Olivier Scaillet, Adrien Treccani, Christopher Trevisan
We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps are frequent events and they cluster in time. The order flow imbalance and the preponderance of aggressive traders, as well as a widening of the bid-ask spread predict them. Jumps have short-term positive impact on market activity and illiquidity and induce a persistent change in the price.
Bitcoin has the largest share in the total capitalization of cryptocurrency markets currently reaching above 70 billion USD. In this work we focus on the price of Bitcoin in terms of standard currencies and their volatility over the last five years. The average day-to-day return throughout this period is 0.328%, amounting in exponential growth from 6 USD to over 4,000 USD per 1 BTC at present. Multi-scale analysis is performed from the level of the tick data, through the 5 min, 1 hour and 1 day scales. Distribution of trading volumes (1 sec, 1 min, 1 hour and 1 day) aggregated from the Kraken BTCEUR tick data is provided that shows the artifacts of algorithmic trading (selling transactions with volume peaks distributed at integer multiples of BTC unit). Arbitrage opportunities are studied using the EUR, USD and CNY currencies. Whereas the arbitrage spread for EUR-USD currency pair is found narrow at the order of a percent, at the 1 hour sampling period the arbitrage spread for USD-CNY (and similarly EUR-CNY) is found to be more substantial, reaching as high as above 5 percent on rare occasions. The volatility of BTC exchange rates is modeled using the day-to-day distribution of logarithmic return, and the Realized Volatility, sum of the squared logarithmic returns on 5-minute basis. In this work we demonstrate that the Heterogeneous Autoregressive model for Realized Volatility Andersen et al. (2007) applies reasonably well to the BTCUSD dataset. Finally, a feed-forward neural network with 2 hidden layers using 10-day moving window sampling daily return predictors is applied to estimate the next-day logarithmic return. The results show that such an artificial neural network prediction is capable of approximate capture of the actual log return distribution; more sophisticated methods, such as recurrent neural networks and LSTM (Long Short Term Memory) techniques from deep learning may be necessary for higher prediction accuracy.
Investor and media attention in Bitcoin has increased substantially in recently years, reflected by the incredible surge in news articles and considerable rise in the price of Bitcoin. Given the increased attention, there little is known about the behaviour of Bitcoin prices and therefore we add to the literature by studying price clustering. We find significant evidence of clustering at round numbers, with over 10% of prices ending with 00 decimals compared to other variations but there is no significant pattern of returns after the round number. We also support the negotiation hypothesis of Harris (1991) by showing that price and volume have a significant positive relationship with price clustering at whole numbers.
Mehmet Balcılar, Elie Bouri, Rangan Gupta, David Roubaud
Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns- except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.
In this research we have tried to identify the relationship between the exchange rate for bitcoin to the leading currencies such as Dollar, Euro, British Pound and Chinese Yuan and Polish zloty as well. We have applied ARMA and GARCH models to model and to analyze the conditional mean and variance. The appliance of GARCH models have identified some dependency in explanation conditional variance between bitcoin and US Dollar, Euro and Yuan, while ARMA analysis have shown no relations between bitcoin and other dependent variables.
Hermann Elendner, Simon Trimborn, Bobby Ong, Teik Ming Lee
Crypto-currencies have developed a vibrant market since bitcoin, the first crypto-currency, was created in 2009. We look at the properties of cryptocurrencies as financial assets in a broad cross-section. We discuss approaches of altcoins to generate value and their trading and information platforms. Then we investigate crypto-currencies as alternative investment assets, studying their returns and the co-movements of altcoin prices with bitcoin and against each other. We evaluate their addition to investors' portfolios and document they are indeed able to enhance the diversification of portfolios due to their little co-movements with established assets, as well as with each other. Furthermore, we evaluate pure portfolios of crypto-currencies: an equallyweighted one, a value-weighted one, and one based on the CRypto-currency IndeX (CRIX). The CRIX portfolio displays lower risk than any individual of the liquid crypto-currencies. We also document the changing characteristics of the crypto-currency market. Deepening liquidity is accompanied by a rise in market value, and a growing number of altcoins is contributing larger amounts to aggregate crypto-currency market capitalization.
Elie Bouri, Péter Molnár, Georges Azzi, David Roubaud · 5 authors
This paper uses a dynamic conditional correlation model to examine whether Bitcoin can act as a hedge and safe haven for major world stock indices, bonds, oil, gold, the general commodity index and the US dollar index. Daily and weekly data span from July 2011 to December 2015. Overall, the empirical results indicate that Bitcoin is a poor hedge and is suitable for diversification purposes only. However, Bitcoin can only serve as a strong safe haven against weekly extreme down movements in Asian stocks. We also show that Bitcoin hedging and safe haven properties vary between 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 behaviour of the cryptocurrency Bitcoin.
Although financial experts have often criticized Bitcoin for being too volatile as an asset and an independent electronic currency, the volatility of Bitcoin has declined at a rapid pace since January 2015. This study addresses if Bitcoin enters a new phase. Many extensions of GARCH have been carried out to adequately estimate Bitcoin price dynamics. Our results suggest that despite maintaining a moderate volatility, Bitcoin remains typically reactive to negative rather than positive news. Bitcoin market is still, therefore, far from being mature.
Bitcoin is a digital currency that has gained significant traction as an economic instrument. Despite its rise, it has received little attention from the scholarly community. This study is one of the first studies to examine Bitcoin’s use as a complement to emerging markets currencies; more specifically, I analyze the value and volatility of Bitcoin relative to emerging market currencies and explore ways in which Bitcoin can complement emerging market currencies. The results suggest that Bitcoin has characteristics that make it well-suited to work as a complement to emerging market currencies and that there are ways to minimize Bitcoin’s risks.
Mehmet Balcılar, Elie Bouri, Rangan Gupta, David Roubaud
The objective of this paper is to employ the recently proposed nonparametric causality-in-quantiles test to analyse the predictability of returns and volatility of Bitcoin over the daily period of 19th December, 2011 to 25th April, 2016, based on information provided by traded volume. The causality-in-quantile approach allows us to test for not only causality-in-mean, but also causality that may exist in the tails of the joint distribution of the variables. In addition, we are also able to investigate causality-in-variance (volatility spillovers) when causality in the conditional-mean may not exist, yet higher order interdependencies might emerge. We motivate our analysis by employing tests for nonlinearity. These tests detect nonlinearity, as well as the existence of structural breaks in the Bitcoin returns, and in its relationship with volume, implying that the Granger causality tests based on a linear framework is likely to suffer from misspecification. Unlike the result of no predictability obtained under the misspecified linear set-up, our nonparametric causality-in-quantiles test indicated that volume predicts returns over the quantile range of 0.25 to 0.75, i.e., barring in the bear and bull regimes of the Bitcoin market. However, we could not detect any evidence of predictability emanating from volume for the volatility of Bitcoin returns at any point of the conditional distribution. Our results highlight the importance of our detecting and modeling nonlinearity when analyzing causal relationships between volume and return in the Bitcoin market.
Elie Bouri, Luis A. Gil‐Alana, Rangan Gupta, David Roubaud
Abstract Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors.
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 paper augments the current research suggesting the less rational factors like attractiveness of Bitcoin and speculative investments to be influential for excessive volatility. In particular, it examines the sentiment as a driver of Bitcoin volatility. The paper contributes with economic rationale about a link between sentiment and Bitcoin. Further, the authors propose a unique decomposition of Bitcoin price to rational and less rational components. The paper tests this theoretical prediction with unique sentiment intraday data in the period of 12/12/2013 – 12/31/2015. The findings of the paper show the marginal presence of sentiment during the overall studied period. However, the explanato- ry power of sentiment significantly increases during the period of excessive volatility, especially dur- ing the bubble period at the end of the year 2013 and beginning of 2014. Moreover, the findings show that positive sentiment is more influential for Bitcoin excessive volatility.
This study gave an overview of the development of Bitcoin and several key economic aspects of its network. Using Johansen’s method, it explored the co-integration relationship among Bitcoin price, local currency exchange rate and stock market index in selected emerging economies. It investigated Bitcoin price’s impact on these local currency exchange rates and their stock market indices respectively using the Vector Error Correction Model (VECM). It further demonstrated these impacts using the Impulse Response Function. The findings in this study indicate that Bitcoin price co-integrates with the local currencies’ exchange rates, with Mexico and Russia being the exception. Despite different short-term responses, in the long term, local currency tends to strengthen against dollar given a decrease of Bitcoin price in the local currency, or an increase of Bitcoin price in dollar. In addition, stock market index’s response to a positive shock on Bitcoin price varies across economies, some saw stick rally while others selloff.