n\n (blue).
Indeed, we will show formally that the possibility of Bitcoin speculative bubbles is related to the sign of the correlation parameter \n
The mathematical theory of financial bubbles is developed, among others, in [23, 24, 25]. Precisely, we introduce the following definition from [23].
\nDefinition 4.1. The Bitcoin price process \n
The term strict \n
Recall that the absence of arbitrage opportunities is “essentially” equivalent to the existence of a probability measure \n
Remark 4.2. Note that stock bubbles arise if \n
Then, to exclude arbitrage opportunities from the market, we define the process \n
where \n
To ensure that \n
and we can consider the corresponding family of equivalent (local) martingale measures \n
where the \n
\n\n
Now, suppose that the risk perception process is zero, that is, \n
is a true \n
Proposition 4.3. In the model outlined in Eq. (24), the Bitcoin price process \n
The proof is based on the application of some of Sin’s results given in [27], where the existence of risk-neutral measures for the Hull-White stochastic volatility model [19] and for similar frameworks is determined by the possibility of explosion in finite time for solutions of certain auxiliary stochastic differential equations. Precisely, it is possible to show that the martingale property of the discounted stock price \n
Let us generalize the model introduced in Eq. (1) by assuming a possible delay \n
where \n
Analogous results as those in Section 2 can be derived by similar computations, and model parameters, for a fixed delay, can be estimated by means of the maximum likelihood method. In order to estimate the delay parameter, we maximize the profile likelihood as defined in [15]. Details of this procedure can be found in [10]. The estimation results of model in Eq. (27) on the same daily data considered in Section 2 are summed up in Table 3.
\n\n | \n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
A = Vol | \n1 day | \n0.4881 | \n1.0459 | \n0.0282 | \n0.0924 | \n
A = SVI | \n7 days | \n1.0964 | \n0.9946 | \n0.1005 | \n0.1885 | \n
Parameter estimates for model in Eq. (27) fitted on daily observations from January 2015 to June 2017.
In Figure 3, we plot simulated trajectories of the price process in Eq. (27) by letting the delay parameter vary.
\nSimulated trajectories of n = 250 daily observations of the attention factor (red) and the Bitcoin price according to model in Eq. (27) when the delay parameter is \n\nτ\n=\n1\n\n day (black) and \n\nτ\n=\n10\n\n days (blue).
The different delays result in a shift to the south-east between the faster and slower reacting trajectories; in the picture, this behavior is sharp since the other model parameters are kept constant. By looking at the picture, the idea to model the price of Bitcoin in different exchanges by the same model in Eq. (27) but allowing different parameters naturally arises.
\nIn particular, considering for instance two exchanges, we have
\nwhere \n
Note that within this model, prices for Bitcoin traded in different exchanges are perfectly correlated. Indeed, this is what happens in observed data; considering daily prices from January 2015 to June 2017 for Bitstamp, Kraken, Cex.io, Gdax, and The Rock exchanges we get cross-correlation values larger than 0.999.
\nWe fit model in Eq. (28) for the Bitstamp and Gdax exchanges on daily observations of Bitcoin price from January 2015 to June 2017 obtaining the outcomes reported in Table 4, when the attention is measured by the trading volume, and in Table 5, when attention is measured by the Google SVI index.
\nExchange | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
Bitstamp | \n1 | \n0.4994 | \n1.0461 | \n0.0281 | \n0.0896 | \n
Gdax | \n2 | \n0.4997 | \n1.0420 | \n0.0326 | \n0.1036 | \n
Model fitting with delay parameter: outcomes for Bitstamp and Gdax exchanges when attention is measured by the trading volume.
Exchange | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
Bitstamp | \n7 days | \n1.0934 | \n0.9946 | \n0.0992 | \n0.1782 | \n
Gdax | \n7 days | \n1.0964 | \n0.9946 | \n0.1160 | \n0.2087 | \n
Model fitting with delay parameter: outcomes for Bitstamp and Gdax exchanges when attention is measured by the SVI index.
It is evident from the outcomes in Table 4 that the model parameters are not significantly different while the delay might be quite different as if the reaction to the attention factor is faster for some exchanges and slower for others. On the contrary, when attention is measured by the Google SVI Index, the delay is unchanged, but the difference between estimated parameters for the price dynamics is nonnegligible.
\nBy analyzing the outcomes and considering the shift effect as depicted in Figure 3, it is tempting to conjecture that the faster reaction determines the leader exchanges and that the slower exchange will then follow. If we could forecast that the next day price of the slower exchange will reach the price today for the faster one, we could obtain a profit by suitably investing in the two exchanges. However, it is worth noticing that the estimation of the delay parameter is obtained by maximizing the likelihood over a whole time series and is a product of averaging so arbitrage cannot be achieved in a direct way.
\nNevertheless, in a multivariate setting as ours, the theory guarantees that arbitrage opportunities are ruled out if the market price of risk in the market is unique. Without entering technical details and assuming \n
It is evident that these values are not equal if we plug parameter estimates in Eq. (30); hence, arbitrage opportunities are not ruled out at least from a theoretical point of view. We will address this issue more precisely in future research.
\nIn this chapter, we have introduced a model in continuous time in order to describe the dynamics of Bitcoin price depending on an exogenous stochastic factor, which represents market attention on the Bitcoin system. Market attention is measured either by the total trading volume in Bitcoins or by means of the Google Search Volume Index, which, as suggested in [16], is a direct measure of the revealed attention for uniformed retail investors. More precisely, the attention factor affects directly the instantaneous mean and volatility of logarithmic returns; in addition, it may be also correlated with the price changes. An estimation procedure to fit the model to observed data is also suggested and, under the assumption of no correlation, a closed formula for standard European option prices on Bitcoin is provided.
\nBy applying outcomes within the mathematical theory of bubbles [23, 24, 25, 27], we are able to show that Bitcoin boosts in a bubble if and only if there is a positive correlation between changes in the price and in the attention factor. This finding is reasonable and claims that a stronger positive dependence between the two processes in Eq. (21) may result in an explosion of the price process.
\nFinally, we allow for a delay on the effect of market attention on the Bitcoin price, and, based on this generalized model, we introduce a multivariate setting for our model (Eq. (28)) in order to take into account the special feature of multiple exchanges where it is possible to trade in Bitcoins. Preliminary results indicate that arbitrage opportunities may arise in two exchanges that are characterized by different delays.
\nWe gratefully acknowledge Marco Patacca for having provided the routines in Matlab® to develop the empirical sections of the paper and for useful suggestions and comments. We also acknowledge funding from Fondazione Cassa di Risparmio di Perugia (Grant 2015:0459013) and Bank of Italy (Grant 407660/16).
\nThe authors declare no conflict of interest.
Gianna Figà-Talamanca gratefully acknowledges interesting discussions on the topic of this chapter with colleagues of the Department of Finance and Risk Engineering where she was visiting professor while preparing this research. The authors also wish to thank Stefano Bistarelli for having introduced them to the intriguing and worth to explore world of cryptocurrencies.
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