Blockchain Papers

Ali Yeganeh, Mohammad Sadjad, Jean‐Claude Malela‐Majika, Sollie Millard

No abstract is available for this record.

Rizqi Akbar Makarim, Desinta Maheswari, Aqila Dina Pramustiwi, Kartika Ayu Rahmawati · 5 authors

The volatility of cryptocurrency markets has increased substantially in recent years, particularly for Ethereum (ETH), which exhibits fat-tailed distributions and persistent volatility clustering that traditional linear models are unable to capture. This study aims to analyze and model the volatility of ETH/USD using high-frequency hourly data to determine the most appropriate volatility model for describing Ethereum’s intraday market dynamics. The dataset consists of 8,760 hourly closing prices from October 31, 2024 to October 31, 2025, obtained through the CryptoCompare API. The methodological framework includes data preprocessing, log-return transformation, stationarity analysis using the Augmented Dickey–Fuller test, detection of heteroskedasticity via the ARCH–LM test, and estimation of several ARCH and GARCH model specifications. The results show that ETH/USD returns are stationary, non-normally distributed, and exhibit clear volatility clustering. Among the ARCH models, only ARCH(1) adequately captures short-term fluctuations, while ARCH(2) provides no additional benefit. In contrast, GARCH models demonstrate superior performance in capturing both short-term shocks and long-term persistence. Based on AIC, BIC, and log-likelihood values, GARCH(1,2) emerges as the best-performing model, offering the highest flexibility in representing Ethereum’s persistent and reactive volatility patterns. These findings confirm that ETH/USD volatility is predictable and can be modeled statistically. Future research may incorporate asymmetric GARCH extensions or external explanatory variables to improve predictive performance.

Oksana Liashenko, Bogdan Adamyk, Oksana Adamyk

This paper examines the market maturation hypothesis in cryptocurrency markets through a three-stage analysis of the evolution of tail risk in Bitcoin (BTC) and Ethereum (ETH). Using daily closing prices from January 2015 to February 2026 for BTC (n = 4058) and November 2017 to February 2026 for ETH (n = 3015), we employ 365-day rolling windows—reflecting the continuous 24/7 operation of cryptocurrency markets—to trace the temporal dynamics of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Maximum Drawdown (MDD). The empirical strategy combines (i) Newey–West trend tests on rolling risk metrics, (ii) regime-conditional analysis across market states (Bull, Bear, or Neutral) and volatility regimes (high/low uncertainty), and (iii) exceedance correlation analysis to capture asymmetric BTC–ETH tail dependence. The results are consistent with the market maturation hypothesis: all ten trend coefficients across both assets are statistically significant (p < 0.001), with linear time trends explaining up to 46.8% (BTC VaR1%) and 67.5% (ETH VaR1%) of variation in rolling tail risk. Sub-period comparisons confirm economically meaningful declines—BTC VaR1% fell by 22.0% and ETH VaR1% by 26.6% between the early and late subsamples. However, maturation is markedly asymmetric across uncertainty regimes: tail-risk reductions concentrate in low-uncertainty periods, whereas BTC MDD in high-uncertainty regimes shows no significant improvement (+1.0%, p = 0.176). Excess correlation analysis reveals a persistent and widening downside asymmetry (ρ− = 0.847 vs. ρ+ = 0.246 at the 90th percentile), with late-period upper-tail correlation turning negative (ρ+ = −0.175 at the 95th percentile), implying that portfolio diversification within the cryptocurrency asset class remains illusory during market stress. These findings carry direct implications for institutional risk management, stress-testing frameworks, and prudential regulation of digital assets.

Keorapetse Leballo, Jules Clément

Bitcoin and major precious metals are frequently discussed as hedges against equity drawdowns, inflation surprises, and policy uncertainty, which implicitly assumes a degree of functional equivalence in their risk behavior. Existing work remains limited in assessing high-dimensional dependence structures in the Bitcoin and precious metals system, without relying on bivariate conditional risk measures or restrictive copula frameworks. This study therefore aims to quantify bilateral and multivariate tail dependence and systemic spillovers between Bitcoin and selected precious metals and to identify the best-performing multivariate tail-risk specification, with model comparison conducted using AIC and BIC. The analysis uses 3,665 daily observations of adjusted closing prices spanning January 2013 to September 2024, sourced from Yahoo Finance. Marginal returns are fitted with an ARFIMA–FIGARCH skewed-t model, while cross-asset tail dependence is estimated via vine-copula quantile regression (C- and D-vines) using a 252-day rolling window (one-day step) with 10,000 copula simulations. Results indicate that Bitcoin exhibits substantially larger downside systemic contributions than precious metals, whereas gold displays the smallest systemic risk profile. Across information criteria, the D-vine–based SCoVaR specification provides the best overall fit, indicating that vine-based multivariate tail-risk measures better characterize systemic spillovers between the cryptocurrency and traditionally defensive assets under extreme market conditions. These results motivate future research on broader cryptocommodity networks and macro-financial conditioning, while practitioners and regulators can use the D-vine SCoVaR to monitor and mitigate downside spillovers in mixed-asset portfolios.

Shikha Jain, Minal Shirure, Aakanksha Bedi, Kapil Kushwaha

The next generation of financial and economic infrastructure has been realized by Real-World Asset (RWA) tokenization, which represents physical and regulated assets on distributed ledgers. Regardless of increased institutional interest, its large-scale adoption is limited by the scalability, regulatory compliance, governance, and finality of settlement issues intrinsic to traditional systems. This paper suggests a fractional asset tokenization model based on ERC-1155 on the Hedera blockchain using its Permissioned-public governance system, deterministic finality, and native token services. The proposed system will enhance the liquidity of assets, their accessibility to the market, and their efficiency and profitability by facilitating compliant fractional ownership, which is consistent with the changing regulatory processes. When compared to Ethereum and Bitcoin, it shows that Hedera would be more appropriate to the requirements of institutional grade RWA tokenization.

Wenhao Zhang, Zhenpeng Tang, Xiaowen Zhuang, Yi Cai · 5 authors

The cryptocurrency market has attracted significant attention from global investors, with Cardano (ADA) ranking among the top cryptocurrencies by market capitalization. However, predicting ADA returns remains challenging due to the complex, multi-scale dynamics influenced by Federal Reserve policies, geopolitical events, and high-frequency trading. This study proposes a “Sliding EMD–Multi Variables” framework for cryptocurrency return prediction, leveraging Empirical Mode Decomposition’s multi-scale fractal properties to capture nonlinear dynamics at different time scales. The sliding window decomposition method addresses data leakage issues while incorporating key economic and policy variables at the component level. The empirical results demonstrate that the Sliding EMD system significantly outperforms univariate and multivariate benchmarks. Compared to the univariate system, it improves MSE, RMSE, SMAPE, and DSTAT by 0.83%, 0.42%, 5.23%, and 0.43%, respectively, while enhancing investment metrics (maximum drawdown, Sharpe ratio, Sortino ratio, Calmar ratio) by 0.19, 0.36, 0.95, and 0.15. Against the multivariate system, improvements reach 5.52%, 3.14%, 5.74%, and 17.62% in prediction accuracy, with investment performance gains of 0.47, 1.69, 4.27, and 0.31. Incorporating economic variables at the component level yields additional improvements of 0.94%, 0.47%, and 0.78% in MSE, RMSE, and MAE. These findings offer valuable insights for cryptocurrency portfolio optimization using fractal-based decomposition methods.

Ruchika Lochab, Luckshay Batra, HC Taneja

.This article extends the theoretical framework of Rényi extropy by establishing new properties, including its convergence to information extropy under limiting parameter conditions and its ability to assume both positive and negative values. Furthermore, it explores the interrelationships among Rényi, information, and Tsallis extropies, providing a unified perspective on these uncertainty measures. To demonstrate its practical utility, we apply Rényi extropy to analyze uncertainty in cryptocurrency markets, specifically Bitcoin (BTC) and Ethereum (ETH). Our findings reveal its superior capability in capturing non Gaussian dynamics and assessing risk compared to traditional entropy-based methods. Furthermore, we integrate machine learning techniques, including Extreme Gradient Boosting (XGBoost) and k-Nearest Neighbors (kNN) to predict BTC and ETH prices, validating the synergy between advanced statistical measures and computational forecasting. The predictive performance is evaluated using advanced XGBoost and k-NN models, assessed through RMSE and R2 metrics. The results demonstrate a significant improvement over traditional benchmarks, including Shannon entropy and ARIMA, in forecasting risk-adjusted returns. The empirical results underscore Rényi extropy’s potential as a robust tool for financial market analysis and risk management.

Tsolmon Sodnomdavaa

This study investigates directional causality between Bitcoin and gold across different market conditions. Rather than relying on mean-based dependence, we examine how causal effects vary across return quantiles, investment horizons, and market regimes. To address this question, we apply a Causal–Frequency–Quantile–Regime (CFQR) framework. The approach combines frequency-domain Granger causality, quantile-based non-causality tests, and endogenous regime classification within a unified setting. Macroeconomic controls are included to reduce omitted variable bias. Statistical inference relies on bootstrap procedures with false discovery rate correction to account for multiple testing. Using daily data from 2013 to 2025, we find that the full-sample directional dominance between Bitcoin and gold is generally weak after multiple testing adjustments. However, under stress regimes, the causal relationship of gold to Bitcoin becomes more pronounced at longer investment horizons. Under normal conditions, causal effects remain unstable and fragmented. Economic effects are modest. Variance-based hedging gains are limited, while downside risk measures show moderate improvement during stress periods. Overall, the evidence suggests that gold does not serve as a universal hedge for Bitcoin, but may exert conditional informational influence during high-uncertainty states. The CFQR framework provides a structured way to identify such state-dependent causal patterns.

Hana Belhadj, Salah Ben Hamad, Nadia Belkhir

Abstract This paper investigates whether Bitcoin serves as a safe haven and a diversification tool for both developed and emerging stock markets during the COVID-19 crisis, in comparison with gold. The analysis covers daily data from June 18, 2012, to May 25, 2020, across a representative set of developed (S&P500, FTSE100, DAX, CAC40, Nikkei225, Ibex35) and emerging (Shanghai, Nifty50, Ibovespa, MOEX) equity markets, providing a comprehensive view of asset interactions in different financial environments. Methodologically, we employ a two-step approach: an EGARCH model to estimate time-varying volatility, followed by a copula-based framework to capture nonlinear and asymmetric dependence structures. This combination allows for a nuanced assessment of asset behavior under both tranquil and crisis conditions. The results show that Bitcoin maintains weak dependence on developed equity markets during the COVID-19 period but fails to display consistent safe-haven characteristics under extreme stress. Gold, by contrast, continues to act as a reliable hedge, confirming its traditional role in protecting portfolios against market downturns. Overall, these findings suggest that while Bitcoin may provide diversification benefits under normal circumstances, it cannot yet replace gold as a robust safe-haven asset. For portfolio managers, this highlights the importance of gold in risk management, while underscoring Bitcoin’s evolving yet still uncertain role in global financial markets.

Sudip Giri, Mario G. Beruvides, Dongping Du

Financial assets are central to economic stability, yet the macroeconomic sensitivity and predictability of emerging digital assets, particularly non-fungible tokens (NFTs), remain unclear. This study evaluates the responsiveness of NFTs, cryptocurrencies, and traditional assets to interest rate and inflation fluctuations using time series forecasting and sensitivity analysis. ARIMAX, Partial Least Squares, Ridge Regression, and Long Short-Term Memory (LSTM) models are employed to capture linear and nonlinear dynamics across asset classes. Using daily data from July 2017 to November 2024, results indicate that LSTM achieves superior predictive accuracy for highly volatile and nonlinear assets, although forecast reliability is limited by structural breaks and thin trading. Traditional assets such as bonds and gold display stable sensitivities to macroeconomic variables, reinforcing their hedging role. In contrast, digital assets exhibit higher volatility and weaker, less stable macroeconomic linkages. NFTs show low correlations with traditional assets, suggesting diversification potential, but low forecast error variance does not imply low risk. Cryptocurrencies demonstrate stronger macroeconomic sensitivity alongside greater instability. Overall, the findings reveal a trade-off between diversification benefits and forecast reliability when integrating digital assets into portfolios.

Najaf Iqbal, Muhammad Abubakr Naeem, Hang Luo, Walid Bakry

Using 5-minute data of 16 cryptocurrency tokens belonging to 5 different categories (AI, Gaming, Meme, Layer 1/2, and FAN tokens), we investigate the risk transmission in higher moments, i.e. realized volatility (RV), realized skewness (RS), and realized kurtosis (RK), employing the TVP-VAR framework and robustness tests. We also perform six sub-sample investigations on various geopolitical and other systemic events. Ethereum, Binance Coin, and Ripple are strongly related to other tokens. Sandbox, Decentraland, and Enjin Coin lead spillover transmission, while Numeraire, Measurable Data Token, and Cryptex Finance absorb most of the shocks. The connections are stronger regarding RV than RS and RK, showing potential for tail-risk reduction, which is heterogeneous regarding extreme events. AI tokens are the least connected during normal conditions as well as most of the extreme events, except the US presidential Election, which puts these tokens in the centre of the system. The Israel-Palestine war, the FTX collapse, and the SEC approval of the first Bitcoin ETF are among the most important events regarding enhancement in the higher-moment risk transmission. Token market investors/traders and regulators can draw essential insights from our findings.

Arsenios-Georgios N. Prelorentzos, Pavlos Koulmas, Panos Xidonas, Stéphane Goutte · 5 authors

Cet article examine les répercussions macrofinancières entre les principales actions technologiques américaines et les principales cryptomonnaies en appliquant un cadre d’autorégression vectorielle quantile (QVAR). À l’aide de données quotidiennes couvrant la période de novembre 2017 à octobre 2023, nous examinons la connectivité dynamique entre les classes d’actifs à différents points de la distribution des rendements, en mettant particulièrement l’accent sur les extrémités, où le risque systémique s’amplifie généralement. Nos conclusions révèlent que, si la connectivité moyenne est importante, l’intensité des répercussions est nettement asymétrique et concentrée dans les quantiles extrêmes, ce qui indique une interdépendance accrue pendant les périodes de tension ou d’exubérance financières. Les cryptomonnaies agissent comme des transmetteurs nets de volatilité vers certaines actions technologiques dans des conditions de marché défavorables, bien qu’elles ne présentent que des retombées limitées en temps normal. Ces résultats soulignent l’importance macroéconomique croissante des actifs numériques et la nécessité d’intégrer des mesures basées sur les quantiles dans les cadres de surveillance macroprudentielle, suggérant que les modèles de tests de résistance et les cadres de stabilité financière doivent être accompagnés de ces modèles d’interconnexion des extrémités, visant à évaluer régulièrement les dépendances entre les extrémités des différents marchés. Enfin, le document fournit des informations importantes aux banques centrales, aux décideurs politiques et aux investisseurs institutionnels, en ce qui concerne la conception d’un suivi macrofinancier complexe, les techniques de gestion des risques et l’allocation des actifs. Classification JEL : C32, C58, D53, E44, E60, G10

O.O. Amam, M.T. Nwakuya, M.A. Ijomah

This study investigates the volatility behaviour of Ethereum (Coinbase) returns using the Generalized Autoregressive Heteroskedasticity GARCH (1,1) model under three distributional assumptions: Normal, Student-t, and the Generalized Error Distribution (GED). Cryptocurrency markets are characterized by extreme price swings, heavy-tailed behaviour, and persistent volatility, making traditional constant-variance models ineffective. Descriptive statistics reveal strong deviations from normality in Ethereum returns, with high kurtosis (7.8454) and an extremely large Jarque–Bera statistic (1797.182 with its p-value less than 5%), indicating excess tail risk and frequent extreme movements. Preliminary analysis reveal that the return series is stationary, free from serial correlation, but exhibits significant ARCH effects, justifying the use of conditional heteroskedasticity models. Empirical results show highly persistent volatility across all models, with α + β values close to unity: approximately 0.99 under the Normal distribution, 1.01 under the Student-t specification, and 0.994 under GED distribution. Model comparison reveals that heavy-tailed error structures outperform the Normal model, with GED achieving the lowest AIC (−3.781), SIC (−3.7629), HQC (−3.7743), and the lowest MAPE (114.6606). These findings demonstrate that flexible distributional assumptions greatly enhance the modelling of extreme and persistent volatility in Ethereum returns. The study emphasizes the importance of adopting heavy-tailed GARCH frameworks when analysing cryptocurrency risk and forecasting volatility.

Cevi Herdian

A Long Short-Term Memory (LSTM) neural network trained on hourly ETH/USDT market data from the Binance exchange is used in this study to examine short-term Ethereum price behavior. The proposed model emphasizes learning temporal dependencies and momentum-driven structures rather than relying on conventional linear forecasting assumptions, acknowledging the highly nonlinear and noise-dominated nature of cryptocurrency markets. The daily high price of Ethereum is selected as the target variable in the forecasting task, which is defined as a univariate regression problem. To ensure realistic predictive assessment, model performance is evaluated using a strictly out-of-sample testing methodology. Empirical findings demonstrate that the LSTM model achieves a strong statistical fit despite significant market volatility. The obtained results—RMSE of 127.33, MAE of 98.76, MSE of 16,213.76, MAPE of 2.73%, and an R² of 0.96—indicate that a substantial portion of short-term price volatility is effectively captured by the nonlinear architecture. Even in a noise-dominated market, the low MAPE and high coefficient of determination suggest robust predictive alignment. Forecasts over the next five days reveal a recurring short-term directional pattern accompanied by widening prediction intervals, which reflect increasing uncertainty as the forecast horizon extends. This pattern underscores the intrinsic difficulty of achieving accurate price-level forecasts in highly volatile cryptocurrency markets. Overall, when applied to short-term cryptocurrency price dynamics, the results indicate that LSTM models are well-suited for capturing trend persistence and regime-related signals, affirming their usefulness as risk-aware decision-support tools rather than deterministic forecasting systems.

Lihki Rubio, Keyla Alba, Carlos E Velásquez, Filipe R. Ramos

Accurately forecasting Bitcoin’s conditional variance is essential for reliable Value-at-Risk (VaR) estimation yet remains challenging due to nonlinear dynamics, volatility clustering, and heavy-tailed return distributions. This study developed a novel stacking ensemble that integrates econometric and machine-learning models through XGBoost meta-learning to produce improved variance forecasts. Hybrid ML–GARCH specifications are incorporated separately to enrich the comparative analysis. All estimators are trained with time-aware cross-validation to ensure temporal coherence and prevent look-ahead bias. Using Bitcoin data from 2014 to 2020, the empirical results show that the stacking ensemble consistently outperforms both standalone and hybrid alternatives in conditional variance forecasting and VaR accuracy, including during periods of severe market stress such as the COVID-19 episode. Residual diagnostics confirm that the ensemble effectively captures persistent temporal dependencies in volatility dynamics. Overall, the proposed methodology offers an innovative and interpretable risk-management tool for financial institutions, combining statistical rigor with the adaptability of machine-learning techniques in digital asset markets.

Necati Altemur, İbrahim Halil Ekşi̇, Rizky Yudaruddin

Purpose This study aims to provide a comprehensive examination of the nonlinear and asymmetric relationships between global uncertainty indicators, namely, gold (GOLD), the US Dollar Index (DXY) and the Volatility Index (VIX), and major cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Cardano (ADA) and Binance Coin (BNB). It particularly focuses on how these dynamics evolve across different market conditions and the extent to which certain cryptocurrencies function as alternative safe-haven assets. Design/methodology/approach The analysis uses weekly data from January 2018 to June 2025, covering five major cryptocurrencies (BTC, ETH, XRP, ADA and BNB). To capture the dynamic and nonlinear relationships between global uncertainty indicators and cryptocurrency markets, the Quantile-on-Quantile Regression (QQR) approach is applied. Furthermore, the Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) technique is used as a robustness check to validate the findings. Findings The results demonstrate that the relationship between global uncertainty indicators and cryptocurrencies is neither linear, stationary nor unidirectional. Instead, it exhibits complex and asymmetric interactions that vary across quantiles and market conditions. Significant and predominantly inverse relationships are identified between the DXY, the VIX and cryptocurrencies, particularly at lower (0.05–0.30) and higher (0.70+) quantile levels. These findings suggest that investor behavior is influenced not only by economic fundamentals but also by uncertainty, market dynamics and risk perceptions. Originality/value This study is the first to apply QQR and QQKRLS methods to analyze the nonlinear and asymmetric linkages between global uncertainty indicators and major cryptocurrencies. It provides novel evidence on how these relationships shift across market conditions, offering fresh insights into the potential safe-haven role of cryptocurrencies.

A. H. Nzokem, Daniel Maposa

This paper investigates extreme risk in cryptocurrency markets by comparing Bitcoin and Ethereum daily returns with those of S&P 500 and SPY ETF. Using the Generalized Tempered Stable (GTS) distribution to model heavy tails and Quantile-Quantile (Q-Q) plots to assess fitness, we find that all assets deviate sharply from normal distribution. Within this framework, Ethereum exhibits a higher frequency of extreme returns than Bitcoin, highlighting differences in risk profiles even among leading cryptocurrencies.

Yosep Na, Jun Young Byun, Jungyoon Song, Jungyoon Song · 6 authors

No abstract is available for this record.

Jackie Zhanbiao Li, Jacie Jia Liu

Abstract With the introduction of spot Ethereum ETFs, Ethereum plays an increasingly important role in the cryptocurrency market. In this paper, we propose a Bayesian modelling framework incorporating a mixture copula for co-modelling Ethereum returns with Bitcoin or FTSE 100 returns. The mixture copula is designed as a combination of the Clayton copula and its three rotations, Frank, and Gaussian copulas. It provides substantial flexibility for handling a variety of dependency structures. The Bayesian approach offers the advantage of jointly estimating both the margins and copulas and simulating future returns in a coherent procedure. Using 10 different risk or risk-return measures, we provide updated empirical evidence on Ethereum’s role in both cryptocurrency and mixed portfolios. The analysis not only evaluates its diversification potential numerically but also sheds light on how the optimal allocations vary across distinct risk preferences and portfolio objectives. Moreover, based on the data of 2017–2024, we estimate that Ethereum futures has a hedging effectiveness on Bitcoin of about 30–40% across different risk preferences. Beyond these findings, the Bayesian mixture copula framework represents a methodological contribution to the modelling of complex dependence structures between financial returns. Taken together, our study delivers new insights that are particularly relevant in light of the evolving cryptocurrency landscape and the increasing integration of digital assets into mainstream investment practice.

Vitor Fonseca Machado Beling Dias, Rodrigo Fernandes Malaquias

In this study, we evaluated the returns and return volatility of a Brazilian stablecoin linked to fertilizers during periods preceding its discontinuation. In light of the safe haven literature, we also tested the correlation between this stablecoin and a traditional cryptocurrency, Bitcoin, and modeled its behavior during periods of Bitcoin’s extreme returns. In terms of methodology, we employ GARCH-family models (including DCC-GARCH) to analyze daily data from 1 December 2022 to 16 January 2025. We also employ an analysis using Large Language Models (LLMs), evaluating the stablecoin time series considering the period of its discontinuation. The results indicated that as the discontinuation date approached, the stablecoin exhibited statistically significant lower returns and higher volatility. While the DCC-GARCH indicated no correlation between the assets, we found that the stablecoin’s returns exhibited a negative relationship with Bitcoin’s extreme returns, challenging its potential efficacy as a safe haven. This article offers practical contributions for digital asset investors, indicating that even physically backed stablecoins, designed for stability, are subject to significant volatility, idiosyncratic risks, and potential discontinuation.

Yong Tang, Mrs Faryal, Ifran Khan

This study uses the Diebold-Yilmaz (2012) and Baruník-Křehlík (2018) frameworks to examine time-varying volatility spillovers among five key rare earth minerals, cryptocurrencies, and macroeconomic uncertainty indexes. Our results reveal considerable cross-market spillovers (31.75% of total variance), which are short-term (29.97%, 1–4 days) in nature and over 50% during the COVID-19 pandemic. Ethereum (70.99%) and bitcoin (66.58%) emerge as predominant short-term transmitters, whereas dysprosium (31.45%) has a more long-lasting, cross-horizon effect. Macroeconomic uncertainty indices act as net recipients. This increased short-run spillover requires forward-looking macroeconomic policy and integrated risk management directed at cryptocurrency and strategic rare earths for financial stability.

Hongjun Zeng, Abdullahi D. Ahmed

Abstract The purpose of this study was to assess the dependence structure and volatility connectedness among the COVID-19 crisis, the 2022 Russia–Ukraine war, and their influence on cryptocurrencies, crude oil, developed markets, and the equity markets of China and ASEAN countries under varying market conditions. The analysis segmented the sample into three distinct periods: pre-COVID-19, during COVID-19, and the 2022 Russia–Ukraine conflict. To assess the dependence structure and risk spillover patterns across the markets for each period, we employed the generalized autoregressive conditional heteroskedasticity (GARCH)-extreme value theory (EVT)-vine copula and quantile vector autoregression (QVAR) connectedness methodologies. Findings from our GARCH-EVT-Vine-Copula model indicated that subsequent to the outbreak of COVID-19, market portfolios associated with the MSCI-developed markets index demonstrated significantly lower tail connectedness. However, the impact of the 2022 Russia–Ukraine war on the stock markets of China and ASEAN countries was found to be overestimated. Furthermore, the QVAR connectedness analysis revealed that connectedness was greater in bullish market conditions than in normal and extreme downside periods. Additionally, the portfolio analysis results suggested that the equity markets of China and ASEAN countries, along with the crude oil markets, cryptocurrency indices, and the MSCI developed markets index, were unable to achieve high levels of hedging effectiveness. Concurrently, it was recommended that investments be directed toward Chinese and ASEAN equities as safe-haven assets.

Amin Shakourloo, Asil Azimli

No abstract is available for this record.

Vincent Adela, Samuel Duku Yeboah, David Korsah, Michael Provide Fumey · 6 authors

Geopolitical crises pose major risks to financial stability, but their implications for digital assets remain poorly understood. While prior studies suggest that cryptocurrencies may act as hedges or highly volatile speculative instruments during periods of uncertainty, the evidence remains inconclusive. This study examines how major cryptocurrencies reacted to geopolitical risk during the Russia–Ukraine war by employing the quantile-on-quantile regression (QQR) method on daily data from February 1 to August 8, 2022. The results reveal heterogeneous and nonlinear effects: Bitcoin (BTC) and Ethereum (ETH) exhibit partial hedging properties under moderate geopolitical risk, whereas alternative cryptocurrencies such as Binance Coin (BNB), Cardano (ADA), and Dogecoin (DOGE) display heightened vulnerability. Stablecoins exhibit contrasting roles, with USD Coin (USDC) acting as a safe haven, whereas Tether (USDT) consistently loses value under periods of uncertainty. These findings underscore that the safe-haven potential of cryptocurrencies is conditional on both market states and the type of asset, highlighting their asymmetry in times of crisis. By clarifying the dynamic role of cryptocurrencies during geopolitical shocks, the study contributes to the debate on whether digital assets enhance diversification or amplify instability, offering practical insights for investors and policymakers seeking resilient risk management strategies.