This paper discusses whether the Bitcoin exchange-traded fund (ETF), which tracks the value of Bitcoin, improves equity portfolios, by using a robust portfolio performance analysis. The equity portfolio is represented by an ETF that tracks the Standard & Poorâs 500. We use data from a turbulent investment period within the coronavirus pandemic, to study the diversification benefits of Bitcoin. We compare the performances of diverse portfolios composed of both ETFs, which include 40 classical dynamic volatility model-based portfolios and 900 score-driven portfolios. For the score-driven portfolios, the dynamic association is modelled by score-driven Clayton, rotated Clayton, Gumbel, rotated Gumbel and Studentâs t copulas. We compare portfolio strategies using the model confidence set test. We find that score-driven portfolios outperform classical volatility model-based portfolios and the equity portfolio. Our results may provide suggestions for cryptocurrency investors on portfolio optimization and may also have policy implications for regulators and policymakers.
This study conducted the econometric analysis to test the hedge and safe haven effects of Non-fungible Tokens (NFTs) on major traditional asset markets in the global financial system. We investigate the estimates of these effects in times of extreme market conditions and the COVID-19 crisis. Our empirical results show evidence of the hedge and safe haven properties of NFTs, confirming two main findings: (i) NFTs act as a hedge and safe haven for particular stock markets and oil, bond, and USD indices, even though the degree of effects varies across asset classes; and (ii) NFTs also serve as sheltering facilities for the markets mentioned above, with more substantial safe haven benefits for bond and USD indices during the recent pandemic crisis.
The stochastic volatility (SV) model is one of the main methods of modeling time-varying volatility.In particular, SV model is actively used in estimation and prediction of financial market volatility and option pricing.This paper attempts to model the time-varying volatility of the bitcoin market price using SV model.Hidden Markov model (HMM) is combined with the SV model to capture characteristics of regime switching of the market.The HMM is useful for recognizing patterns of time series to divide the regime of market volatility.This study estimated the volatility of bitcoin by using data from Upbit, a cryptocurrency trading site, and analyzed it by dividing the volatility regime of the market to improve the performance of the SV model.The MCMC technique is used to estimate the parameters of the SV model, and the performance of the model is verified through evaluation criteria such as MAPE and MSE.
Samuel Gaskin, Rafay Kalim, Kelvin J. Wallace, David Islip ¡ 6 authors
This article addresses the shortcomings of the existing literature regarding cryptocurrency portfolio construction. First, we address the effectiveness of time-series models that capture stylized features. We perform a comparison study on various methods for estimating distributions for asset returns, including normal, historical, and GARCH models within a CVaR setting. The goal of this comparison is to determine the financial benefits of constructing portfolios based on estimated distributions that consider stylized features of crypto return series. Next, we create and compare various prediction models for cryptocurrencies and integrate them with mean-variance optimization to base performance on portfolio management metrics, such as Sharpe ratio and level of diversification, rather than statistical metrics like accuracy and R<sup>2</sup> on which the literature solely focuses. We determine it is unclear which optimization approach (CVaR or Robust MVO) leads to better crypto portfolios, and so, to address this, we compare optimization procedures on out-of-sample data through a thorough cross-validation of hyperparameters for each technique. We then compare the resulting risk-optimal portfolios from each technique. The results show that a CVaR approach with a GARCH simulation and a decision tree prediction model with robust mean-variance optimization yield portfolios of similar risk. We also show that using statistical metrics to evaluate models may not always yield the best financial performance.
AntĂłnio Portugal Duarte, FĂĄtima Sol Murta, Nuno Baetas da Silva, Beatriz Rodrigues Vieira
This paper analysis and compares the volatility of seven cryptocurrencies â Bitcoin, Dogecoin, Ethereum, BitcoinCash, Ripple, Stellar and Litecoin â to the volatility of seven centralized currencies â Yuan, Yen, Canadian Dollar, Brazilian Real, Swiss Franc, Euro and British Pound. We estimate GARCH models to analyze their volatility. The results point to a considerably high volatility of cryptocurrencies when compared to that of centralized currencies. Therefore, we conclude that cryptocurrencies still fall far short of fulfilling all the requirements to be considered as a currency, specifically regarding the functions of store of value and unit of account.
This paper applies the multivariate GARCH models to investigate the role of Bitcoin as a hedge and safe haven for ASEAN+6 stock markets compared to gold. We used daily data for the dates 2 January 2017â20 January 2023, covering the recent COVID-19 pandemic. The empirical findings provide compelling evidence of cross-market shock and volatility transmission between stock returns and Bitcoin returns in both directions. Therefore, the dynamics of Bitcoin returns significantly influence the volatility of stock returns, and the relationship also holds in reverse. All diagonal element estimations are statistically significant for both periods, as shown by the findings of the return and volatility spillovers between the returns of gold and the ASEAN+6 stock market. For most ASEAN+6 equity markets evaluated, Bitcoin and gold are not safe havens, and their inclusion increases the portfolio downside risk.
Previous research has shown volatility jumps and co-jumping behaviours in cryptocurrency markets. Motivated by these findings, we employ the herding effect and financial contagion channel to outline a theoretical framework of volatility-state-dependent correlations in cryptocurrency markets. We show that digital currency markets are more strongly correlated when experiencing an identical volatility regime, which echoes co-jumping behaviours addressed by the literature. Moreover, the strong correlation that occurs when the paired cryptocurrencies simultaneously experience a high volatility regime results in the least effectiveness of diversification in terms of a minimum portfolio risk reduction. Last but not least, the proposed state-dependent approach in this study proves effective at the task of risk forecasting and risk reduction for cryptocurrency portfolios, beyond the bivariate GARCH-based models, which are a pure and simple time-dependent approach.
While volatility spillover is a vital research area in financial economics (due to its importance for risk valuation and portfolio diversification strategies), the volatility linkage between Bitcoin and electricity/energy markets has not received adequate attention. As the Bitcoin mining cost comes mainly from electricity (which is highly dependent on natural gas), we hypothesize that natural gas is a non-trivial Bitcoin price volatility driver and aim to test if this is the case. Specifically, we employ a widely used model called the HAR-RV model to assess volatility spillover across Bitcoin and natural gas using high-frequency data. We find a spillover effect from natural gas to Bitcoin, and the positive (negative) component of natural gas volatility stabilizes (destabilizes) Bitcoin volatility. The spillover effect is further examined and confirmed using an out-of-sample approach.
This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, InclĂĄn and Tiaoâs iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets, confirming our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modeling. The ARFIMA-FIGARCH model, with structural breaks and a skewed Student-t distribution, fits the cryptocurrency marketâs price dynamics well.
In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The methodology proposed allows to focus on extreme returns and describe their temporal evolution by introducing in the model time-dependent coefficients evolving according to a latent discrete homogeneous Markov chain. As it is often used in the expectile literature, estimation of the model parameters is based on the asymmetric normal distribution. Maximum likelihood estimates are obtained via an Expectation-Maximization algorithm using efficient M-step update formulas for all parameters. We evaluate the introduced method with both artificial data under several experimental settings and real data investigating the relationship between daily Bitcoin returns and major world market indices.
In this paper, we investigate the co-dependence and portfolio value-at-risk of cryptocurrencies, with the Bitcoin, Ethereum, Litecoin and Ripple price series from January 2016 to December 2021, covering the crypto crash and pandemic period, using the generalized autoregressive score (GAS) model. We find evidence of strong dependence among the virtual currencies with a dynamic structure. The empirical analysis shows that the GAS model smoothly handles volatility and correlation changes, especially during more volatile periods in the markets. We perform a comprehensive comparison of out-of-sample probabilistic forecasts for a range of financial assets and backtests and the GAS model outperforms the classic DCC (dynamic conditional correlation) GARCH model and provides new insights into multivariate risk measures.
Abstract Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates, which may result in poor out-of-sample performance. In particular, the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long. This is precisely the case in the cryptocurrency market, where there are hundreds of crypto assets that have been traded for a few years. We propose enhancing the mean-variance (MV) model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period. In the pre-selection stage, we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality. The prototypes of the clustering partition are automatically examined and the one that best suits our risk-aversion preference is selected. We then run the MV portfolio optimization with the crypto assets of the selected cluster. The proposed approach is tested for a period of 17 months in the whole cryptocurrency market and two selections of the cryptocurrencies with the higher market capitalization (175 and 250 cryptos). We compare the results against three methods applied to the whole market: classic MV, risk parity, and hierarchical risk parity methods. We also compare our results with those from investing in the market index . The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators. This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.
Although cryptocurrencies are catching the fancy of investors for various benefits such as decentralization, low transaction costs, and inflation hedging, their extreme volatility is sometimes keeping many away. Consequently, modeling and forecasting cryptocurrency market volatility are essential to investorsâ investment decisions and risk management. However, most previous studies have been limited to Bitcoin volatility, disregarding cryptocurrency market performance as a whole. This study estimates realized volatility of cryptocurrency market with a variety of algorithms employing a portfolio-style technique. After comparison, LSTM networks surpass the conventional GARCH-type models; meanwhile, the hybrid GARCH neural network models perform the worst. This study provides an impetus for a significant number of academics interested in the extreme volatility of cryptocurrencies. Additionally, it illustrates that more sophisticated models may not always lead to better predictive performance.
Minhyuk Lee, Younghwan Cho, Seung Eun Ock, Jae Wook Song
This research analyzes asymmetric volatility and multifractality in four representative cryptocurrencies using index-based asymmetric multifractal detrended fluctuation analysis. We suggest investigating an idiosyncratic risk premium, which can be obtained by removing the market influence in the cryptocurrency return series. We call the process a capital asset pricing model filter. The analyses on the original return series showed no significant sign of asymmetric volatility. However, the filter revealed a distinct asymmetric volatility, distinguishing the uptrend and downtrend fluctuations. Furthermore, the analyses on the idiosyncratic risk premium detected some cases of asymmetry in the degree and source of multifractality, whereas that on the original return series failed to detect the asymmetry. In conclusion, in a highly volatile market, the capital asset pricing model filter can improve an investigation of the asymmetric multifractality in cryptocurrencies.
Abstract We use a semiparametric GARCH-in-Mean copula model to examine the volatility dynamics and tail dependence between cryptocurrency markets and financial markets. We do not find any statistically significant tail dependence between the financial and cryptocurrency markets, but we find lower tail dependence between Bitcoin and stock returns. There is lower tail dependence among Bitcoin, Ethereum, and Litecoin, and the lower tail dependence between Ethereum and Litecoin returns is the strongest. The GARCH-in-Mean model shows that the uncertainty effect on cryptocurrency returns is not statistically significant, while uncertainty has a negative and statistically significant effect on Bitcoin returns. The fact that there is no tail dependence between cryptocurrency and the interest rate or the effective exchange rate of U.S. dollar suggests that cryptocurrency could offer safe haven, defined as an asset that is uncorrelated with stocks and bonds.
Dora Almeida, Andreia DionĂsio, Isabel Vieira, Paulo Ferreira
Cryptocurrencies are relatively new and innovative financial assets. They are a topic of interest to investors and academics due to their distinctive features. Whether financial or not, extraordinary events are one of the biggest challenges facing financial markets. The onset of the COVID-19 pandemic crisis, considered by some authors a "black swan", is one of these events. In this study, we assess integration and contagion in the cryptocurrency market in the COVID-19 pandemic context, using two entropy-based measures: mutual information and transfer entropy. Both methodologies reveal that cryptocurrencies exhibit mixed levels of integration before and after the onset of the pandemic. Cryptocurrencies displaying higher integration before the event experienced a decline in such link after the world became aware of the first cases of pneumonia in Wuhan city. In what concerns contagion, mutual information provided evidence of its presence solely for the Huobi Token, and the transfer entropy analysis pointed out Tether and Huobi Token as its main source. As both analyses indicate no contagion from the pandemic turmoil to these financial assets, cryptocurrencies may be good investment options in case of real global shocks, such as the one provoked by the COVID-19 outbreak.
Cryptocurrencies and blockchain technologies have been among the most widely discussed topics in academic research and practical applications in recent years. The adoption of these technologies has seen tremendous growth and is becoming increasingly relevant as a potential disrupter of many economic industries. Thus, this thesis aims to investigate the relevance of cryptocurrencies to global financial markets. In the second chapter of this thesis, we explore the impact of Bitcoinâs risks on traditional asset classes. Our cross-asset analysis reveals that Bitcoin has positive spillover effects on risky assets but negative spillover effects on defensive assets. By examining the source of these risk transmissions, we demonstrate that U.S. companiesâ increased economic exposures to blockchain and cryptocurrency technologies have exacerbated these spillovers. Our empirical findings highlight that the price fluctuations of an unregulated asset such as Bitcoin can influence the price dynamics of regulated assets. Motivated by the findings of the second chapter, our third chapter investigates risk exposures associated with Bitcoin in equity portfolios. We show that the Bitcoin-equity dynamics intensified post-COVID-19 and provide investment practitioners with practical guidance on managing unregulated asset risks. Finally, we turn our attention to the behavioural biases of cryptocurrency investors, drawing on the low volatility anomaly. This study investigates the differentiated pricing of jump and diffusive risks in the cross-section of cryptocurrency returns. We show that a hedged portfolio sorted on idiosyncratic diffusive risk yields a weekly return of -1.11%, suggesting the existence of a low idiosyncratic risk anomaly. Subsequently, we examine explanations for this anomaly and show that limits to arbitrage prevent arbitrageurs from correcting the mispricing. In doing so, we demonstrate that cryptocurrency investors exhibit similar biases to equity investors.
ABSTRACT Recent literature explores the profitability of various cryptocurrency momentum trading strategies and proposes cryptocurrency momentum as a pricing factor (Liu et al.). How risky is this factorâbased investment strategy for cryptoâinvestments? We answer this question by examining the distributional characteristics (hence, riskiness) of six cryptocurrency momentum trading strategies. The empirical evidence suggests that the realised variances of cryptocurrency momentum strategies are governed by power laws. The statistical tests derived from block bootstraps indicate that the population mean and variance of the momentum factor realised variances are statistically not defined. Contrary to the belief that cryptocurrency momentum trading strategies produce generous payoffs, our results imply that, in real life, we might not be able to realise these risk premiums. We conclude that the performance metrics evaluating the profitability of cryptocurrency momentum strategies, using variance as an input, are not informative. We also find crossâsectional dependence amongst the tail risk of momentum strategies based on different formation periods.
Abstract The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t -distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.