We investigate the long-range cross-correlation and cross-multifractality between the “dirty” and “clean” cryptocurrencies and the major financial assets: the Dow Jones Index (DJI), the Euro–Dollar exchange rate (EURUSD), and Gold. The analysis shows a high long-range correlation between most pairs with some exceptions, including the DJI–Ripple and Gold–Polygon. When the DJI is paired with clean cryptocurrencies such as Polygon and Cardano, they exhibit multifractal properties. As for the EURUSD–BTC and Gold–BTC, these two pairs demonstrated the highest level of multifractality in their corresponding pairs. All pairs of cryptocurrencies and main financial indices are persistent, with the exceptions of EURUSD–POLYGON (H = 0 . 4970 ± 0 . 0048 for q =2), GOLD–BTC (H = 0 . 5039 ± 0 . 0058 for q =2) and GOLD–LTC (H = 0 . 5044 ± 0 . 0057 for q =2) that are Brownian, and GOLD–POLYGON (H = 0 . 4917 ± 0 . 0055 for q =2) which is anti-persistent. For q =5, all are anti-persistent, except DJI-Eth, XRP, and ADA are Brownian, and EURUSD-XRP is persistent. We also assessed the asymmetric persistence behavior when the market is upward or downward and found that for the pairs involving dirty cryptocurrencies with DJI and EURUSD, there is a higher level of persistence during the downward market. On the other hand, Gold-related pairs were almost symmetric. Thus, we identified the complexity and variability of the cryptocurrency pairs with the traditional financial instruments, which shows their various reactions to the changes in the market and types of assets.
Aktham Maghyereh, Mohammad Al‐Shboul, Basel Awartani
Research background: This paper explores the hedging and safe-haven properties of gold-backed cryptocurrencies within the context of conventional cryptocurrencies such as Bitcoin, Ethereum, Tether, and Binance. With the rise of blockchain technology, cryptocurrencies have gained recognition as alternative investment assets, drawing comparisons to traditional safe-haven assets like gold. However, the risk management potential of crypto gold, especially during periods of extreme market volatility, remains under-examined. Purpose of the article: The purpose of this article is to assess the effectiveness of gold-backed cryptocurrencies as hedging instruments and safe havens for investors in conventional cryptocurrencies. By analyzing their tail dependence during extreme market fluctuations, the study aims to determine their risk management utility. Methods: To achieve this, we employ a Student’s t copula structure integrated with an ARMA-GJR-GARCH model to measure the time-varying tail dependence between gold-backed and conventional cryptocurrencies. This approach allows for a comprehensive analysis of both normal and extreme market conditions. We use the Digix Gold Token (DGX) as a representative of gold-backed cryptocurrencies. The study examines four major conventional cryptocurrencies — Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance (BNB) — by analyzing daily closing prices from May 14, 2018, to January 31, 2023, which comprise 1702 observations. The dataset, sourced from coincodex.com, includes periods of significant market stress, such as the COVID-19 pandemic and the Russian-Ukrainian conflict. Findings & value added: The findings reveal a weak association between gold-backed cryptocurrencies and conventional cryptocurrencies, resulting in medium-to-low hedging effectiveness during the sample period. Nevertheless, during crisis periods, a negative association is observed, indicating that gold-backed cryptocurrencies act as effective safe havens in times of market distress. The study contributes to the literature by providing empirical evidence on the risk management benefits of crypto gold, particularly during financial crises, and highlights its potential inclusion in portfolios with cryptocurrency investments to enhance resilience.
Klaus Grobys, James W. Kolari, Davide Sandretto, Syed Jawad Hussain Shahzad · 5 authors
Abstract This paper explores the tail behavior of cryptocurrency momentum strategies and the profitability of volatility-managed momentum portfolios. Our main results derived from using a sample of large-cap cryptocurrencies and equal-weighted momentum portfolios indicate that cryptocurrency momentum is subject to severe crashes. Even a single cryptocurrency can cause insignificant momentum portfolio returns. In line with the literature on volatility-managing equity portfolios, our findings suggest that volatility management is a useful tool for mitigating cryptocurrency momentum crashes. Further corroborative evidence suggests that cryptocurrency momentum appears to be a phenomenon associated with large-cap cryptocurrencies.
Since its creation in 2008, Bitcoin has often been compared to precious metals due to their shared characteristics as safe havens, hedges, and risk diversification tools. This study uses the DCC-GARCH model to analyze dynamic conditional correlations and volatility spillovers between Bitcoin and the returns of gold, copper, silver, and platinum. The findings reveal persistent volatility and clustering in the returns of both Bitcoin and these metals. There is a one-way volatility spillover from gold to Bitcoin, and from Bitcoin to copper, silver, and platinum. Significant dynamic conditional correlations are observed between Bitcoin and both gold and copper, while no significant correlations are found with silver and platinum. These results provide valuable insights for portfolio diversification strategies and inform policymaker decisions in financial markets.
Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causal–noncausal invertible–noninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMA–GARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewness—common in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news (α) while overstating volatility persistence (β). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics.
The cryptocurrency market, known for its inherent volatility, has been significantly influenced by external shocks, particularly during periods of global crises such as the COVID-19 pandemic and the Russia–Ukraine war. This study investigates the volatility of the top seven cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance Coin (BNB), USD Coin (USDC), XRP, and Cardano (ADA)—from 1 January 2020 to 1 September 2024, employing a range of GARCH models (GARCH, EGARCH, TGARCH, and DCC-GARCH). This research aims to examine the persistence of leverage effects, volatility asymmetry, and the impact of past price fluctuations on future volatility, with a particular focus on how these dynamics were shaped by the pandemic and geopolitical tensions. The findings reveal that past price fluctuations had a limited impact on future volatility for most cryptocurrencies, although leverage effects became evident during market anomalies. Stablecoins (USDC and USDT) showed a distinct volatility pattern, reflecting their peg to the US Dollar, while platform-associated BNB demonstrated unique volatility characteristics. The results underscore the market’s sensitivity to price movements, highlighting the varying reactions of investor profiles across different cryptocurrencies. These insights contribute to understanding volatility transmission within the cryptocurrency market during times of crisis and offer important implications for market participants, particularly in the context of risk management strategies.
This paper investigates the peg instability of USDT from the tail risk spillover perspective using the CoVaR method. Specifically, we examine whether the conditional quantiles of USDT exhibit significant differences during periods of substantial declines in Bitcoin (BTC) and Ethereum (ETH) prices compared to normal market conditions. Using high-frequency data, we perform a bivariate Realized GARCH estimation of CoVaR and show that incorporating intraday information improves the precision of CoVaR estimation. We first verify that extreme negative returns in BTC and ETH significantly shift the correlation with USDT returns from positive to negative when compared to normal market conditions, indicating that USDT exhibits strong hedging properties and thus is not stable. Using the ΔCoVaR as a measure of the peg instability of USDT, we further detect significant downside to upside risk spillover effects from non-stablecoins (BTC and ETH) to USDT. These empirical findings provide implications for both testing and measuring the peg instability of USDT.
This paper applies the Lévy-GJR-GARCH model to explore the empirical dynamics of Bitcoin, Ethereum, and Ripple. It highlights volatility clustering, pronounced skewness, and high kurtosis in cryptocurrency markets. The study finds that models integrating innovation distributions more accurately capture and explain the volatility processes and tail risks in these assets. Advanced models, especially those accounting for extreme tail-end and asymmetric jump effects, are better suited for adapting to market changes and providing precise risk indicators, effectively identifying potential losses.
This study analyzed the volatility and risk profiles of three prominent blockchain-based cryptocurrencies—Dogecoin, Polygon, and Solana—using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Volatility, a key risk metric for cryptocurrencies, was modeled through the GARCH(1,1) framework, which effectively captured the time-varying nature of price fluctuations. The analysis revealed that Dogecoin exhibited the highest volatility and risk, primarily driven by its speculative market behavior and social media influence. Polygon and Solana, while also volatile, demonstrated more stability, with their risk profiles reflecting the technological advancements and broader use cases within their respective blockchain ecosystems. The study also incorporated Value at Risk (VaR) and Conditional Value at Risk (CVaR) metrics to assess the potential downside risks for each cryptocurrency. Dogecoin had the highest potential for extreme losses, followed by Polygon and Solana. The GARCH model successfully identified the volatility persistence in these assets, showing that past market conditions heavily influenced future volatility. This research contributes to the literature on cryptocurrency volatility by applying the GARCH(1,1) model to analyze digital assets with varying market characteristics. The findings emphasize the need for robust risk management strategies tailored to the unique behaviors of individual cryptocurrencies. Limitations of the study included the use of historical data and the focus on only three cryptocurrencies, suggesting opportunities for future research. Potential areas for further study include the incorporation of additional variables, such as macroeconomic indicators, and the exploration of alternative volatility models, such as EGARCH or TGARCH, to better capture the complexities of cryptocurrency markets. These insights provide valuable guidance for investors, risk managers, and policymakers navigating the volatile and evolving landscape of blockchain-based digital assets.
The rapid growth of decentralized finance (DeFi) has revolutionized the global financial landscape, providing decentralized alternatives to traditional financial services. This study investigates the asymmetric multifractal behavior of nine DeFi markets—AAVE, Pancake Swap (CAKE), Compound (COMP), Curve Finance (CRV), Maker DAO (MKR), Synthetix (SNX), Sushi Swap (SUSHI), UniSwap (UNis), and Yearn Finance (YFI)—using Asymmetrical Multifractal Detrended Fluctuation Analysis (A-MFDA). The use of generalized Hurst exponents, Rényi exponents, and singularity spectrum functions revealed that DeFi markets exhibit multifractal behaviors. The analysis uncovered clear differences between uptrend and downtrend fluctuation functions, highlighting asymmetric multifractal behavior. The asymmetry intensity was analyzed through excess differences in uptrend and downtrend generalized Hurst exponents. AAVE, COMP, SNX, UNis, SUSHI, and MKR exhibit negative asymmetry, with stronger correlations during negative trends. CAKE shifts from positive to negative asymmetry, showing sensitivity to both trends. CRV is more volatile in negative trends, while YFI consistently displays positive asymmetry across market fluctuations. The results also reveal that long-term correlations and heavy-tailed distributions contribute to the multifractality of DeFi assets. This study highlights the need for dynamic risk management in DeFi markets, urging investors to adopt adaptive strategies for volatile assets and prepare for sudden price fluctuations to safeguard investments.
Purpose This paper examines the relationship between the degree of information asymmetry among investors and the occurrence of bubbles in cryptocurrency markets. Design/methodology/approach The study applies the Philipps, Shi and Yu (PSY) methodology to identify bubbles in 74 cryptocurrencies from July 2014 to April 2021. Findings The findings indicate that there is a negative relationship between the degree of information asymmetry among investors and the number and duration of bubbles across cryptocurrencies. Originality/value This finding supports the riding-bubble argument of Asako et al. (2020), which suggests that when the information asymmetry among investors is high, rational investors are less certain about what irrational, inexperienced investors might decide. This strategic uncertainty leads rational investors to close out their positions more quickly, resulting in a shorter duration of the bubble and a reduced propensity for new bubbles to emerge. The study’s findings hold regardless of the proxies used to measure information asymmetry and noise trading, cryptocurrency characteristics and regression model specifications.
Has the mean-variance framework become obsolete? In this paper, we replace traditional variance–covariance methods of portfolio optimisation with relative Tsallis entropy and mutual information measures. Its goal is to enhance risk management and diversification in complicated finance ecosystems. We utilize the S&P 500 and Bitwise 10 cryptocurrency indices’ daily returns (2019–2024 data) and conduct our analysis to the year 2020 under extreme shocks. Many models were trained with different configurations, like mean-variance (MV), mean-entropy (ME), and mean-mutual information (MI) traders and their corresponding variants, using Sharpe’s ratio, Jensen’s alpha, and entropy value of risk (EVAR). The findings indicate that entropic models outperform conventional models in terms of diversification and, especially, extreme risk management. Because the appropriate normalization conditions often fail to be satisfied, we can informally see that after a recalibration of the effective frontier, we obtain from EVAR an accumulated resilience aspect to these rare events while also observing the great potential of entropy-based models to replicate non-linear dependencies between assets. The results show that models combining entropy and mutual information optimise the gain–loss ratio (GLR), providing stable diversification and improved risk management, while maximising returns in complex and volatile market environments.
This research employs Multifractal Detrended Fluctuation Analysis (MFDFA) to investigate multifractal properties in financial variables, including Bitcoin prices and economic indicators. Spanning 2019–2022, the analysis reveals multifractal scaling not only in Bitcoin prices, but also in economic indicators such as inflation rates and energy commodity prices. The non-linear singularity spectra unveil the multifaceted nature of scaling properties. Temporal analysis exposes intriguing trends in multifractality with implications for market efficiency. Furthermore, correlation analysis unveils connections among multifractal properties. For instance, a positive correlation between oil prices and Bitcoin suggests similar market forces. The log-log plot of fluctuation function Fq versus lag size demonstrates a power-law relationship, characteristic of multifractal systems. The empirical data’s alignment in log-log space suggests self-similarity in the Bitcoin time series, supporting multifractality. The calculated Hurst exponents values suggest varying degrees of multifractality across the years, with 2021 exhibiting the highest degree and 2022 the lowest. Furthermore, an asymmetry index (0.5767) deviating from 0.5 indicates that the multifractal nature of the Bitcoin market is not symmetric. This research enhances risk assessment and portfolio optimization in finance. It challenges the Efficient Market Hypothesis (EMH), emphasizing the significance of MFDFA in comprehending financial market and economic factor’s relationships.
2017'den sonra kripto para birimlerinde yaşanan fiyat dalgalanmaları, getiri fırsatları ve volatilite, yatırımcıların ilgisini çekerken; büyüyen işlem hacmi ve piyasa değeri, bu varlıkları geleneksel yatırımlara ek olarak yüksek kazanç ve portföy çeşitlendirme olanağı sunan bir seçenek haline getirmiştir. Buradan hareketle çalışmada piyasa değeri en yüksek üç kripto para biriminin (Bitcoin, Ethereum ve Tether USDt) 2017-2024 dönemi için volatilite düzeyleri asimetrik volatilite ölçüm modellerinden EGARCH modeli ile karşılıklı olarak incelenmektedir. EGARCH modellerine göre, Bitcoin ve Ethereum'da kötü haberler, getiri volatilitesini iyi haberlerden daha fazla etkilerken, kaldıraç etkisi gözlemlenmiştir. Buna karşılık, Tether USDt'de iyi haberlerin volatilite üzerindeki etkisi daha güçlü olup, anti-kaldıraç etkisi söz konusudur. Piyasadaki şokların, kripto paraların getiri volatilitesi üzerinde daha kalıcı bir etkiye sahip olduğu ve en çok Ethereum'un getiri oynaklığını etkilediği görülmektedir. Yarı ömür volatilite ölçüsü sonuçları, Bitcoin, Ethereum ve Tether USDt için sırasıyla 7 gün, 8 gün ve 74 gün olduğunu ortaya koymuştur. Bu durum, Bitcoin ve Ethereum’da yaşanan volatilitenin benzer sürede etkisinin kaybolduğunu, ama Tether USDt’de ise daha uzun sürdüğünü göstermektedir. Bunun sebebi Tether USdt kripto paranın stabil coin olmasıdır. Bu bağlamda, yatırımcılar ve portföy yöneticilerinin, kararlarını şekillendirirken kripto paraların asimetrik özellikleri ile oynaklık seviyelerini göz önünde bulundurmaları oldukça önemlidir.
Abstract Market efficiency assumes that prices in financial markets are perfectly informative and, therefore, it is not possible to design trading strategies that outperform the market. The concept of efficiency has important implications for financial stability and, consequently, for financial policies. If asset returns exhibit persistent or anti-persistent behavior, then predictability based on past returns might be possible, which would be a clear violation of the weak form of efficiency. Many studies rely on the Hurst exponent to evaluate the level of memory of financial returns, and the purpose of this paper is to show that long memory or anti-persistence of financial returns is not incompatible with the random walk model or the efficient market hypothesis (EMH). The use of the Hurst exponent to demonstrate the inefficiency of financial markets using common estimators is troublesome, especially when applied to financial returns, since values of $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> are not evidence against the random walk model or the EMH. Moreover, the high variability of Hurst exponent estimates and their dependence on the chosen algorithm should motivate careful use of this tool. This study proposes a simple theoretical explanation and an extensive simulation study to show that $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> for financial returns is perfectly compatible with the random walk model. As a robustness check, both the traditional rescaled range and the wavelet lifting algorithms are used. Applications to real data are also discussed to show that the empirical values of the Hurst exponent are in the range suggested by the simulations, providing evidence that over-reliance on the Hurst exponent could lead to erroneous rejection of the random walk model. Specifically, the paper presents an application to the daily returns of stock market indices (DJIA and S&P 500) over a period of more than 30 years and cryptocurrencies (Bitcoin and Ethereum) over a period of more than 5 years.
The study examines the relationship between volume and volatility in leading cryptocurrencies i.e. Bitcoin and Ethereum, within the framework of Mixture of Distribution Hypothesis (MDH). It accommodates structural shifts in the cryptocurrency prices and uses fat-tailed distributions. The results show that the MDH is rejected for both cryptocurrencies, and volume alone cannot explain the heteroskedasticity of returns; however, it acts as a significant predictor for volatility, especially when incorporating structural breaks in the model. Further, the forecasting performance improves when fat-tailed distributions, such as the skewed student’s t and Johnson’s Su distribution are used to model the innovations. Thus, volume holds important information in the crypto markets and can affect returns, thereby, raising concerns about market efficiency. Our results are robust across different periods, modelling approaches and forecasting horizons, and hold substantial implications for traders, market participants, regulators, and governments in designing effective policies.
Saralees Nadarajah, Jules Clément, Patrick Rakotomarolahy, Henri T. J. E. Ratolojanahary
The purpose of this study is to conduct an empirical comparative study of volatility models for three of the most popular cryptocurrencies. We study the volatility of the following cryptocurrencies: Bitcoin, Ethereum, and Litecoin. We consider the GARCH-type, boosting-family-tree-based ensemble learning, and ANFIS volatility models for these financial crypto-assets, which some have claimed capture stylized facts about cryptocurrency volatility well. We conduct comparative studies on in-sample and out-of-sample empirical analyses. The results show that tree-based ensemble learning delivers better forecast accuracy. Nevertheless, the performance of some GARCH-type volatility models is relatively close to that of the best model on both training and evaluation samples.