This paper uses different multivariate GARCH models to model conditional correlations and analyze the volatility spillovers between cryptocurrency time series. The dynamic conditional correlation GARCH model is found to fit the data the best. Our empirical results are fourfold. First, on average, a $1 long position in BitShares (BTS) can be hedged for 15% with a short position in MonaCoin (MONA), while a $1 long position in MONA can be hedged for 14% with a short position in Ripple (XRP). Second, the average weight for the BTS/MONA portfolio is 0.48, indicating that for a $1 portfolio, 48% should be invested in BTS and 52% invested in MONA. Third, the average weight for the BTS/XRP portfolio indicates that 27% should be invested in BTS and 73 % invested in XRP. Finally, the average weight for the MONA/XRP portfolio indicates that 33% should be invested in MONA and 67% invested in XRP.Keywords: Cryptocurrencies, Multivariate GARCH, Volatility spillover, Hedging, Portfolio designs.JEL Classifications: C5, C22, C32, G1.DOI: https://doi.org/10.32479/ijefi.7383
Abstract Periodically collapsing rational bubbles model speculative demand in asset markets. The price and quantity of bitcoin are integrated of different orders, which is evidence of a bubble. Cointegration tests that allow for the potential presence of such bubbles with alternative proxies for fundamentals cannot reject a bubble in bitcoin.
Dorje C. Brody, Lane P. Hughston, Bernhard K. Meister
A term structure model in which the short rate is zero is developed as a candidate for a theory of cryptocurrency interest rates. The price processes of crypto discount bonds are worked out, along with expressions for the instantaneous forward rates and the prices of interest-rate derivatives. The model admits functional degrees of freedom that can be calibrated to the initial yield curve and other market data. Our analysis suggests that strict local martingales can be used for modelling the pricing kernels associated with virtual currencies based on distributed ledger technologies.
Given that there are both continuous and discontinuous components in the movement of asset prices, existing asset pricing models that assume only continuous price movements should be revised. In this paper, we explore the features of jumps, which are discontinuous movements, by examining Bitcoin pricing. First, we identify jumps in the Bitcoin price on a daily basis, applying a non-parametric methodology and then break down the Bitcoin total rate of return into a jump rate of return and a continuous rate of return. In our empirical analysis, price jumps turn out to be independent of volatility. Moreover, the jumps in the Bitcoin price do not appear at regular intervals; rather, they tend to be concentrated in clusters during special periods, implying that once an economic crisis occurs, the crisis will last for a long time due to contagion effects and the economy will take a considerable amount of time to recover fully. Further, the contribution of the jump rate of return to the total rate of return of the Bitcoin price is lower than the contribution of the continuous return, implying that the pursuit of sustainable returns rather than large but temporary returns will improve the total rate of return over the long term. Finally, more jumps are observed when trading volume is lower, implying that market illiquidity drives discontinuous movement in asset prices. Overall, the features of jump risk are like two sides of the same coin and jump risks are expected to have a significant effect on asset pricing, suggesting that consideration of jumps is essential for risk management as well as asset pricing.
Retrieving a set of 143 cryptocurrencies for a sample spanning 2014–2018, we investigate the popular momentum strategy implemented in the cryptocurrency market. Contrary to earlier studies our findings do not indicate any evidence of significant momentum payoffs, supporting the view that the cryptocurrency market is far more efficient than suggested in earlier studies.
The goal of this paper is to explore the relationship between momentum effects and liquidity in cryptocurrency markets. Portfolios based on momentum-liquidity bivariate sorts are formed and rebalanced on a varying number of cryptocurrencies through time. We find a strong momentum effect in the most liquid cryptocurrencies, which supports the theories of investor herding behavior. Moreover, we propose two profitable long-only strategies: the illiquid losers and liquid winners, which exhibit improved risk adjusted performance over the market capitalization weighted portfolio.
This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Student’s-t Copulas, we find that Ethereum is likely to be the independent coin in this market, while Bitcoin tends to be the spillover effect recipient. Our study sheds further light on investigating the contagion risks among cryptocurrencies by employing Student’s-t Copulas for joint distribution. This result suggests that all coins negatively change in terms of extreme value. The investors are advised to pay more attention to ‘bad news’ and moving patterns in order to make timely decisions on three types (buy, hold, and sell).
This article considers the problem of testing for an explosive bubble in financial data in the presence of time-varying volatility. We propose a sign-based variant of the Phillips, Shi, and Yu (2015, International Economic Review 56, 1043–1077) test. Unlike the original test, the sign-based test does not require bootstrap-type methods to control size in the presence of time-varying volatility. Under a locally explosive alternative, the sign-based test delivers higher power than the original test for many time-varying volatility and bubble specifications. However, since the original test can still outperform the sign-based one for some specifications, we also propose a union of rejections procedure that combines the original and sign-based tests, employing a wild bootstrap to control size. This is shown to capture most of the power available from the better performing of the two tests. We also show how a sign-based statistic can be used to date the bubble start and end points. An empirical illustration using Bitcoin price data is provided.
This paper investigates the importance of "time of execution" and the relevance of "precision time" in order driven transactions done over distributed ledgers. We created a distributed marketplace using stock market price data from the Toronto Stock Exchange (TMX). We then proceeded to test and measure the impact of timing of orders at the nanosecond level. Whilst price discovery in order driven markets is done instantaneously, with distributed markets, it is necessary to know which order to process first to avoid "front-running". We argue that a protocol for the time of order of receipt and execution should be subject to nanosecond stacking. Our approach incorporates both transitory and permanent price discovery components. It allows for the efficient processing of transactions and the order that are received by a market clearing distributed ledger.
This paper fits in the trend of discussing the efficiency of cryptocurrency markets. Since 2008, when Bitcoin appeared on the market, arbitrageurs from all over the world have been trying to find the gaps in the markets, which will let them earn risk-free money using financial operations. Although a lot of researchers are trying to figure out arbitrage opportunities, looking at different exchanges and using different cryptocurrencies, so far hardly anyone has looked at arbitrage opportunities with the use of FIAT currencies within the same or different exchanges. This paper examines such opportunities for three different exchanges, i.e. Kraken, Bitfinex and Bitstamp -exchanges that enable trading in USD and EUR against Bitcoin at the same time. The main empirical results suggest that there are significant arbitrage opportunities on these markets. In the paper, we also show the main constraints in FIAT currencies arbitrage on cryptocurrency exchanges.
This letter investigates the dynamic relationship between market efficiency, liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin time series is anti-persistent. After 2013, as liquidity increased, the Hurst exponent rose to approximately 0.5, improving market efficiency. For several periods, however, the Hurst exponent was found to be significantly less than 0.5, making the time series anti-persistent during those periods. We also investigate the multifractal degree of the Bitcoin time series using the generalized Hurst exponent and find that the multifractal degree is related to market efficiency in a non-linear manner.
Matthias Schnaubelt, Jonas Rende, Christopher Krauß
The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets.
In this study, we investigate the relationship between Bitcoin mining technology variables and Bitcoin returns, using a GARCH-M model. Additionally, we examine the predictive power of the mining technology variables on future Bitcoin returns. We find that mining difficulty and block size are inversely related to Bitcoin returns. Additionally, our findings signifying that the higher the block size the lower the Bitcoin price and consequently the lower the expected return. Second, our findings show that mining difficulty and block size are robust predictors of future Bitcoin returns.
A peer-to-peer network for conducting encrypted digital trade called cryptocurrency was created eight years ago. The first and most well-known cryptocurrency, Bitcoin, is leading the charge as a disruptive technology to decades-old, largely unaltered financial payment infrastructure. Although cryptocurrencies are unlikely to displace traditional fiat money, they might alter how Internet-connected global markets communicate with one another by removing restrictions imposed by conventional national currencies and exchange rates. Technology develops quickly, and the success of a particular technology is almost entirely determined by the market it attempts to better.
Bitcoin has emerged to become the most popular cryptocurrency and its presence\nhas the potential to disrupt existing payment and monetary systems. Over the past\ndecade, the bitcoin price has exhibited extreme volatility, puzzling for both academics\nand market practitioners. We examine the dynamic relationship between\ninvestor attention and the bitcoin price using principal component analysis and vector\nerror correction models and discover that investor attention is an important contributor\nin bitcoin price formation. Variance decomposition analysis suggests that\ninvestor attention explain a significant amount of future variations in the bitcoin\nprice, and investor attention can be used to predict direction of future price change.\nOur study offers insight into the bitcoin market and the economic impact of investor\nattention.
The cryptocurrency literature has attempted to identifying factors that explain excess returns. We utilise principal component analysis to determine whether a (small) set of factors can explain returns and whether this varies over time. We find that a substantial proportion of cryptocurrency return variation is explained by a single principal component that is highly correlated with bitcoin returns. The explanatory power of this factor is greatest for larger cryptocurrencies and increases markedly in the most recent part of the sample. Our results have implications for investors determining optimal portfolio decisions and for policymakers wary of systemic risk.
This paper analyses the efficiency of cryptocurrency markets by applying econometric models to different short-term investment horizons. A number of experiments are carried out to demonstrate that small training sets can still be used to build efficient and useful forecasts, which in turn can be transformed into straight-forward investment strategies. It also compares the application of selected models on cryptocurrency and mature stock markets. The forecasting accuracy of the models is explored using different error metrics and different horizons. The results suggest that the variation of the error estimates doesn’t appear to be tightly related to the maturity of the markets, but rather depends on the intrinsic characteristics of the analyzed time series.
In this study, a methodology is presented where a hybrid system combining an evolutionary algorithm with artificial neural networks (ANNs) is designed to make weekly directional change forecasts on the USD by inferring a prediction using closing spot rates of three currency pairs: EUR/USD, GBP/USD and CHF/USD. The forecasts made by the genetically trained ANN are compared to those made by a new variation of the simple moving average (MA) trading strategy, tailored to the methodology, as well as a random model. The same process is then repeated for the three major cryptocurrencies namely: BTC/USD, ETH/USD and XRP/USD. The overall prediction accuracy, uptrend and downtrend prediction accuracy is analyzed for all three methods within the fiat currency as well as the cryptocurrency contexts. The best models are then evaluated in terms of their ability to convert predictive accuracy to a profitable investment given an initial investment. The best model was found to be the hybrid model on the basis of overall prediction accuracy and accrued returns.