Blockchain Papers

Emiliano Álvarez, Juan Gabriel Brida, Leonardo Moreno, Andrés Sosa

No abstract is available for this record.

Yue Qiu, Shen Qu, Zhentao Shi, Tian Xie

No abstract is available for this record.

Walid Mensi, Ramzi Nekhili, Xuan Vinh Vo, Sang Hoon Kang

ABSTRACT This paper examines the hourly downward/upward multifractality and dynamic efficiency of four cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC)— before and during the COVID‐19 pandemic, and during the Russia–Ukraine tension. Using the asymmetric multifractal detrended fluctuation analysis method, the results show significant asymmetric multifractality in all series, which intensifies for BTC only throughout the COVID‐19 crisis and narrows for ETH, XRP, and LTC. Moreover, we show that cryptocurrency markets are more inefficient during the upward (downward) trend and before (during) the COVID‐19 crisis. LTC is the least inefficient market pre COVID‐19, whereas XRP is the least inefficient during the pandemic crisis. The results show evidence of excessive asymmetric multifractality for all four crypto markets. Before the COVID‐19 crisis, positive values of excess asymmetry in multifractality have been identified for BTC and LTC markets, whereas the excess asymmetry values were negative for ETH and XRP markets. BTC and ETH markets showed wider multifractality fluctuations compared to LTC and XRP, indicating a stronger reaction to the war's impact.

Jieru Wan, Liyan Han, You Wu

No abstract is available for this record.

Nadiah Ruza, Saiful Izzuan Hussain, Nurulkamal Masseran

This study employed Extreme Value Theory (EVT) to identify high-risk investment opportunities in the volatile crptocuurency market. EVT provides a more accurate risk assessment than traditional methods as it focuses on the tail distribution. The daily outcomes of six major cryptocurrencies were used for the analysis (Bitcoin, Ethereum, Ethereum Classic, Litecoin, Monero and Ripple). The time frame extends from January 2017 to December 2019 and includes major changes. Returns are fitted to the generalized Pareto distribution (GPD) in conjunction with the extreme value distribution. The results show that Bitcoin has a relatively low downside risk compared to other cryptocurrencies. Ethereum and Litecoin have more stable return patterns, suggesting a safer profile, while Ripple and Monero have the highest tail risk. These findings are consistent with other studies looking at the diversification and safe-haven properties of certain cryptocurrencies and highlight the importance of Extreme Value Theory (EVT) in evaluating extreme negative risk. The study is highly relevant for investors, portfolio managers and regulators to minimize volatility and reduce systemic risk in digital asset markets.

Authors unavailable

<h2 style="text-align: center;"><span style="color:#e74c3c;">Volume 30&nbsp;Number 1, 2025</span></h2> <h2 style="text-align: center;">Quantile Time-Frequency Price Connectedness Between Classic Cryptocurrencies, NFT, DEFI, and B...</h2>

Ziyad Baali

No abstract is available for this record.

Seyed Mohammad Habeli, Seyed Mahdi Barakchian, Ali Motavasseli

No abstract is available for this record.

N.-H. Chen, Jennifer Chan, Linh Nghiem

No abstract is available for this record.

Cathy W. S. Chen, Po‐Hui Chen, Ying‐Lin Hsu

ABSTRACT Cryptocurrencies exhibit high volatility, emphasizing the importance of accurately measuring tail risk in their markets. This research incorporates a threshold‐switching mechanism into Taylor's ES‐CAViaR models that unveil features such as asymmetry and jump phenomena. These enhancements effectively capture the diverse tail risks of cryptocurrencies while enabling the simultaneous forecasting of both Value‐at‐Risk (VaR) and Expected Shortfall (ES). The proposed models incorporate two types of functions to address the VaR and ES nexus with the option to use the rolling standard deviation of returns as a short‐term volatility proxy as a regressor. We estimate the parameters and forecast tail risk within a Bayesian framework. Taking the two largest cryptocurrencies by market capitalization, Bitcoin and Ethereum, we assess the one‐step‐ahead forecasting performance over a four‐year out‐of‐sample period using a rolling window approach. The comparative results from backtests and five scoring functions among eight competing models support the conclusion that models with a threshold mechanism capture the tail risk of cryptocurrencies more accurately than other risk models.

Seyedeh Fatemeh Mottaghi, Bertram I. Steininger

No abstract is available for this record.

Aubain Nzokem

The paper presents two series representations of a L{\'e}vy process for the Generalized Tempered Stable (GTS) distribution: a series representation generated by the inverse tail integral and a short noise representation. Both series representations are used to simulate the daily returns of Bitcoin and Ethereum. The Q-Q plot analysis shows smooth linear patterns, indicating strong agreement between the empirical and theoretical GTS distributions.

Gabriel Borrego Rold aacute n

No abstract is available for this record.

Ismail Jirou, Ikram Jebabli, Amine Lahiani

No abstract is available for this record.

Tetsuya Takaishi

The finite sample effect on the Hurst exponent (HE) of realized volatility time series is examined using Bitcoin data. This study finds that the HE decreases as the sampling period $Δ$ increases and a simple finite sample ansatz closely fits the HE data. We obtain values of the HE as $Δ\rightarrow 0$, which are smaller than 1/2, indicating rough volatility. The relative error is found to be $1\%$ for the widely used five-minute realized volatility. Performing a multifractal analysis, we find the multifractality in the realized volatility time series, smaller than that of the price-return time series.

Messaoud Chibane, Nathalie Janson

No abstract is available for this record.

Matúš Horváth, Tomáš Výrost

No abstract is available for this record.

Rui Zha, Lean Yu, Xi Xi, Yi Su

No abstract is available for this record.

Kamphol Panyagometh

During the COVID-19 pandemic and subsequent periods of US monetary policy normalization after quantitative easing during COVID-19, global financial markets have encountered elevated levels of volatility and risk. In response, investors have increasingly sought out unconventional financial assets, such as Bitcoin, to mitigate exposure and enhance portfolio diversification. This study utilizes a Dynamic Conditional Correlation (DCC) Multivariate GARCH model, specifically employing the GARCH (1,1) specification, to analyze the relationship between stock markets index of major countries and cryptocurrency, with a particular focus on Bitcoin. The results indicate statistically significant correlations between Bitcoin and stock market returns in several countries during the COVID-19 period. Volatility appears to be influenced by historical stock market performance during both the pandemic and the subsequent normalization of monetary policy. Furthermore, the DCC-GARCH models reveal low significant coefficients for ASEAN stock market indices before and during the COVID-19 pandemic, indicating that these markets may have displaced Bitcoin as a hedge asset. In contrast, stock market indices in America and Europe consistently show statistical significance across all periods, suggesting that Bitcoin’s role as a hedge in these regions is limited. In contrast, gold clearly demonstrated safe haven properties before the COVID-19 pandemic which a characteristic had not been observed for Bitcoin. However, gold has emerged as a safe haven for only ASEAN stock markets since the U.S. initial 0.25% interest rate hike.

Serkan Aras, Mehmet Ozan Özdemir, Cihan ÇILGIN

No abstract is available for this record.

Jiangcheng Li, Yingfeng Xu, Chen Tao, Guang-Yan Zhong

No abstract is available for this record.

Dias Saparbekov

This study assesses the out-of-sample forecasting capabilities of risk-neutral density models in Bitcoin options market, with a focus on the Normal Inverse Gaussian (NIG) density. Understanding forward-looking price dynamics becomes critical as cryptocurrencies continue to gain a reputation in financial markets. This research examines how the NIG model, with its capability to capture skewness and kurtosis, compares to the benchmark log-normal (LN) distribution. The analysis applies the likelihood ratio test to evaluate the predictive performance of the models. As a result, NIG model improves the accuracy of tail forecasts, outperforming LN in capturing extreme market movements, which holds implications for risk management and market timing in Bitcoin market.

Iulia Cristina Iuga, Raluca Andreea Nerişanu, Larisa-Loredana Dragolea

This study explores the volatility spillover effects between clean and dirty cryptocurrencies and key financial indices, specifically focusing on Green Finance Indices (such as solar, wind, and nuclear) and Economic Indices (like the Baltic Dry Index and CRB Index). Employing the diagonal BEKK model and the DCC GARCH model, the study spans data from February 17, 2020, to September 30, 2024, to analyze how cryptocurrencies, categorized by their environmental impact, influence these indices. The results reveal significant volatility spillovers from both clean and dirty cryptocurrencies, with clean cryptocurrencies such as Cardano showing a stabilizing effect, while dirty cryptocurrencies like Bitcoin exhibit more pronounced and asymmetric volatility impacts on green finance indices. Furthermore, the persistent correlations identified through the DCC GARCH model highlight the dynamic relationships between cryptocurrency markets and green finance, suggesting that shocks in cryptocurrency volatility can significantly affect the financial dynamics of renewable energy investments. These insights are valuable for portfolio diversification and risk management, indicating that certain cryptocurrencies may serve as effective hedging instruments against risks in green finance. This study contributes to a deeper understanding of the interaction between digital financial assets and sustainable investments, offering practical implications for investors, financial managers, and policymakers committed to achieving Sustainable Development Goals (SDGs).

Sami Mestiri

Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterize the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.