Neil Gandal, JT Hamrick, Tyler Moore, Tali Oberman
To its proponents, the cryptocurrency Bitcoin offers the potential to disrupt payment systems and traditional currencies. It has also been subject to security breaches and wild price fluctuations. This paper identifies and analyzes the impact of suspicious trading activity on the Mt. Gox Bitcoin currency exchange, in which approximately 600, 000 bitcoins (BTC) valued at $188 million were fraudulently acquired. During both periods, the USD-BTC exchange rate rose by an average of four percent on days when suspicious trades took place, compared to a slight decline on days without suspicious activity. Based on rigorous analysis with extensive robustness checks, the paper demonstrates that the suspicious trading activity likely caused the unprecedented spike in the USD-BTC exchange rate in late 2013, when the rate jumped from around $150 to more than $1, 000 in two months.
Bitcoin, the most innovate digital currency as of now, created since 2008, even through experienced its ups and downs, still keeps drawing attentions to all parts of society. It relies on peer-to-peer network, achieved decentralization, anonymous and transparent. As the most representative digital currency, people curious to study how Bitcoin’ price changes in the past. In this paper, we use monthly data from 2011 to 2016 to build a VEC model to exam how economic factors such as Custom price index, US dollar index, Dow jones industry average, Federal Funds Rate and gold price influence Bitcoin price. From empirical analysis we find that all these variables do have a long-term influence. US dollar index is the biggest influence on Bitcoin price while gold price influence the least. From our result, we conclude that for now Bitcoin can be treated as a speculative asset, however, it is far from being a proper credit currency.
Bitcoin is attracting a steadily increasing interest since its first appearance in 2008. Bitcoin price forecasting would be of great practical interest given its role as a relatively new virtual “currency”. This presupposes the modeling and verification of some kind of relation, causal or not, connecting bitcoin price to other “established” factors of economic interest. Towards this goal, cross-correlation analysis is used in this work to investigate relations between bitcoin price and a set of other factors of economic interest. The years 2013 to 2015 are selected as the temporal basis of this research, because earlier bitcoin prices were practically zero. Results reveal a strong correlation between bitcoin and stock market indices or other economical factor values. SWOT analysis for bitcoin is carried out for the same period of time, based on cross-correlation as well as on existing research results. Bitcoin is seen to possess more benefits than risks, while its strong temporal correlations with other economic indices or prices constitute an opportunity to be further explored towards the goal of bitcoin price forecasting.
Elie Bouri, Naji Jalkh, Péter Molnár, David Roubaud
We study the relationship between Bitcoin and commodities by assessing the ability of Bitcoin to act as a diversifier, hedge, or safe haven against daily movements in commodities in general, and energy commodities in particular. We focus on energy commodities because energy, in the form of electricity, is an essential input in the Bitcoin production. For the entire period, results show that Bitcoin is a strong hedge and a safe-haven against movements in both commodity indices. We further examine whether that ability is also present for non-energy commodities and our analysis show insignificant results when energy commodities are excluded from the general commodity index. We also account for the December 2013 Bitcoin price crash and our results reveal that Bitcoin hedge and safe-haven properties against commodities and energy commodities are only present in the pre-crash period, whereas in the post-crash period Bitcoin is no more than a diversifier. In addition to uncovering the time-varying role of Bitcoin, we highlight the dissimilarity in the dynamic correlations between the extreme downward and extreme upward movements.
During times of extreme market turmoil, it is acknowledged that there is a tendency towards "flight to safety". A strong (weak) safe haven is defined as an asset that has a significant positive (negative) return in periods where another asset is in distress, while hedge has to be negatively correlated (uncorrelated) on average. The Bitcoin's surge alongside the aftermath of Trump's win in the 2016 U.S. presidential elections has strengthened its status as the modern safe haven. This paper uses a truly noise-assisted data analysis method, termed as Ensemble Empirical Mode Decomposition-based approach, to examine whether Bitcoin can act as a hedge and safe haven for U.S. stock price index. The results document that the Bitcoin's safe-haven property is time-varying and that it has primarily been a weak safe haven in the short term and the long-term. We also demonstrate that precious metals lost their safe haven properties over time as the correlation between gold/silver and U.S. stock price declines from short-to long-run horizons.
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 behavior of the cryptocurrency Bitcoin.
Cryptocurrencies have become increasingly popular since the introduction of bitcoin in 2009. In this paper, we identify factors associated with variations in cryptocurrencies' market values. In the past, researchers argued that the "buzz" surrounding cryptocurrencies in online media explained their price variations. But this observation obfuscates the notion that cryptocurrencies, unlike fiat currencies, are technologies entailing a true innovation potential. By using, for the first time, a unique measure of innovation potential, we find that the latter is in fact the most important factor associated with increases in cryptocurrency returns. By contrast, we find that the buzz surrounding cryptocurrencies is negatively associated with returns after controlling for a variety of factors, such as supply growth and liquidity. Also interesting is our finding that a cryptocurrency's association with fraudulent activity is not negatively associated with weekly returns-a result that further qualifies the media's influence on cryptocurrencies. Finally, we find that an increase in supply is positively associated with weekly returns. Taken together, our findings show that cryptocurrencies do not behave like traditional currencies or commodities-unlike what most prior research has assumed-and depict an industry that is much more mature, and much less speculative, than has been implied by previous accounts.
Hélder Sebastião, António Portugal Duarte, Gabriel Guerreiro
This paper analyses the price discovery process in the USD/Bitcoin market since the Mt.Gox bankruptcy until the aftermath of the hack attack on Bitfinex (01-Mar-2014 until 30-Nov-2016).The Geweke feedback measures, estimated pairwise using hourly returns, show that there is a positive relationship between the total feedback and market share, measured by trading volume, that most of the information is transmitted between exchanges within an hour, at least for the main four exchanges (Bitfinex, Bitstamp, BTC-e and ItBit), while lagged feedback runs mainly from the major exchange.Other minor exchanges seem to react to price information with some delay and are thus considered as merely satellite exchanges.Bitfinex stands out as the most important exchange in transmitting information to the market: the relative importance of the lagged feedback from Bitfinex to the market is 18.29% while the lagged feedback from the market to Bitfinex accounts only for 0.60% of the total feedback.The volatility in the major exchange in each pair is the main factor explaining the feedback measures, sustaining the claim that the information-based component of volatility increases with the relative dimension of the exchange.
The rapid advancement in encryption and network computing gave birth to new tools and products that have influenced the local and global economy alike. One recent and notable example is the emergence of virtual currencies, also known as cryptocurrencies or digital currencies. Virtual currencies, such as Bitcoin, introduced a fundamental transformation that affected the way goods, services, and assets are exchanged. As a result of its distributed ledgers based on blockchain, cryptocurrencies not only offer some unique advantages to the economy, investors, and consumers, but also pose considerable risks to users and challenges for regulators when fitting the new technology into the old legal framework. This paper attempts to model the volatility of bitcoin using 5 variants of the GARCH model namely: GARCH(1,1), EGARCH(1,1) IGARCH(1,1) TGARCH(1,1) and GJR-GARCH(1,1). Once the best model is selected, an OLS regression was ran on the volatility series to measure the day of the week the effect. The results indicate that the TGARCH (1,1) model best fits the volatility price for the data. Moreover, Sunday appears as the most significant day in the week. A nontechnical discussion of several aspects and features of virtual currencies and a glimpse at what the future may hold for these decentralized currencies is also presented.
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¡ªespecially institutional ones¡ªas 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)¡ªboth 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 peer to peer (p2p) payment cash system and an unregulated digital currency that is primarily designed and developed in 2008 without tender legal status. Bitcoin is so-called cryptocurrency because it uses the cryptographic function in order to secure the creation and transfer of money. During recent years, Bitcoin has been emerging as the well-known electronic currency and gaining popularity worldwide as well as caught the media attention in the area of volume trading. Therefore, Bitcoin will be a potential financial asset for investors due to its extraordinary returns. The purpose of this research is to find out how Bitcoin returns correlate with stock markets and to assess the risk that the electronic currency bears, to conclude whether Bitcoin is a favourable instrument for investors that want to diversify their portfolios. Therefore, daily data from 2013 to 2017 is used to measure correlations with major global stock markets and analyse in a regression to what extend Bitcoin is integrated into financial systems. In addition, Bitcoin’s risk has been measured by estimating value at risk, as well as the volatility and a regression analysis with explanatory variables has been performed to identify the driving factors of the unusually high volatility. Finally, the researchers constructed models to forecast expected returns to identify whether Bitcoin is rather a short or long term instrument. The researchers came to the conclusion that Bitcoin is a favourable instrument to diversify a portfolio as it correlates negatively with most of the analysed stock market indices and the research result showed that Bitcoin is not yet integrated into financial systems. It has however been paid attention to the new types of risk and the questionable image the electronic currency has as it is often used to support criminal activities. The fact that no authority, clearing house or central bank's involvement is present, creates uncertainty for many investors.
Bitcoin volatility is known to be high, as is shown by comparing Bitcoin volatility to several currencies and to assets like stock, gold etc. This work attempts to extend this work by comparing Bitcoin volatility to volatility of currencies of least developed countries and other cryptocurrencies. Exchange rate and return data drawn from Bloomberg and covering March 2014 to March 2017 was analysed. It was found that Bitcoin volatility is still considerably higher than volatilities of currencies of least developed countries. Only five currencies were more volatile for more than 10% of the time span analysed.