Purpose The purpose of this article is to provide some insights on the true nature of bitcoin and to study empirically its performance by using robust models, widely used in the academic literature. Previous studies assess performance with simple measures such as the Sharpe ratio. Such measures are insufficient because they do not take into account the bitcoin’s specificities, such as the possibilities to diversify risk. Design/methodology/approach The authors use quantitative methodologies to assess the performance of financial assets. Performance is defined as a risk-adjusted return. The authors use regression analysis and measure bitcoin’s performance as the constant term ( α ) of the projection of its returns on the returns of relevant factors of risk. Findings Bitcoin has low correlation with the market index and with factor-mimicking portfolios, which indicates opportunities to diversify risk. The performance of bitcoin ( α ) is positive and significant; this result is robust across period and world region specifications. Research limitations/implications The true nature of bitcoin is subject of debate and needs further research. Furthermore, other factors should be considered in analysing the bitcoin’s performance, such as those related to investors’ behaviour or political risk. Practical implications The empirical results obtained in this paper may be used by professional portfolio managers to diversify risk and to enhance their portfolio’s performance. Originality/value This paper adds to the literature by arguing that bitcoin has the nature of common stock, and therefore, its performance has to be assessed with models that are relevant for this type of securities. This paper is the first using performance models that adjust returns for relevant sources of risk.
Alessandra Cretarola, Gianna Figà‐Talamanca, Marco Patacca
In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.
Bitcoin is traded on exchanges which use an open limit order book. This paper investigates the microstructure of various bitcoin markets with respect to liquidity and private information processing. The markets are found to be fairly liquid, providing liquidity at a stable rate throughout the 24 hours trading period. The spread itself as well as the proportion attributed to adverse selection costs are high suggesting that private information is an important aspect in the bid-ask spread.
The emergence of financial technology in the last 10 years has created a new type of asset that is Cryptocurrency. Cryptocurreny offers a small transaction fee without involving a third party in its transaction and the ability to make its users anonymous. It became one of its main selling points and was quickly accepted widely in the financial world. Cryptocurrency price movements become volatile. For examples, Bitcoin issued in 2009, the value is not more than USD 10, but in early June 2017, Bitcoin is worth about USD 3000 (Bloomberg, July 5th, 2017). Many investors are interested to invest in Cryptocurrency, especially investors with high risk tolerance. This study aims to find the effects of Cryptocurrency on well-formed portfolios. The assets we use are Foreign Currency, Commodity, Stock, and ETF. The Cryptocurrency we will use is Bitcoin, Ripple and Litecoin. Using the Modern Portfolio Theory approach, we can create an investment portfolio. The results show that the portfolio with Cryptocurrency indeed increases the effectiveness of the portfolio in two ways. The first is to minimize the standard deviation and the second is to create more allocation options for investors to choose from. The optimum allocation of Cryptocurrency is from 5% to 20% depending on the risk tolerance of the investor.
Abstract Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have low liquidity compared to traditional assets, one needs to take into account liquidity issues when adding them to a portfolio. We propose a Liquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of risk-return portfolio optimization under liquidity constraints. Cryptocurrencies are included in portfolios formed with stocks of the S&P 100, US Bonds, and commodities. We illustrate the importance of the liquidity constraints in an in-sample and out-of-sample study. LIBRO improves the weight optimization in the sense that it only adds cryptocurrencies in tradable amounts depending on the intended investment amount. The returns greatly increase compared to portfolios consisting only of traditional assets. We show that including cryptocurrencies in a portfolio can indeed improve its risk–return trade-off.
Bitcoin was the first cryptocurrency to use blockchain and has been the market leader since the first bitcoin was mined in 2009. After the birth of Bitcoin with the genesis block, more than 1,000 altcoins and crypto-tokens have been created, with at least 919 trading actively on unregulated or registered exchanges. This entire class of cryptocurrencies and tokens has been classified by some tax authorities as having the same status as commodities. If cryptocurrency is viewed in the same class as commodities, how different is it in terms of its risk and return structure? This article sets out to help readers understand cryptocurrencies and to explore their risk and return characteristics using a portfolio of cryptocurrency represented by the Cryptocurrency Index (CRIX). Substantial discussions are centered on Bitcoin and its close variants. Some questions are raised about the potential of cryptocurrencies as an investment class. Results show that the return correlations between cryptocurrencies and traditional assets are low and that adding CRIX returns to a traditional asset portfolio improves risk–return performance. Sentiment analysis also indicates the CRIX has a relatively high Sharpe ratio. Although we should view the results with care, a new form of financing for cryptocurrency and blockchain start-ups is born. The disruption brought about by Bitcoin may be felt beyond payments through what is known as initial crypto-token offerings or initial token sales. <b>TOPICS:</b>Currency, risk management, performance measurement, mutual funds/passive investing/indexing
Guglielmo Maria Caporale, Luis A. Gil‐Alana, Alex Plastun
This paper examines persistence in the cryptocurrency market. Two different long-memory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013–2017. The findings indicate that this market exhibits persistence (there is a positive correlation between its past and future values), and that its degree changes over time. Such predictability represents evidence of market inefficiency: trend trading strategies can be used to generate abnormal profits in the cryptocurrency market.
Bitcoin is a cryptocurrency. It is not a legal currency but a private monetary system that manages itself and does not depend on central banks or governments. Since the development of Bitcoin, its trading volume has been increasing largely and rapidly. Some fear the increase in Bitcoin usage as it is quite different from traditional currencies; however, its use is spreading all over the world. This paper examines empirically whether or not weekly price anomalies exist by checking the market efficiency of Bitcoin. The empirical results show that the Bitcoin market is not efficient. However, the empirical results show that Bitcoin transactions are becoming and can become more efficient. The results suggest that Bitcoin returns will be random in the future.JEL classification numbers: E42, E44, E51Keywords: Anomaly, Bitcoin, efficient market
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.
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.
Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Back test trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 month's periods. Some recently published portfolio selection strategies are also used to perform the same back tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.
We propose an optimal dynamic pairs trading strategy model for a portfolio of cointegrated assets. Using stochastic control techniques, we compute analytically the optimal portfolio weights and relate our result to several other strategies commonly used by practitioners, including the static double-threshold strategy. Finally, we apply our model to a bitcoin portfolio and conduct an out-of-sample test with historical data from three exchanges, with two cointegrating relations.
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.
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 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 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.