We provide an extreme value analysis of the returns of Bitcoin. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate extreme value analysis. Those properties will be compared to the traditional exchange rates of the G10 currencies versus the US dollar. For investors - especially institutional ones - an understanding of the risk characteristics is of utmost importance. So for bitcoin to become a mainstream investable asset class, studying these properties is necessary. Our findings show that the bitcoin return distribution not only exhibits higher volatility than traditional G10 currencies, but also stronger non-normal characteristics and heavier tails. This has implications for risk management, financial engineering (such as bitcoin derivatives) - both from an investor's as well as from a regulator's point of view. To our knowledge, this is the first detailed study looking at the extreme value behaviour of the cryptocurrency Bitcoin.
Bitcoin is a virtual money and a new payment system which is not regulated by a central authority.Bitcoin became popular quickly and gained the ability of affecting the real economy.Being used extensively and seen as an investment tool, Bitcoin created its own market, users and investors.This study aims to shed light on Bitcoin market.To understand what Bitcoin is, the history of Bitcoin was summarized firstly and the Bitcoin system and how the protocol works was explained.Then Efficiency, Liquidity and Volatility of the Bitcoin Markets were analyzed.We concluded that the pricing of Bitcoin is too complicated; and the Bitcoin market is still vulnerable to many risks and speculation.
This dissertation is a compilation of three papers that investigate the role of optimal contracting in a delegated portfolio management setting. While the study of optimal contracts in classical principal-agent setup has been extensively studied, relatively few have been studied in the context of delegated portfolio management in finance. And even delegated portfolio management papers in finance, there are still several open questions and unresolved issues that are beyond the scope of a standard principal-agent problem. In Chapter 1, I study a continuous-time principal-agent problem with drift and stochastic volatility control. While the problem with drift-only control by an agent has been extensively studied recently, very few existing papers allow an agent to endogenously influence volatility. Endogenous volatility control is particularly important in delegated portfolio management settings as volatility is one of the defining aspects of modern financial portfolio management. In Chapter 2, I study a model that encompasses dynamic agency, delegated portfolio management and asset pricing. Traditionally, the fields of ``asset pricing'' and ``corporate finance'' are studied independently of each other. However, as the modern portfolio management industry blooms in size and influence, the role of the portfolio manager and the contracts that are extended to them arguably has a role in the securities that they invest in, and hence in equilibrium, the asset pricing implications of the market overall. This paper is an attempt to bridge ``asset pricing'' and ``corporate finance'' (specifically interpreted to mean delegated portfolio management contracting) into one. In Chapter 3, I study whether a principal investor is better off delegating most of his money to a single portfolio manager (centralized delegation), as opposed to multiple portfolio managers (decentralized delegation), especially when there is the possible presence of moral hazard. With the size of the hedge fund industry and growing empirical support that moral hazard is a growing risk among hedge fund managers, it becomes imperative to understand when an investor decides to delegate his money, should it be delegated in a more centralized or decentralized fashion.
Tema ovog rada su kriptovalute. Budući da većina ljudi nije pravodobno upoznata s ovom temom, ovaj rad prikazuje i opisuje kriptovalute te način na koji se upotrjebljuju u svakodnevnom životu. Kriptovalute (eng. cryptocurrency) digitalne su valute dizajnirane kao sredstvo razmjene. Poznate su po tome što su državne agencije i banke isključene iz procesa razmjene. Kriptovalute omogućuju jednostavnu, jeftinu i brzu transakciju na području cijeloga svijeta. Trenutno najisplativije kriptovalute su Bitcoin i Ethereum, a u radu je opisana njihova korisnost, prednosti i mane. Budući da se Bitcoinu predviđa uspješna budućnost i sve je prisutniji i prihvatljiviji na tržištu, u radu su navedeni primjeri iz Hrvatske koji to potvrđuju. Sve veći broj poduzetnika odlučuje se za uvođenje kriptovaluta. U primjerima je obuhvaćen širok spektar djelatnosti, od frizerskih usluga, preko raznih tvrtki koji se bave prodajom računalne opreme, ugostiteljskih usluga preko mogućnosti brzog i lakog podizana gotovine na kripto bankomatima pa sve do plaćanja komunalnih usluga, pa čak i humanitarno djelovanje. Mnogi smatraju da su kriptovalute samo sinonim za prijevare i pranje novca, no programeri tvrde da su kriptovalute samo jedna vrsta tehnologije, alat koji sam po sebi ne može biti ni dobar ni loš, ovisno o tome za što se koristi. Autor ovoga rada proveo je istraživanje o tome kako se može besplatno započeti trgovanje kriptovalutama te je anketom ispitao stavove ispitanika o implementaciji kriptovaluta u društvu.
Blockchain or, more generally, distributed ledger technology allows to create a decentralized digital ledger of transactions and to share it among a network of computers. In this paper, we argue that the implementation of this technology in financial markets offers investors new options for managing the degree of transparency of their holdings and their trading intentions. We first identify two intrinsic features of a distributed ledger that impact the availability of these new options, namely the mapping between identifiers and end-investors and the degree of transparency of the ledger, and we then examine how the implementation design of these critical features affects investor trading behavior, trading costs, and investor welfare, in a theoretical model of intermediated and peer-to-peer trading. The most transparent setting yields the highest investor welfare, despite the risk of front-running. In the absence of full transparency, welfare is weakly higher if investors are allowed to split their holdings among many identifiers.
Cryptocurrencies became popular with the emergence of Bitcoin and have shown an unprecedented growth over the last few years. As of November 2016, more than 720 cryptocurrencies exist, with Bitcoin still being the most popular one. We provide both a statistical analysis as well as an extreme value analysis of the returns of the most important cryptocurrencies. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate and multivariate extreme value analysis. The tail dependence of cryptocurrencies is investigated (using both empirical and Gaussian copulas). For investors—especially institutional ones—as well as regulators, an understanding of the risk and tail characteristics are of utmost importance. For cryptocurrencies to become a mainstream investable asset class, studying these properties is necessary. Our findings show that cryptocurrencies exhibit strong non-normal characteristics, large tail dependencies, depending on the particular cryptocurrencies and heavy tails. Statistical similarities can be observed for cryptocurrencies that share the same underlying technology. This has implications for risk management, financial engineering (such as derivatives on cryptocurrencies)—both from an investor’s as well as from a regulator’s point of view. To our knowledge, this is the first detailed study looking at the extreme value behaviour of cryptocurrencies, their correlations and tail dependencies as well as their statistical properties.
Bitcoin is a relatively new and attractive asset. It is used for peer-to-peer transactions and is built upon an interesting system called the Blockchain which allows for fast and secure transactions between users. Although Bitcoin and its underlying infrastructure show a lot of potential for growth and innovation, many users of the so-called “cryptocurrency” are wary of holding it instead of other currencies such as the U.S. dollar because of the high volatility exhibited in the price of Bitcoin. The goal of this paper is to examine the use of theoretically priced put options, “protective puts”, to hedge against price decreases that Bitcoin may experience. The user of this protective put strategy is considered to be an investor with an optimistic view on the price of Bitcoin and wants to own some, but is uncomfortable with the potential for substantial losses due to price decreases. In implementing the protective put strategy, the price of Bitcoin that the investor owns has a floor at the strike price of the options purchased to hedge the risk of price decreases. If the price increases enough, then the options are sold, and those with the new strike price are bought to lock in a higher protected price for the investor. The investor’s goals are to reduce the risk of losses by owning Bitcoin while its price decreases and to lessen the volatility that his portfolio experiences at the expense of the cost of purchased options eating into potential profits. Upon analysis of both historical and simulated data, utilizing protective puts as a hedging mechanism against decreases in the price of Bitcoin has proven effective at reducing expected volatility and limiting losses. The use of the strategy, when analyzed across different simulated market environments, allows for the capture of price increases while stopping excessive losses. Proportional to an unhedged Bitcoin portfolio, the proposed approach reduces volatility more than it reduces expected percentage gains. Upon analysis, the expected profit is slightly less than 28% lower at around 8% hedged from 11% unhedged. However, the standard deviation of percentages of profits or losses is 53% lower, having decreased from 27.8% to about 13.1%. Bitcoin: A Brief Introduction History, Mechanics and Use of Bitcoin In October of 2008, a mysterious person or group known as Satoshi Nakamoto released a paper detailing a peer-to-peer electronic cash system that would come to be known as Bitcoin (The New York Times 2013). The software behind Bitcoin, called the Blockchain, was innovative because the code allowed transactions to be authenticated and processed without a central bank or government. For years, Bitcoin grew nearly unbeknownst to the mainstream public as it was used mostly to facilitate black market transactions. In April 2013 a price surge in the value of Bitcoin caused the total value of all bitcoins to surpass one billion US dollars–this milestone triggered a media frenzy. Over the past 30 months, the value of a single Bitcoin has continued to be volatile, reaching a peak of over $1,242 US dollars before descending to the current price of around $430 per bitcoin (Coindesk 2015). During this period Bitcoin has been adopted as an accepted form of payment, and notable companies have implemented payments using it including Microsoft and Overstock.com (BitcoinValues 2015). Governments have demonstrated an interest in understanding and regulating Bitcoin. For example, former US Federal Reserve Chairman Ben Bernanke has said that Bitcoin “may hold long-term promise, particularly if the innovations to promote a faster, more secure, and more efficient payment system” (Tracy 2013). Bernanke highlights three key advantages that Bitcoin holds over traditional currencies. Bitcoin transactions theoretically are more secure, faster, and effectively free to facilitate. Not all governments have welcomed the rise of Bitcoin, China’s central bank has prohibited any financial institutions from handling bitcoin transactions (Wilhelm 2014). Bitcoin has attracted speculative investors who seek to capitalize on its volatility and perceived upside. As Bitcoin has become more established as a potential asset, companies have recently begun facilitating the development of Bitcoin options exchanges. Issues Holding Bitcoin Back from Major Adoption Despite the potential that Bitcoin has, many issues hold it back from large-scale adoption. Legality and security are the first two problems that potential investors run into when considering Bitcoin as an investment. These two issues are very gray at the moment, as some countries consider Bitcoin to be a currency while others consider it property and potential gains or losses are taxed differently. Many investors can buy Bitcoin online and hold it in a 3rd party wallet, but most do not have the deep understanding of computer science and cryptography that underlies Bitcoin. Additionally, news of stolen Bitcoin and unknowns about flaws or holes in storage mechanisms can also scare away buyers (Onies, Olayinka, Daniele). Despite these issues, Bitcoin has seen adoption because of its use in payments and also because of the potential that its Blockchain architecture holds for future development. Finally, should an optimistic or informed buyer decide to purchase bitcoins, they should expect the price of the cryptocurrency to be highly volatile. One bitcoin is one bitcoin; however, most people operate under a system where their base currency denomination is not in Bitcoin. The volatility shown in exchange rates is often due to news about acceptance or governmental regulation, in addition to market factors and broader adoption of the technology. The result of this volatility is that should an investor want to redeem his bitcoins for another currency, he may receive much more or much less than was originally spent to acquire them. Bitcoin as an Investment Because of the extraordinary potential that Bitcoin and the Blockchain have shown in recent years, there has been demand for the digital currency as an alternative investment. While reasons for owning it may differ, ranging from holding a different currency, the potential for capital appreciation or to be part of the future, the desire is there. However, there are few people willing to take on the risk of owning bitcoins when the price of the asset is so volatile concerning its exchange rate into US dollars. The Bitcoin market is still nascent and as such proper hedging methods have not been developed yet, requiring investors to accept the risk present in the market. Bitcoin adoption may be much higher in the future if people can have more control over the financial outcomes of their investment through hedging mechanisms (Prior 2015). Hedging a Historical Bitcoin Portfolio Methodology and Goals To test hedging the risk of a buy-and-hold Bitcoin portfolio, the decision was made to use the simple, yet often effective strategy of buying protective put options (CBOE). To evaluate how well the strategy would have worked, the last six months of daily price data for Bitcoin as a test sample. For the put options, European-style options were used, and the strike prices were set at $25.00 intervals. The expiry was placed to be at the end of the six-month period tested. Ten put options were bought (each covering 100 bitcoin) on the first day, as well as 1,000 bitcoins and progress of the hedge was tracked over the six-month period. The purchase of the put options is assumed to have been funded from a pool of cash which can be accessed for the cost of options and it is not tracked separately from the rest of the portfolio, although gain and loss from sales and purchases of puts are. To capture the upside of the investment in Bitcoin, if the price moved high enough to warrant the purchase of a put option at a higher strike price, it was done so. In doing this, the old put was sold to regain some of the cash spent on it because it is no longer required for the hedge. If the price of Bitcoin dropped below the strike price of the options, the price was locked-in at the strike price. The options were held until expiry since they are European-style and cannot be exercised beforehand. This allowed the price of Bitcoin to move until the expiry of the hedge and to possibly not require the use of the put by the time the option’s expiry date arrived. The goal of implementing such a hedging strategy is to reduce the volatility the Bitcoin portfolio experiences and to limit losses, while not sacrificing a majority of upside potential. Due to the high historical volatility of bitcoins, it was sought to determine whether a hedging strategy involving protective put options will limit losses while still allowing for significant upside for the long-term investor who is optimistic about Bitcoin prices and adoption. Simulating Options Prices In order to price the options for the protective put strategy, the Black-Scholes equation was used (Black, Scholes 1973). In order to compute accurate prices for the puts, the following inputs were used: · Risk-free interest rate of 1.00% · Daily price of a bitcoin · Strike price in increments of $25.00 · Days remaining until option expiry · Dividend of zero · Historical volatility of Bitcoin prices, calculated to be 53.40% Put prices were calculated using these inputs by a Visual Basic for Applications (VBA) script inside an Excel sheet, as well as in a column-based format for consistency and compatibility with Palisade Corporation’s Excel add-in for simulation, @Risk. To calculate the volatility to use in the equation, the historical percentage of Bitcoin price changes over the six-month period the experiment was run on was used. To get the yearly volatility, the standard deviation of those values was multiplied by , because Bitcoin trades every day of the year. Assumptions Made During Model Development To streamline model development and simplify the analysis, some theoretical assumptions were made. One major assumption is that there are no transaction costs. Many Bitcoin exchanges charge a fee for placing trades, typically 0.25% of a transaction’s face value Coinbase. Additionally, Bitcoin options exchanges are not up and running yet, so transaction costs for options were omitted as well due to a lack of data and desire for simplicity in determining the efficacy of the protective put hedging strategy. Bitcoin, being considered an alternative currency, is purchased by exchanging another form of currency for it. In the historical scenario and simulations run, Bitcoin is bought with U.S. dollars; however, no currency exchange fees are incorporated, nor are bid or ask spreads. Some exchanges charge a final fee when Bitcoin is converted to another currency or withdrawn from the account. These costs were not built into the model since the premise of this work is based on an optimistic Bitcoin investor who has no desire to withdraw any form of currency from his accounts. Lastly, the options market for U.S. equities will sometimes exhibit mismatched prices or market making spreads. This model assumes that option prices will not be affected by these factors and that they will trade at their fair value as determined by the Black-Scholes equation with inputs specified above. There will be no market impact as liquidity is assumed to be infinite, and there will be no transaction costs per contract or order. Analysis of Historical Results Performance of Puts Over Six-Month Test Period Over the six-month test period evaluated, the protective put strategy worked well. The value of Bitcoin during the evaluation period was relatively volatile, which provided a good scenario for the procedure to be tested against. The price of a bitcoin over the six months chosen can be seen in Figure 1. The price path looks as though it follows a rough sine wave with a five-month wavelength and then spikes up during the sixth month. This study’s goals are to limit the downside of an investment in Bitcoin while retaining most of the upside and reducing the volatility of returns. Regarding accomplishing these, the protective puts worked as planned. During the six-month period, a few different trends seemed apparent based on the price path. During the upwards part of the wave in the price of a bitcoin, the portfolio increased in value, and the downward drag on value was the cost of upgrading puts to their next strike price. This impediment to the portfolio's value is to be expected, as a hedge can be defined as paying a price to reduce uncertainty. When the price went down below that of the strike price of the options owned, the lowest price for the portfolio was capped at the strike price of the options, multiplied by the number of bitcoin owned plus the cost of the options. In this section of time, the options did not expire, allowing the right to sell the bitcoins in the portfolio for a set price moving forward if the portfolio needed to be liquidated. The portfolio remained intact, and the price of bitcoin moved upwards again, allowing puts of an even higher price to be purchased and lock in a higher price for each bitcoin. On the last day, since the price of bitcoin was above the strike price of the puts owned, the puts expired worthless. The effects of the hedge can be seen in Figure 2, where the cost of the portfolio was locked in near the beginning, only increasing when puts were exchanged for others at a higher strike price. The value, however, increased over time and did not have the ability to fall much below the original cost of the portfolio in the worst case scenario of the price of Bitcoin falling through the strike price of the puts. In Figure 3, it can be seen that the volatility of the hedged portfolio is much lower, and the potential for loss was much lower. When compared to the unhedged portfolio’s profit and loss, it is clear that without hedging, selling any time from months three to five would have resulted in a loss, while the hedged portfolio would have allowed the capture of approximately a 20% gain. Reducing the volatility of the portfolio was another goal when using the protective put strategy. Using the historical volatility model to get the volatility for Bitcoin over the six-month period gave a 53.4% yearly volatility. Using the same metrics for the percent changes in the value of the portfolio comprised of Bitcoin and put options, the volatility resulted in a value of 39%. Evaluating solely the volatility of the 1,000 bitcoins owned in the portfolio gave the same result, offering a 39% yearly volatility. As such, the puts did effectively reduce portfolio volatility over the six-month period, while allowing the capture of upside and potentially limiting losses should the price of Bitcoin decreased over the time period examined. Simulation of Bitcoin Portfolio Using Random Walks Methodology To get a better idea of how the protective put strategy would work in different and potentially trending environments, simulated geometric random walks were used to analyze potential price paths of Bitcoin over a period of six months (Nau). 10,000 simulations were run for each random walk scenario and the portfolio and Bitcoin profits and losses in dollars and percentages were analyzed as outputs, as well as portfolio and Bitcoin volatility. Initially, a positive drift was used in the random walk, indicating a general uptrend in the simulated price of Bitcoin. To validate the strategy in multiple types of markets, random walks with negative and null drift were also used. Random Walk with Positive Drift In order to simulate the price of Bitcoin, a geometric random walk with positive drift was initially used. Upon analysis of the natural logarithm of historical price changes represented by , there was a positive drift of value 0.0032 based on the average daily price change percentage with volatility incorporated. Stated symbolically, , where is the drift value calculated and is the standard deviation of the daily changes, of value 0.02808. It is worth noting that the drift may be different based on different windows of time. The ones digits in the formula used represent the size of the time step for each day, as the equation progresses to price from daily. The random walk with positive drift is represented by the equation below, where represents a random perturbation chosen from the standard normal distribution. Unsurprisingly, with the positive drift, the simulated price of a bitcoin trended up, resulting in a mean ending portfolio value of $433,000 given a beginning investment of $228,230 (the cost of 1,000 bitcoin at the starting date of the historical scenario). After incorporating the cost of buying and selling options to hedge the simulated positions, the average profit was 34.2% over the simulated term of six months (Figure 4). Over the same period and using the same inputs, the percentage of profit on the unhedged portfolio was 77% (Figure 5). Comparing the profit and loss percentages to those achieved by the unhedged portfolio, the unhedged portfolio has a much greater return. This return is not surprising since the positive drift term in the random walk equation should result in favorable increases in Bitcoin’s price over the duration of the simulation, making a hedge less necessary in hindsight. A comparison of the standard deviations of the scenarios also reveals the effects of hedging. The standard deviation of the profit and loss percentage for the unhedged portfolio is 69% while the protective puts decreased that same profit and loss standard deviation to 34% for the hedged portfolio. It is clear that the protective put strategy allows an investor to invest with less risk by reducing standard deviations of simulated profit and loss percentages, but the strategy also sacrifices potential return, likely due to the high price of the options due to volatility. Random Walk with Zero Drift While the six-month period analyzed had shown the price of Bitcoin to exhibit an upward trend, this may not be the case in the future. For this reason, random walks of null and negative drift were analyzed as well in order build a better image of what may happen using the protective put strategy in environments of less favorable price paths. The equation used for the random walk with zero drift is: Simulating the random walks using a drift value of zero provided results representative of approximately an equal number of winning and losing price progressions. The average expected profit for the portfolio comprised of put options and bitcoins was slightly less than 1%, with a standard deviation of 16.7%. Examining the chart (Figure 6), it is clear that the protective put strategy had an effect on the outcomes, there are spikes in the histogram of outcomes representing the price floors that the put options created. This shows that the options did indeed limit the losses that could have been experienced in downward trends. The effect of the options can also be seen in the confidence interval. The hedged portfolio had a 90% chance of ending with an expected profit or loss between -19.3% and 33.4%, while the same confidence interval for the unhedged portfolio expected between a -49.8% and 72.8% profit or loss. The unhedged portfolio has a much smoother distribution of outcomes (Figure 7), representing the outcomes of wherever the price of Bitcoin ended during each simulation. The expected loss for the unhedged simulations was approximately zero percent, but with a much higher standard deviation of 39.12%. When comparing the unhedged portfolio to the hedged portfolio, the puts managed to reduce expected losses to about a maximum of 19.3%, while the unhedged portfolio had a 4.9% chance of losing more than half of the initial investment. Expected gains also decreased by over 50%, however this was an expected consequence of the hedging strategy’s option costs. Random Walk with Negative Drift After evaluating random walks with positive and zero drift, for completeness a random walk with negative drift was simulated. For the drift value, -0.0032 was used, since this is the negative counterpart to the positive value used in the random walk with positive drift. When the expected profit is negative, as is the case when the drift implies a downward trend in the simulated price of Bitcoin over time, the benefit of a protective put strategy became most apparent due to limiting losses consistently. The equation used for the random walk with negative drift is as follows: The hedged distribution of profit and loss percentages has some peaks likely representing different strike prices that the options prevented the portfolio from falling under (Figure 8). The mean of the distribution is -11.2%, which is likely attributable to the price of Bitcoin falling to the strike price of the option fairly quickly, but the portfolio was prohibited from dropping any further. The standard deviation of the hedged strategy’s results was a mere 7.4%. Because of these factors, the most lost was 19.3%. There was a 90% chance of earning anywhere from the minimum to 3.4%, although the strategy tended to lose approximately either 19%, 14% or 7% due to the strike prices. Also included in the losses are any increases in the simulated price path resulting in a higher strike price before the options were driven into effect. The unhedged distribution suffered significant losses in the negative drift scenario. Given the bias of the simulated price to trend down, the expected loss was 43.97%, with a standard deviation of 21.98% (Figure 9). In the simulated scenario, there was a 90% probability of losing between 3.9% and 71.9%, with the highest simulated loss being 85.64% although it could theoretically be 100%. When compared to the summary statistics of the hedged portfolio, it is clear that hedging is expected to reduce volatility and expected loss significantly in a situation of negative drift. Combined P&L of Random Walk Scenarios To verify the usefulness of the protective put hedging strategy in reducing expected losses and volatility over different price paths, a simulation was conducted with all three previous random walks. The results of the distributions of both the hedged and unhedged distributions were simulated 10,000 times. To establish the combined statistics of the hedged and unhedged portfolios, it was assumed that each outcome is equally For the of the hedged and unhedged simulated outcomes are (Figure the unhedged distribution is much more as is by its standard deviation of on a mean of simulated outcomes, the distribution of hedged results has a standard deviation of and a mean of another the combined hedged scenarios have a standard deviation that is lower than the combined unhedged The expected return of the hedged distribution is lower than that of the unhedged distribution. to the results of the simulation, the use of protective puts the volatility of the profit and loss distribution more than the expected return was indicating that the strategy worked for the of reducing the volatility of limiting downside and not much upside. on the analysis a hedging strategy utilizing protective put options reduces the volatility and losses of a Bitcoin portfolio Additionally, when the price of Bitcoin is simulated using geometric random protective puts reduce losses and volatility over scenarios of expected price and trends. If the value of Bitcoin then the cost of the puts options reduces the however, less so than the in volatility. Due to the strategy of puts options at greater strike options in the case of significant the bitcoin portfolio in a gain. unhedged Bitcoin portfolio would be to a in Bitcoin prices that gains or significant losses. the effects of losses, reducing volatility and still a return, the use of a hedging strategy such as the proposed protective put strategy may well be for optimistic Bitcoin In order to a model simulation and other to this to be to Figure path of Bitcoin for the six-month historical test period. Figure of the portfolio cost over the same six Figure of profits with and without hedging. 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Abstract This paper identifies and analyzes BitCoin features which may facilitate BitCoin to become a global currency, as well as characteristics which may impede the use of BitCoin as a medium of exchange, a unit of account and a store of value, and compares BitCoin with standard currencies with respect to the main functions of money. Among all analyzed BitCoin features, the extreme price volatility stands out most clearly compared to standard currencies. In order to understand the reasons for such extreme price volatility, we attempt to identify drivers of BitCoin price formation and estimate their importance econometrically. We apply time-series analytical mechanisms to daily data for the 2009–2014 period. Our estimation results suggest that BitCoin attractiveness indicators are the strongest drivers of BitCoin price followed by market forces. In contrast, macro-financial developments do not determine BitCoin price in the long-run. Our findings suggest that as long as BitCoin price will be mainly driven by speculative investments, BitCoin will not be able to compete with standard currencies.
The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading based social media sentiment has the potential to yield positive returns on investment.
To examine whether the recent price patterns and transaction costs of Bitcoin represent a general characteristic of decentralized virtual currencies, we analyze virtual currencies in online games that have been voluntarily managed by individuals since 1990s. We find that matured game currencies have price stability similar to that of small size equities or gold, and their transaction costs are sometimes lower than real currencies. Assuming that virtual currencies with a longer history can provide an estimate for Bitcoin's prospects, we project that Bitcoin will be less influenced by speculative trades and become a low cost alternative to real currencies.
Derivatives are financial instruments whose value depend on the values of other, more basic underlying variables. One of the most common and simple derivatives is a futures contract. This manuscript introduces the new kind of futures contracts called non-linear inverse futures contracts (inverse futures in short) firstly introduced by ICBIT trading platform specifically for Bitcoin trading and later picked up by major bitcoin trading platforms.
Crashes have fascinated and baffled many canny observers of financial markets. In the strict orthodoxy of the efficient market theory, crashes must be due to sudden changes of the fundamental valuation of assets. However, detailed empirical studies suggest that large price jumps cannot be explained by news and are the result of endogenous feedback loops. Although plausible, a clear-cut empirical evidence for such a scenario is still lacking. Here we show how crashes are conditioned by the market liquidity, for which we propose a new measure inspired by recent theories of market impact and based on readily available, public information. Our results open the possibility of a dynamical evaluation of liquidity risk and early warning signs of market instabilities, and could lead to a quantitative description of the mechanisms leading to market crashes.
Bitcoin (BTC) is a major virtual currency. Using weekly data over the 2010-2013 period, we analyze a BTC investment from the standpoint of a US investor with a diversified portfolio including both traditional assets (worldwide stocks, bonds, hard currencies) and alternative investments (commodities, hedge funds, real estate). Over the period under consideration, BTC investment had highly distinctive features, including exceptionally high average return and volatility. Its correlation with other assets was remarkably low. Spanning tests confirm that BTC investment offers significant diversification benefits. We show that the inclusion of even a small proportion of BTCs may dramatically improve the risk-return trade-off of well-diversified portfolios. Results should however be taken with caution as the data may reflect early-stage behavior that may not last in the medium or long run.
Bitcoin is a decentralized, open-source cryptocurrency used to make private, peer-to-peer transactions anywhere across the world. Although the individuals involved are (mostly) anonymous, every Bitcoin transaction is a matter of public record; anyone can view every Bitcoin transaction ever made. Following the methodology developed by Evans and Lyons (2002), this paper adapts and estimates a FX microstructure model that emphases order flow, the difference between buyer- and seller-initiated trading volume, to the Bitcoin market Using a data set consisting of all major currency transactions occurring on the Mt. Gox exchange, our results are quite similar to prior microfinance research on traditional currencies insofar order flow is a significant determinant of Bitcoin spot rates.
Bitcoin has two major roles: as currency and as financial asset. This paper attempts to address these roles: whether Bitcoin is a real currency, and what its financial features are. Using daily data of the exchange rates quoted from the world major Bitcoin dealer since the inception of Bitcoin and the spot market exchange rates, we calculate the triangle arbitrage asset price to decompose the features of this currency. The results suggest significant liquidity discount of Bitcoin and risk premium as a financial asset in terms of British Pound Sterling (2.46%) and Chinese Yuan (0.3%). There is idiosyncratic risk component associated with Bitcoin implied by the Granger causality tests. Bitcoin, as investment objectives instead of currency unit, is associated with excess risk and low returns. Such poor performance discourages investors to spend Bitcoin as currency and to pursue the arbitrage profit. Investors store and hold Bitcoin as fixed asset. In addition, both arbitrage stickiness and low Treynor ratio are persistent over time.
The present paper seeks to effectively address the following question: What Bitcoin looks like? To do so, we regress Bitcoin price on a number of variables (Bitcoin fundamentals recorded in the literature) by applying an ARDL Bounds Testing approach for daily data covering the period from December 2010 to June 2014. Our findings highlight the speculative nature of Bitcoin. We also provide insightful evidence that Bitcoin may be used for economic reasons but there is any sign of being a safe haven. By considering the Chinese trading bankruptcy and the closing of Road Silk by FBI, the contribution of users’ interest stills sharply dominant, indicating the robustness of our results.
This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed with a finite amount of crypto and/or fiat cash and issues buy and sell orders, according to her strategy and resources. The number of Bitcoins increases over time with a rate proportional to the real one, even if the mining process is not explicitly modelled. The model proposed is able to reproduce some of the real statistical properties of the price absolute returns observed in the Bitcoin real market. In particular, it is able to reproduce the autocorrelation of the absolute returns, and their cumulative distribution function. The simulator has been implemented using object-oriented technology, and could be considered a valid starting point to study and analyse the cryptocurrency market and its future evolutions.