Abstract Cryptocurrency markets have evolved into a vital segment of the global financial ecosystem, drawing considerable interest from both investors and regulatory bodies. Yet, their extreme price instability demands innovative strategies for risk mitigation and investment that diverge from conventional financial practices. This research focuses on analyzing the volatility patterns of leading cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)âby employing GARCH-family models such as GARCH, EGARCH, TGARCH, and CGARCH. Through a comparative evaluation of these models, the study identifies the optimal framework for characterizing cryptocurrency market volatility. Utilizing daily closing prices from Yahoo Finance (January 1, 2019, to January 8, 2025), the analysis reveals that TGARCH outperforms others for BTC, EGARCH for ETH, and CGARCH for BNB, underscoring the critical role of asymmetric volatility in these markets. This work advances existing research by offering a detailed comparison of GARCH-based approaches and practical insights for risk evaluation and portfolio optimization.
Abstract The approval of Bitcoin ETFs by the Securities and Exchange Commission (SEC) on 01/11/2024 was an essential event for both the cryptocurrency market and the traditional financial system. Bitcoin ETFs work as a bridge between digital assets and traditional financial instruments, contributing to increased liquidity and attracting new institutional investors who were reluctant before due to regulatory and security concerns. This study assesses the impact of the approval of Bitcoin ETFs on the stability of the financial system, focusing on the correlations and the volatility spillover effects of Bitcoin and three major financial indices (S&P 500, Dow Jones Industrial Average, and Nasdaq-100). Using Pearson Correlation, Time-Varying Parameter Vector Autoregression (TVP-VAR) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, this research offers a comprehensive analysis of the influence of Bitcoin on the dynamics of market. The results show that, although the correlations between Bitcoin and stock market indices reached a peak in 2021, they dropped later, suggesting a gradual decoupling from traditional financial markets. However, after the launch of Bitcoin ETFs in 2024, the correlations with financial indices â especially with S&P 500 â started to rise again, suggesting a reintegration of Bitcoin into the traditional financial system. Contrary to initial expectations, the results obtained from data covering 90 days before and after the launch of Bitcoin ETFs donât show a significant increase in short-term correlations, which suggest a smooth adaptation of the market to these new financial instruments. In addition, although Bitcoin ETFs contribute to the stabilization of cryptocurrency volatility, they introduced new types of intra-day fluctuations, highlighting the need for an advanced strategy of risk management. The study concludes that, while Bitcoin ETFs contribute to the stability of financial markets, they introduce systemic risks which require continuous surveillance from the regulatory authorities. Long-term implications of the approval of Bitcoin ETFs remain uncertain, hence more research is needed in order to comprehensively assess the impact of these new financial instruments on the global financial stability.
This study compares two approaches for measuring Conditional Value-atRisk (CoVaR), emphasizing the role of high-frequency intraday data in assessing systemic risk within financial systems. The first approach, AB CoVaR, estimates the risk of an asset Y conditional on another asset X being exactly at its Value-at-Risk (VaR) threshold. In contrast, the GE CoVaR refines this measure by capturing the risk of Y when X exceeds its VaR threshold, thereby accounting for more extreme scenarios and larger potential losses. To estimate these CoVaR measures, we employ high-frequency data sampled at five-minute intervals from major cryptocurrencies, including Bitcoin, Ethereum, Ripple, Solana, and Binance Coin. The results indicate that the GE CoVaR approach systematically yields higher risk estimates and exhibits superior predictive performance when applied to intraday data. Moreover, the analysis reveals strong interconnectedness among cryptocurrency returns. Bitcoin and Ethereum emerge as the primary sources of systemic risk, whereas Solana and Binance Coin are the most heavily affected assets. These findings underscore the granular risk dynamics captured through intraday analysis.
Purpose This study examines the performance of pair trading strategy in the cryptocurrency market under three statistical approaches including distance, cointegration and a hybrid method combining both distance and cointegration approaches. Design/methodology/approach The research uses daily, 4-h, 1-h, 15-min and 5-min data from the top 50 cryptocurrencies (by market capitalization) listed on Binance during three distinct periods: the bullish period of 2020, the stable period of 2021 and the bearish period of 2022. To perform a sensitivity analysis of the model, four approaches were implemented. First, both fixed and dynamic thresholds were applied across all three methods to assess their impact on trading results. Second, three standard deviations of 1.44, 1.65 and 2 were used, representing the coverage of normal data points in 85%, 90% and 95% of the time, respectively, to evaluate their influence on model profitability. Third, three different exit thresholds were employed to determine the extent to which changes in trade closure thresholds affect profitability. Fourth, the effect of the number of pairs in the portfolio on the modelâs profitability was examined. Findings The findings from these approaches highlight the inefficiency of the cryptocurrency market and demonstrate the profitability of pair trading across various time frames, particularly in high-frequency time frames such as 15-min and 5-min intervals. Moreover, the results show that using a fixed threshold significantly outperforms a dynamic threshold in terms of both returns and Sharpe ratio. Additionally, the findings indicate a positive impact of altering the entry thresholds, exit thresholds and the number of pairs in the portfolio on the profitability of the models. Practical implications Due to the increasing attention to cryptocurrencies in investment management, the proposed model and the results of this study can be significantly used by cryptocurrency market traders, portfolio managers and fintech to achieve significant returns along with the increase in market liquidity. Originality/value This article has used pair trading strategies in the cryptocurrency market as a form of high-frequency trading for the first time. In addition, this article has proposed a hybrid approach based on the combination of distance and cointegration criteria to increase the efficiency of pair detection in the cryptocurrency market. It has evaluated the pair trading strategy in the form of different entry and exit criteria for a cryptocurrency.
Purpose (1) How can high-frequency data be utilized more effectively to identify and extract various risks in the cryptocurrency market? (2) Do the risk characteristics of different cryptocurrencies exhibit consistency or variability across multiple dimensions? (3) Based on these risk characteristics, how can more precise risk prevention and hedging strategies be provided to investors? Design/methodology/approach (1) The threshold optimal detection (TOD) model can accurately separate jump and continuous behaviors by setting appropriate threshold values, effectively identifying extreme price fluctuations in the market. (2) The method for separating trend and cyclical behaviors can be achieved through filter design and application. This approach effectively distinguishes between long-term and short-term fluctuations in the cryptocurrency market, enabling a clearer analysis of market risks across different time scales. (3) We use linear regression to estimate the sensitivity of cryptocurrency returns to different risk factors, represented by the β coefficient. (4) The time-frequency domain characteristics of wavelet coherence analysis allow for simultaneous examination of risk frequencies in the cryptocurrency market, providing a more comprehensive understanding of market behavior. Findings First, by integrating high-frequency data with multidimensional risk decomposition techniques, this study systematically identifies and analyzes various risk features within the cryptocurrency market, enriching the existing literature on high-frequency volatility and risk identification. Second, this paper innovatively decomposes continuous risk into trend risk and cyclical risk, providing a more refined framework for managing market volatility risks. Finally, the paper proposes differentiated response strategies tailored to various risk characteristics, particularly in jump risk management, offering practical guidance on the use of derivatives such as options to provide actionable solutions for investors. Originality/value This paper proposes a multidimensional risk extraction and analysis method by examining high-frequency data of nine major cryptocurrencies from December 2020 to July 2024. It not only explores the characteristics of jump risk and continuous risk but also further decomposes continuous risk into trend risk and cyclical risk using filtering techniques, revealing the heterogeneous performance of different cryptocurrencies in both long-term and short-term volatility. This multidimensional risk analysis allows for a more comprehensive capture of various market fluctuation patterns, providing investors and risk managers with more effective response strategies.
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA.
Purpose This study aims to explore dynamic correlations and volatility spillovers as well as hedging opportunities of clean and dirty cryptocurrencies with both developed and emerging regional stock markets during the COVID-19 pandemic and the RussiaâUkraine conflict. Design/methodology/approach This study applies, first, the dynamic conditional correlationâgeneralized autoregressive conditional heteroskedasticity model proposed by Engle (2002) to analyze the dynamic correlations between cleanâdirty cryptocurrencies and regional stock markets. Second, it uses the VARâMGARCH with the BEKK representation developed by Engle and Kroner (1995), to explore the volatility spillover effects between all variables. Third, it determines the optimal portfolio weights and the hedge ratios following Kroner and Ng (1998) and Kroner and Sultan (1993), respectively. Findings The findings reveal significant correlation and volatility spillovers between cryptocurrencies and regional stock markets, which are more prevalent during the COVID-19 pandemic than during the RussiaâUkraine conflict. In addition, in times of crisis, pairing clean cryptocurrencies with emerging market indices appears more attractive due to their relatively lower contagion effects and volatility spillovers as well as cheaper hedging cost. Originality/value To the best of the authorsâ knowledge, this study is among the first to analyze the dynamic linkages of clean and dirty digital currencies with regional stock market indices during both COVID-19 pandemic and RussiaâUkraine conflict, contributing to not only enhanced understanding of these cross-market spillovers in time of crisis but also their hedging benefits. This understanding provides actionable insights into portfolio construction and risk management involving clean and dirty cryptocurrencies and regional stock market indices.
Portfolio optimization is a fundamental problem in financial theory, aiming to balance risk and return in asset allocation. Traditional models, such as MeanâVariance optimization, are effective, but often fail to account for diversification adequately. This study introduces the MeanâVarianceâEntropy (MVE) model, which integrates Tsallis entropy into the classic MeanâVariance framework to enhance portfolio diversification and risk management. Entropy, specifically second-order entropy, penalizes excessive concentration in the portfolio, encouraging a more balanced and diversified allocation of assets. The model is applied to a portfolio of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Cardano (ADA), and Binance Coin (BNB). The performance of the MVE model is compared with that of the traditional MeanâVariance model, and results demonstrate that the entropy-enhanced model provides better diversification, although with a slightly lower Sharpe ratio. The findings suggest that while the entropy-adjusted model results in a slightly lower Sharpe ratio, it offers better diversification and a more resilient portfolio, especially in volatile markets. This study demonstrates the potential of incorporating entropy into portfolio optimization as a means to mitigate concentration risk and improve portfolio performance. The approach is particularly beneficial for markets such as cryptocurrency, where volatility and asset correlations fluctuate rapidly. This paper contributes to the growing body of literature on portfolio optimization by offering a more diversified, robust, and risk-adjusted approach to asset allocation
This study examines and compares the effectiveness of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) models in forecasting volatility across three distinct financial markets: cryptocurrencies, Indonesian stocks, and U.S. stocks. The research analyzes daily closing price data from April 2018 to September 2024, focusing on five major cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, and Ripple), five Indonesian blue-chip stocks (BBCA, BBRI, BYAN, BMRI, and TPIA), and five major U.S. stocks (Apple, Nvidia, Microsoft, Google, and Amazon). Using comparative analysis of ARCH(1), GARCH(1,1), and EGARCH(1,1,1) models, the study evaluates their predictive accuracy through multiple metrics including AIC, MAE, RMSE, and SMAPE. Results indicate that EGARCH(1,1,1) generally performs better for cryptocurrencies and U.S. stocks, while GARCH(1,1) shows superior performance for Indonesian stocks, suggesting that volatility patterns and optimal forecasting models vary across different market contexts.
This paper investigates the volatility dynamics and underlying long memory features of four major cryptocurrencies-Bitcoin, Ethereum, Litecoin, and Ripple-which were selected due to their high liquidity, large trading volumes, and historical significance in the digital asset market. The long-range dependence exhibited in cryptocurrency markets is often overlooked. However, based on the strong evidence of persistent dependence in the return series, we adopt advanced volatility models that are capable of accommodating high volatility and heavy-tails, as well as the long memory properties of cryptocurrencies. Specifically, we employ long-memory extensions of the GAS (Long memory GAS) and GARCH (Fractionally Integrated Asymmetric Power ARCH) models, integrating heavy-tailed innovation distributions: the Generalized Hyperbolic Distribution (GHD) and Generalized Lambda Distribution (GLD). Standard GARCH and GAS models are included as benchmarks. The performance of the models are assessed using Value-at-Risk (VaR) estimation, backtesting (in-sample and out-of-sample) and volatility forecasting metrics. The results indicate that long memory models, particularly the FIAPARCH model, consistently outperforms the standard GAS and GARCH models in capturing tail risk and the volatility persistence. These findings emphasize the critical role of long memory in modeling the risk of cryptocurrencies, indicating that accounting for volatility persistence can significantly enhance the accuracy of risk estimates and strengthen risk management practices.
ABSTRACT This paper investigates the economic consequences for Bitcoin options' prices of a long memory in conditional volatility and conditional nonânormality of Bitcoin returns. The arbitrageâfree prices of Bitcoin options are determined by market consistent valuation and the conditional Esscher transform. Monte Carlo estimates for option prices from estimated models based on Bitcoin returns data are provided. Explanations for the results from an economic perspective are provided. Economic insights and implications of the results for the nature of cryptocurrencies, their risk evaluation, and the hedging of Bitcoin's derivatives are explored.
Whether financial assets movements exhibit correlation and memory has been an intriguing question for physicists. This study aims to investigate whether financial shocks exhibit non-Markovian behavior. In particular, it explores the presence of long-term memory and non-local fluctuations during financial crises. The non-Markovian behavior of volatility and return during the cryptocurrency crashes of 2017â2021 and 2021â2024 cycles are examined. The analysis shows that a scaling relation, which is valid for a singular Markovian process, breaks down in data sets spanning approximately 1 year and 3 years after the onset of the 2017 crash. A similar pattern was observed in the 2021 crash, although the analysis does not work for some data sets. In these time intervals, the crash process shows non-Markovian behavior with financial shocks demonstrating non-local fluctuations and evidence of long-term memory.
Rihab Belguith, Hind Alnafisah, Yasmine Snene Manzli, Ahmed Jeribi
This study investigates how gold and Bitcoin can mitigate risk for investors in the BRICS Plus economies (Brazil, Russia, India, China, South Africa, and invited members: Egypt, Argentina, Saudi Arabia, and the United Arab Emirates). We employ a time-varying copula approach to analyze the safe haven, hedging, and diversification abilities of these assets against the BRICS Plus stock market indices for the period from January 4, 2016, to January 5, 2024. This timeframe encompasses significant events including the COVID-19 pandemic, the RussiaâUkraine conflict, and the Silicon Valley Bank collapse. Results show that during normal periods, both gold and Bitcoin act as diversifiers. However, during crises, their dynamics change, revealing their effectiveness as risk mitigators. Gold emerges as a strong diversifier and safe haven, particularly for Russia, India, Argentina, Saudi Arabia, and the United Arab Emirates during the COVID-19 pandemic. Bitcoin exhibits some safe-haven qualities, especially for South Africa and India, but its effectiveness varies by country. Our research suggests gold is a more consistent hedge than Bitcoin, especially during market downturns. This study offers practical insights for investors and policymakers. Investors in the BRICS Plus region can leverage these findings to make informed decisions by choosing between the stability of gold and the potential diversification of Bitcoin. Policymakers, on the other hand, can use this knowledge to develop strategies that help manage risk during volatile market conditions.
This study examines the impact of the coronavirus disease 2019 (COVID-19) pandemic on market efficiency by analyzing three time series -- price returns, absolute returns, and volatility increments -- in stock (Deutscher Aktienindex, Nikkei 225, Shanghai Stock Exchange (SSE), and Volatility Index) and cryptocurrency (Bitcoin and Ethereum) markets. The effect is found to vary by asset class and market. In the stock market, while the pandemic did not influence the Hurst exponent of volatility increments, it affected that of returns and absolute returns (except in the SSE, where returns remained unaffected). In the cryptocurrency market, the pandemic did not alter the Hurst exponent for any time series but influenced the strength of multifractality in returns and absolute returns. Some Hurst exponent time series exhibited a gradual decline over time, complicating the assessment of pandemic-related effects. Consequently, segmented analyses by pandemic periods may erroneously suggest an impact, warranting caution in period-based studies.
This study explores the role of gold-backed cryptocurrencies (PAXG and XAUT) as effective diversifiers, hedges, and safe havens for NFTs and DeFi assets, particularly during market crises such as the COVID-19 pandemic and the 2022 cryptocurrency crash. By employing a dynamic GARCH-copula approach, the research analyzes the interconnectedness and volatility spillovers between these digital asset classes, providing insights into their behavior during times of heightened uncertainty. We also compute the optimal hedge ratio for each gold-backed cryptocurrencies/stabelcoins-NFT/DeFi/Traditional cryptocurrencies pair and evaluate their dynamic hedging effectiveness. The findings reveal that gold-backed cryptocurrencies offer superior hedging capabilities compared to stablecoins (USDT and BUSD), enhancing portfolio diversification and risk management. The results underscore the importance of incorporating gold-backed assets into digital portfolios to improve resilience and achieve better risk-adjusted returns during periods of market turmoil.