Blockchain Papers

Ismail Jirou, Ikram Jebabli, Mohammad Isleimeyyeh, Elie Bouri

No abstract is available for this record.

Wafa Kammoun Masmoudi, Olfa El Aoun, Wael Dammak

No abstract is available for this record.

Ji-Won Kang, Daihyun Kwon, Sun‐Yong Choi

This study proposes a hybrid model that integrates Wavelet frequency decomposition, convolutional neural networks (CNNs), and Transformers to predict correlation structures among eight major cryptocurrencies. The Wavelet module decomposes asset time series into short-, medium-, and long-term components, enabling multi-scale trend analysis. CNNs capture localized correlation patterns across frequency bands, while the Transformer models long-term temporal dependencies and global relationships. Ablation studies with three baselines (Wavelet–CNN, Wavelet–Transformer, and CNN–Transformer) confirm that the proposed Wavelet–CNN–Transformer (WCT) consistently outperforms all alternatives across regression metrics (MSE, MAE, RMSE) and matrix similarity measures (Cosine Similarity and Frobenius Norm). The performance gap with the Wavelet–Transformer highlights CNN’s critical role in processing frequency-decomposed features, and WCT demonstrates stable accuracy even during periods of high market volatility. By improving correlation forecasts, the model enhances portfolio diversification and enables more effective risk-hedging strategies than volatility-based approaches. Moreover, it is capable of capturing the impact of major events such as policy announcements, geopolitical conflicts, and corporate earnings releases on market networks. This capability provides a powerful framework for monitoring structural transformations that are often overlooked by traditional price prediction models.

Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek

Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of market inefficiencies, may improve volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, in the present contribution a recently proposed method of disentangling sources of multifractality in time series was applied to the most representative instruments from the digital market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality are temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series.

Aubain Nzokem, Daniel Maposa

The Generalized Tempered Stable (GTS) distribution extends classical stable laws through exponential tempering, preserving the power-law behavior while ensuring finite moments. This makes it especially suitable for modeling heavy-tailed financial data. However, the lack of closed-form densities poses significant challenges for simulation. This study provides a comprehensive and systematic comparison of GTS simulation methods, including rejection-based algorithms, series representations, and an enhanced Fast Fractional Fourier Transform (FRFT)-based inversion method. Through extensive numerical experiments on major financial assets (Bitcoin, Ethereum, the S&P 500, and the SPY ETF), this study demonstrates that the FRFT method outperforms others in terms of accuracy and ability to capture tail behavior, as validated by goodness-of-fit tests. Our results provide practitioners with robust and efficient simulation tools for applications in risk management, derivative pricing, and statistical modeling.

Ahlem Lamine

This study provides an in-depth analysis of the dynamic connectedness between G7 stock market indices, traditional cryptocurrencies (Bitcoin, Ethereum), gold, digital gold (PAXG, XAUT), and companies specializing in artificial intelligence (AI). Covering the period from 2020 to 2024, the analysis focuses on four distinct periods: the COVID-19 pandemic, the Russia-Ukraine conflict, the banking crisis triggered by the collapse of Silicon Valley Bank in March 2023 and the speculative rise in the gold markets in 2024. The methodology employs a Quantile Vector Autoregressive (QVAR) connectivity approach, starting with the median quantile and systematically extending to various quantiles to capture the entire distribution of connectedness under different market conditions. Our results reveal significant fluctuations in the Total Connectivity Index (TCI) during the studied crises and demonstrate how the roles of key assets—Bitcoin, Ethereum, gold, PAXG, XAUT, and AI firms—shift between being net emitters and receivers of shocks. These shifts underscore the importance of asset selection in crafting effective hedging strategies. Specifically, we observe that G7 investors adopt varying diversification strategies depending on their domestic market conditions and the specific crisis period. The study highlights that assets for diversification and risk reduction vary by country and crisis. Traditional cryptocurrencies and AI companies in general emerge as promising diversification tools, especially in times of technological disruption and economic uncertainty. Several financial implications for investors and policymakers are proposed, providing insights for optimizing portfolio resilience in the face of global market volatility.

Imran Khan, Sami Ur Rahman

No abstract is available for this record.

Arthur Rodrigues Pereira de Carvalho, Felipe Quintino, Helton Saulo, Luan Carlos de Sena Monteiro Ozelim · 6 authors

This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture long memory, paired with both constant-volatility (CONST) and stochastic-volatility specifications via the Cox–Ingersoll–Ross (CIR) process. The novel family of models is based on Generalized Ornstein–Uhlenbeck processes with a fluctuating exponential trend (GOU-FE), which are modified to account for multiplicative fBm noise. Traditional Geometric Brownian Motion processes (GFBM) with either constant or stochastic volatilities are employed as benchmarks for comparative analysis, bringing the total number of evaluated models to four: GFBM-CONST, GFBM-CIR, GOUFE-CONST, and GOUFE-CIR models. Estimation by numerical optimization and evaluation through error metrics, information criteria (AIC, BIC, and EDC), and 95% Expected Shortfall (ES95) indicated better fit for the stochastic-volatility models (GOUFE-CIR and GFBM-CIR) and the lowest tail-risk for GOUFE-CIR, although residual analysis revealed heteroscedasticity and non-normality. For intraday data, Exponential, Weibull, and Generalized Gamma Autoregressive Conditional Duration (ACD) models, with adjustments for intraday patterns, were applied to model the time between transactions. Results showed that the ACD models effectively capture duration clustering, with the Generalized Gamma version exhibiting superior fit according to the Cox–Snell residual-based analysis and other metrics (AIC, BIC, and mean-squared error). Overall, this work advances the modeling of Bitcoin prices by rigorously applying and comparing stochastic frameworks across temporal scales, highlighting the critical roles of long memory, stochastic volatility, and intraday dynamics in understanding the behavior of this digital asset.

Fahad Ali, Muhammad Usman Khurram

No abstract is available for this record.

Konti, Kristiana Nikoletta

Η παρούσα μεταπτυχιακή διατριβή εξετάζει τη δυναμική της μεταβλητότητας στις αγορές κρυπτονομισμάτων και αναπτύσσει προβλεπτικά μοντέλα για την εις βάθος κατανόηση της συμπεριφοράς των τιμών σε ένα έντονα κερδοσκοπικό περιβάλλον. Με βάση δεδομένα από τέσσερα βασικά κρυπτονομίσματα, συγκεκριμένα, το Bitcoin (BTC), το Ethereum (ETH), το Litecoin (LITE) και το Dogecoin (DOGE) εφαρμόζονται προηγμένες οικονομετρικές μέθοδοι για την αποτύπωση κρίσιμων χαρακτηριστικών, όπως η συσσώρευση μεταβλητότητας, οι βαριές ουρές κατανομής και οι ασύμμετρες αντιδράσεις σε εξωγενείς διαταραχές. Το μεθοδολογικό πλαίσιο περιλαμβάνει μοντέλα GARCH(1,1) και EGARCH(1,1) με κατανομή GED, ARIMA - GARCH για τον από κοινού προσδιορισμό μέσης τιμής και διακύμανσης, GARCH-X με ενσωμάτωση μακροοικονομικών μεταβλητών, Στοχαστικά Μοντέλα Μεταβλητότητας Bayes (SV), καθώς και ARFIMA για τη διερεύνηση μακροχρόνιας μνήμης.Τα εμπειρικά αποτελέσματα δείχνουν ότι η μεταβλητότητα παραμένει ιδιαίτερα υψηλή σε όλα τα υπό εξέταση κρυπτονομίσματα, με εμφανή φαινόμενα μόχλευσης στο BTC, DOGE και LITE, ενώ το ETH εμφανίζει σχεδόν συμμετρικές αντιδράσεις. Η ενσωμάτωση μακροοικονομικών μεταβλητών αναδεικνύει τον καθοριστικό ρόλο του Δείκτη Τιμών Καταναλωτή (CPI), της προσφοράς χρήματος (Μ2) και των τιμών ενέργειας και τεχνολογίας, κυρίως στην περίπτωση του BTC, επιβεβαιώνοντας τη διασύνδεση των αγορών κρυπτονομισμάτων με τις διεθνείς χρηματοοικονομικές συνθήκες. Οι προβλέψεις ενός βήματος δείχνουν ότι BTC και LITE επιτυγχάνουν την υψηλότερη ακρίβεια, το ETH εμφανίζει μέτριο επίπεδο ακρίβειας, ενώ το DOGE παραμένει ιδιαίτερα απαιτητικό στη μοντελοποίηση, με τον έλεγχο VaR να καταδεικνύει συστηματική υπερεκτίμηση του καθοδικού κινδύνου. Τα Στοχαστικά Μοντέλα Μεταβλητότητας Bayes αποδεικνύονται πιο αποτελεσματικά από τα GARCH στην αποτύπωση αιφνίδιων μεταβολών και αλλαγών καθεστώτος, ενώ τα αποτελέσματα ARFIMA επιβεβαιώνουν περιορισμένη μακροχρόνια μνήμη για BTC και LITE, ήπια επιμονή για ETH και σχεδόν μηδενική για DOGE.Τα ευρήματα υπογραμμίζουν τη σημασία ευέλικτων, ασύμμετρων και βαριάς ουράς μοντέλων για ακριβή πρόβλεψη της μεταβλητότητας στις αγορές κρυπτονομισμάτων. Η μελέτη συμβάλλει στη διεθνή βιβλιογραφία μέσω ολοκληρωμένης συγκριτικής ανάλυσης μοντέλων μεταβλητότητας και προσφέρει πρακτικές κατευθύνσεις σε επενδυτές, διαχειριστές κινδύνου και φορείς χάραξης πολιτικής. Ειδικότερα, προτείνεται η αξιοποίηση προηγμένων μοντέλων για εκτίμηση κινδύνου, η ενσωμάτωση μακροοικονομικών δεικτών στο πλαίσιο πρόβλεψης, καθώς και η υιοθέτηση μετρικών ευαίσθητων στις ουρές κατανομής, όπως το Expected Shortfall, για τη διαχείριση ιδιαίτερα κερδοσκοπικών περιουσιακών στοιχείων.

Florentin Şerban, Silvia Dedu

Traditional portfolio optimization models, rooted in the mean–variance framework of Markowitz, rely heavily on variance as a risk measure. Although theoretically elegant, this approach becomes fragile in volatile and structurally unstable markets such as cryptocurrencies, where return distributions deviate significantly from normality, cor-relations are unstable, and concentration risk emerges. These limitations have motivated the search for alternative frameworks capable of capturing uncertainty in a more flexible and distribution-free manner. Entropy, originally introduced by Shannon as a measure of information, has gradually been recognized in the financial literature as a suitable proxy for diversification and systemic uncertainty. To address the shortcomings of variance-based models, this paper introduces the Weighted Shannon Entropy (WSE) model as a diversification-oriented alternative. By extending the classical Shannon entropy with asset-specific informational weights, the WSE framework provides additional flexibility for modeling heterogeneous asset char-acteristics, such as liquidity, informational value, or perceived reliability. Using the principle of maximum entropy and the method of Lagrange multipliers, we derive ex-ponential-form solutions for portfolio weights that naturally discourage concentration, ensure balanced allocations, and remain analytically tractable. The methodology is validated empirically on a portfolio of four leading cryptocurren-cies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—using market data from January to March 2025. The results demonstrate that the entropy-based optimization framework produces well-diversified portfolios, robust to volatility and structural instability, and provides a distribution-free alternative to the classical mean–variance model. Beyond its empirical performance, the WSE formulation highlights the conceptual advantage of entropy in integrating return, risk, and diversification into a single unified framework. The paper contributes both theoretically and practically: it strengthens the mathematical foundation of entropy-based portfolio selection, extends its applicability to digital asset markets, and illustrates how weighting schemes can enrich the classical Shannon measure. Future research may extend this approach to multi-period optimization, gen-eralized entropies such as Tsallis and Kaniadakis, or integration with machine learning models for dynamic portfolio management.

D. Kim, Frederique J. Vanheusden, Amee Kim

This study is the first to integrate recurrence plots, recurrence quantification analysis (RQA) and short-time Fourier Transform (STFT) to predict cryptocurrency market behaviour. Recurrence plots, RQA statistics and STFT spectrograms were calculated from return data and used as input in random forest algorithms as they are optimal tools for identifying non-linear dynamics in market data and analyse their frequency. Our optimised XGBoost algorithm provided a forecasting AUC above 76.7% and accuracy of 70% in predicting increasing or decreasing returns. This highlights the model’s ability to support cryptocurrency investment decision-making within an interpretable machine learning framework.

Bharat Sethi

In the scholarly literature on financial services, the prevailing paradigm over the past century was the supply of lending, borrowing, trading, and investment by regulated intermediaries, i.e. mainly banks, brokers and exchanges. These are institutions, which operate under a known regulatory structure that assumes homogeneity in their operations. Most recently, the fusion of an entirely new set of technologies such as Decentralized Finance (DeFi) has been changing the sector in a very fundamental way. Blockchain technology currently provides a framework, with the help of which financial applications can perform the transaction process automatically without the existence of central power and, thus, create an environment that is transparent and available.

Grzegorz Dudek, Witold Orzeszko, Piotr Fiszeder

Cryptocurrency markets are characterized by ex-treme volatility, making accurate forecasts essential for effective risk management and informed trading strategies. Traditional deterministic (point) forecasting methods are inadequate for capturing the full spectrum of potential volatility outcomes, underscoring the importance of probabilistic approaches. To address this limitation, this paper introduces probabilistic fore-casting methods that leverage point forecasts from a wide range of base models, including statistical (HAR, GARCH, ARFIMA) and machine learning (e.g. LASSO, SVR, MLP, Random Forest, LSTM) algorithms, to estimate conditional quantiles of cryp-tocurrency realized variance. To the best of our knowledge, this is the first study in the literature to propose and systematically evaluate probabilistic forecasts of variance in cryptocurrency markets based on predictions derived from multiple base models. Our empirical results for Bitcoin demonstrate that the Quantile Estimation through Residual Simulation (QRS) method, partic-ularly when applied to linear base models operating on log-transformed realized volatility data, consistently outperforms more sophisticated alternatives. Additionally, we highlight the robustness of the probabilistic stacking framework, providing comprehensive insights into uncertainty and risk inherent in cryptocurrency volatility forecasting. This research fills a sig-nificant gap in the literature, contributing practical probabilistic forecasting methodologies tailored specifically to cryptocurrency markets.

Makoto Nakakita, Tomoki Toyabe, Teruo Nakatsuma

This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributions—normal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gamma—were considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volume–volatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management.

Lingshan Du, Ji Shen

ABSTRACT We propose a novel option pricing model that explicitly incorporates volatility‐of‐volatility (VOV) dynamics and its associated risk premium. Our framework integrates realized variance and realized quarticity to capture latent VOV dynamics, addressing key challenges in cryptocurrency option pricing. Using Fourier inversion methods, we derive a closed‐form pricing formula for European‐style options. Empirical analysis with high‐frequency cryptocurrency option data shows that our model improves pricing accuracy, reducing implied volatility errors by 8.55% compared to benchmark models. The model outperforms benchmarks across all moneyness levels, remains robust for both short‐ and long‐maturity contracts, and maintains accuracy under high volatility. This study contributes to the literature by introducing a tractable and empirically validated approach to cryptocurrency option pricing through explicit VOV modeling.

Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali

No abstract is available for this record.

Jung‐Bin Su

This study utilizes version 6 of the regression analysis of time series (RATS) software package to implement the estimation of the bivariate diagonal generalized autoregressive conditional heteroscedasticity (GARCH) model combined with a composite asset selection approach including two hybrid performance measures to solve ‘the trade-off problem between return and risk’ and ‘the inconsistent results from different performance measures’ in the problem of asset allocation within a group of minimum variance portfolios during the pre-COVID-19 and COVID-19 periods. Empirical results show that the optimal portfolios obtained from this approach and the assets added to a portfolio to achieve better performance differ between the pre-COVID-19 and COVID-19 periods. For instance, the optimal portfolios are the Chinese yuan-Ethereum and Bitcoin-Ethereum for the pre-COVID-19 period, but the WTI-Ethereum for the COVID-19 period. To achieve better performance, we added Ethereum to our portfolio during the pre-COVID-19 period, while WTI and Bitcoin were added during the COVID-19 period. Thus, the COVID-19 pandemic had a significant impact on the performance of asset allocation in the three markets. The proposed approaches in this study can be embedded in a computer as an asset allocation algorithm of Robo-advisers.

Remy Jonkam Oben, Mehdi Seraj, Şerife Zihni Eyüpoğlu

Purpose The evolution of financial technology has been rapid, culminating in the mainstream acceptance and adoption of blockchain technology over the past decade. By providing the foundational infrastructure on which smart contracts and decentralized applications can be built and operated, the Ethereum blockchain facilitated the emergence of decentralized finance (DeFi). Not only have DeFi instruments increased portfolio options for investors, but they also have the potential to influence volatility transmissions both in traditional financial markets and within the digital space. To better inform policymaking, risk management and portfolio construction, this study aims to investigate both the time-based and frequency-based volatility connectedness among four leading DeFi instruments and 12 traditional financial markets. Design/methodology/approach This study analyzes weekly price data ranging from October 05, 2020, to March 04, 2024. The study employs advanced econometric frameworks (Diebold–Yilmaz and Baruník–Krehlík models) to estimate both the time-based and frequency-domain volatility connectedness among the studied financial instruments. Findings Empirical results show that the DeFi instruments are highly interconnected, the very-large financial markets are highly interconnected and there are low connections between DeFi instruments and traditional financial markets. Moreover, the larger (smaller) stock markets are net volatility transmitters (receivers). Overall, the volatility connectedness among all the studied instruments is moderate (48.4% on average), with the instruments being most (least) connected in the long (short) term. Originality/value This study expands the literature by including major DeFi assets that have been largely overlooked. Also, the study introduces novelty by incorporating global markets. In fact, to the best of the authors’ knowledge, it is the first study to analyze both time- and frequency-based volatility connectedness among DeFi assets and global financial markets.

Remy Jonkam Oben, Mehdi Seraj, Şerife Zihni Eyüpoğlu

Purpose This study investigates volatility and returns spillovers among US technology stocks, decentralized finance (DeFi) tokens and conventional cryptocurrencies, while also examining strategies for optimal portfolio allocation. Design/methodology/approach The study analyses daily financial market data from October 05, 2020 to February 09, 2024 by employing the Diebold and Yilmaz (2012) and dynamic conditional correlations generalized auto-regressive conditional heteroscedasticity (DCC-GARCH) models. Findings Empirical findings showed that the US technology stocks were highly interconnected both in returns and volatilities (same as the crypto assets), while technology stock-crypto asset market connections were quite low. Moreover, the technology stocks (crypto assets) were generally net volatility and return receivers (transmitters). Overall, market connectedness was high (65.6% for volatility and 77.2% for return). Portfolio optimization results showed that technology stock-crypto asset (all-DeFi, all-cryptocurrency, all-technology stock and DeFi-cryptocurrency) portfolios were attractive to risk-averse (risk-neutral and risk-seeking) investors. Originality/value This is the first study to comprehensively analyze volatility and return connectedness and provide insights into portfolio optimization across traditional technology, DeFi and cryptocurrency markets. The insights from this study will aid in risk management, optimal portfolio diversification and formulation of regulations and policies to promote market stability.

Tomas Pečiulis, Asta Vasiliauskaitė

Cryptocurrency markets are highly volatile, creating challenges for accurate risk management and forecasting. As digital assets become more integrated into financial systems, understanding their volatility dynamics is essential for investors and policymakers. Previous research has primarily applied standard GARCH models to cryptocurrencies, often neglecting advanced specifications that capture asymmetry, regime-switching, and long-memory effects. This limits the accuracy of volatility forecasts and fails to reflect the unique behaviour of digital assets. This study aims to identify the most effective GARCH-class models for forecasting volatility in Bitcoin, Ethereum, Binance Coin, and Ripple. We analyse daily returns from August 2017 to December 2024, applying eight advanced GARCH specifications: EGARCH, GJR-GARCH, FIGARCH, HYGARCH, MSGARCH, CS-GARCH, and Log-GARCH. Hyperparameter tuning is conducted via grid search across lag orders (p, q ∈ [1, 5]), mean equations, and error distributions. Model performance is evaluated using AIC, BIC, RMSE, and MAE. Results show that MSGARCH and EGARCH outperform symmetric and short-memory models, highlighting the importance of regime-switching and leverage effects. FIGARCH provides the best fit for Bitcoin and Ethereum, confirming long-memory persistence. Skewed Student’s t and GED distributions improve accuracy by capturing heavy tails and asymmetry. These findings demonstrate the limitations of standard GARCH models and underscore the value of advanced specifications in modelling cryptocurrency volatility. The study offers practical insights for traders and risk managers, contributing to more robust forecasting in non-stationary markets. Advanced GARCH models significantly enhance volatility prediction for digital assets. Future research could extend this framework to other speculative instruments or integrate machine learning techniques to further improve performance.

V. V. Dikovitsky

Introduction This article presents a method for short-term cryptocurrency price forecasting utilizing news headlines. Methods The study analyzes the impact of news on asset prices within one hour of publication, employing machine learning-based classification with BERT and GPT models, as well as GloVe vector representations. Results The proposed cascade classifier model enhances prediction accuracy by initially assessing the strength of a news item and subsequently forecasting the direction of price movement. Experimental results demonstrate the effectiveness of the developed classification model. Discussion The model achieves an accuracy of 79% in predicting price movements, confirming the potential of leveraging news headlines to improve short-term forecasts in cryptocurrency markets.

Fangfang Zhu, Shaojun Fu, Xiangdong Liu

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets.

Michael Michaelides, Niraj Poudyal

Abstract Value‐at‐Risk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the time‐heterogeneous Student's t autoregressive model outperforms standard models commonly used by practitioners.