As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China's energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China's energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China's energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market.
Anshul Agrawal, Sanjeev Kadam, Vibhanshu Jha, Ved Prakash
Sentiment analysis and forecasting play a crucial role in understanding market emotions and their trends. When there is a high volatility or variability in the market, it becomes quite difficult for investors and market participants to predict market trends. In this study, VADER analysis is used for sentiment analysis, and the long short-term memory network (LSTM) model is applied for forecasting to predict Bitcoin prices during volatile periods, including the COVID-19 pandemic and the Russia–Ukraine war. In this research, we analyze the daily closing prices of Bitcoin from 2020 to 2023, extracted from Twitter news. The data are divided into two sub-periods: the first from 2020 to 2022, covering the COVID-19 period and the second from 2022 to 2023, covering the Russia–Ukraine war. This study aims to determine patterns and fluctuations in Bitcoin price sentiment over the observed time period and explain how sentiment dynamics are linked to Bitcoin price movements under erratic market conditions. The growing influence of social media on financial markets underlines the significance of this analysis. This study delivers valuable insights for investors, analysts, market participants and policymakers to manage their portfolios and mitigate risk during volatile periods.
The primary focus of this study is to monitor significant changes compared to the author's previous articles, with the objective of identifying alterations in the legality of cryptocurrency; the extent of its volatility; its profitability; and its use as a medium of exchange. The author asserts that, in 2025, the legality of cryptocurrencies underwent significant changes on a global scale. The regulatory approach to digital assets varies across nations, with some adopting a regulatory framework that encompasses these assets, while others have opted for a prohibitionist stance. The profitability of mining has been observed to decrease in consequence of rising time and energy costs, whilst the volatility index has been noted to decrease due to the entry of institutional investors and the adoption of merchant strategies. In summary, the profitability of crypto asset acquisition has reached a state of maturity. The focus has shifted from the initial hype to the development of effective strategies, the optimal timing of transactions, and the conducting of thorough research.
Farrukh Nawaz, Mirzat Ullah, Ohannes George Paskelian, Umar Nawaz Kayani · 5 authors
Abstract This study delves into the substantial fluctuations in returns of leading cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)—alongside major global stock indices, including the NASDAQ Composite, S&P 500, and Euronext ENX. Utilizing the swap variance (SwV) analysis estimation approach, the research examines these entities based on their respective market capitalizations, assessing market jumps, integrated volatility, and realized volatility as key metrics for evaluating abnormal returns. The findings reveal that economic crises trigger an increased occurrence of market jumps in both cryptocurrency and stock markets, contributing to heightened volatility. This phenomenon underscores market inefficiencies and challenges the Efficient Market Hypothesis (EMH) framework. While positive jumps are more frequent, negative jumps are significantly larger in magnitude, supporting theories such as asymmetric volatility, the leverage effect, prospect theory, and loss aversion. Notably, the cryptocurrency market exhibits greater volatility compared to traditional stock markets, particularly during periods of heightened economic policy uncertainty. These insights hold profound implications for investors, portfolio managers, and policy‐makers, offering a nuanced understanding of the intricate dynamics within these financial ecosystems. By uncovering the interplay between market jumps, volatility, and economic uncertainty, the study provides valuable guidance for navigating the complexities of modern financial markets.
Cui-Ping Wen, Kai‐Hua Wang, Chi‐Wei Su, Xin Li · 5 authors
This study examines the impacts of bitcoin price (BTP), crude oil price (COP), and economic policy uncertainty (EPU) on China’s green bonds (GBs) in a period from 2014: M10 to 2024: M04 using the quantile autoregressive distributed lag model. Results demonstrate that BTP and EPU positively and negatively affect GBs in the long-term across all quartiles, respectively, while COP enhibits insignificance. In the short-term, all variables positively affect GBs and are concentrated in the low quantiles. This study constructs a multivariate framework to explore financial linkages across markets and examines variable interactions, enriching the theoretical framework of the GB market.
Pedro Henrique Filgueiras dos Santos Oliveira, Daniel Muller Rezende, Saulo Moraes Villela, Heder S. Bernardino · 6 authors
One of the main events involving the world economy in 2022 was the beginning of the war between Russia and Ukraine. This event offers an opportunity to analyze how a large-magnitude world event can affect the use of cryptocurrencies. Ethereum is one of the most prominent and widely used cryptocurrency platforms and, as such, provides a valuable case study for this scenario. This work investigates the behavior of accounts and their transactions on the Ethereum network during this event. For this purpose, we collect all Ethereum transactions during two distinct periods: (i) during the month the conflict began, and (ii) during the previous year. We organized a dataset with the accounts involved in these transactions and the subset of these accounts that interacted with a service within Ethereum named Flashbots Auction. Flashbots Auction is crucial as it addresses issues regarding transaction ordering and miners exploiting that ordering to make profit. Then, we model temporal graphs in which each vertex represents an account, and each edge represents a transaction between two accounts. We analyzed the behavior of these accounts via graph metrics for both groups during each observed time window. The results show changes in account behavior and activity, as well as variations in daily transaction volume.
Susanna Levantesi, Gabriella Piscopo, Alba Roviello
Accurate estimation of cryptocurrency market volatility is crucial for investors. The Crypto Volatility Index (CVI) was developed to measure the market’s expectations for the 30-day implied volatility of Bitcoin and Ethereum to address the growing demand for reliable predictions. This study explores the relationship between the CVI and the volatility of traditional financial markets, including the Gold Volatility Index (GVZ), the Crude Oil Volatility Index (OVX), and the S&P500 Volatility Index (VIX). Three other variables are also analyzed: the USD to EUR exchange rate (USDEUR), the Federal Reserve interest rate (FED), and the NASDAQ index. The aim of the research is explanatory: the input variables and the CVI are observed contemporaneously to catch the complex relation between them. Using Pearson correlation, distance correlation, and mutual information, we demonstrate the presence of non-linear relationships between some variables in the dataset. Explanatory analysis is conducted using machine learning techniques, specifically the Random Forest (RF) algorithm and Gradient Boosting Machines (GBM) to account for these potential non-linear interactions. These methods are better suited than standard linear models for identifying complex relationships. In particular, the RF algorithm reaches a better level of accuracy than GBM and avoids overfitting.
Since Trump took office, cryptocurrencies have received widespread attention. The Bitcoin market has witnessed a brief bull market, fluctuating within the range of $95,000 to $110,000, with investors' enthusiasm for investment remaining high. On February 22, 2025, the Bitcoin market witnessed a sharp decline, triggering a large number of margin calls. It was later revealed that this plunge was initially triggered by panic selling due to a wave of Bitcoin thefts. However, the theft of Bitcoin cannot be regarded as the main reason for this sharp drop. Traders are also a factor, especially their psychological fluctuations and irrational behaviors before and after margin calls. By studying the original articles in psychological finance, behavioral finance and neuroscience, combined with the specific manifestations of the anchoring effect and loss aversion psychology of Bitcoin market traders, this paper explores how traders' excessive reliance on anchor points and loss aversion lead to irrational behaviors and adverse trading outcomes, with the aim of reducing cognitive biases and improving decision-making for market traders under uncertainty. This study reveals that anchoring effect and loss aversion significantly affect the decision-making process of Bitcoin traders. Understanding these psychological factors can help traders manage risks more effectively and make more rational investment choices.
This study investigates the effect of Bitcoin mining reward halvings on price fluctuations and enhances prediction models by incorporating a novel factor termed "halving impact weight". This weight quantifies the delayed influence of halving events on Bitcoin prices with an exponentially decaying model. Incorporating this factor significantly enhances forecast accuracy: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) decrease by 12.05% and 12.49%, respectively. Our findings demonstrate the critical importance of accounting for mining reward halvings in predictive models of Bitcoin prices.
This study examines the dynamic connectedness between Bitcoin and various financial assets, including the stock market, gold, oil, bonds, and exchange rates, as well as explores portfolio strategies involving these assets. The study covers the period from January 2, 2015, to March 1, 2024. The quantile connectedness approach and portfolio strategies are utilized in the analysis. The findings are as follows: Intermarket volatility spillover significantly increases under extreme conditions. Bitcoin emerges as a transmitter during bullish markets and acts as a receiver in bearish and normal market conditions. Gold serves as a receiver in extreme conditions and a transmitter in normal conditions. Unlike gold, oil acts as a transmitter under extreme conditions and functions as a receiver under normal conditions. Among the fundamental markets, the stock market is the most significant shock transmitter. In risk-mitigating portfolios, the proportion of Bitcoin is low, while the proportions of gold and the dollar index are high. Bitcoin has been found to have low hedging properties. <br />Implications for Central European Audience: Since the emergence of Bitcoin in 2008, the cryptocurrency market has developed rapidly. Bitcoin and cryptocurrencies have come to occupy an important place in financial markets in terms of value and volume. Bitcoin can affect portfolio management in the financial system in terms of diversification, hedging, risk management, portfolio strategies, and linkages between financial assets. This study investigates the linkages, hedging and portfolio strategies between Bitcoin and the stock market, gold, oil, bond and exchange rate markets. The results of the study are important for portfolio managers, risk managers, financial analysts and economic managers.
Bitcoin's return volatility from 2014 to 2022 reveals significant changes in response to political and macroeconomic developments, particularly during the 2016 and 2020 U.S. presidential elections. In 2016, Bitcoin exhibited modest price movement and low volatility, while in 2020, the asset experienced dramatic price increases and heightened volatility, reflecting increased market maturity and institutional interest. Political uncertainty, regulatory shifts, and market sentiment played crucial roles in shaping volatility dynamics during these periods. Using GARCH(1,1) and EGARCH(1,1) models, time-varying volatility patterns and asymmetric effects of market shocks are analyzed. GARCH results confirm volatility clustering and high persistence, whereas EGARCH captures leverage effects, showing that negative shocks influence volatility more than positive ones. Visualizations of conditional variance support these findings, indicating that Bitcoin reacts more intensely to adverse news, especially during politically turbulent periods. Residual diagnostics suggest model adequacy and enhance the reliability of insights. These results underscore Bitcoin's evolving role as a financial asset increasingly affected by global events and investor sentiment, offering valuable implications for market participants and policymakers monitoring risk in cryptocurrency markets.
This paper proposes a comprehensive deep-learning framework, SentiStack, for Bitcoin price forecasting and trading strategy evaluation by integrating multimodal data sources, including market indicators, macroeconomic variables, and sentiment information extracted from financial news and social media. The model architecture is based on a Stacking-LSTM ensemble, which captures complex temporal dependencies and non-linear patterns in high-dimensional financial time series. To enhance predictive power, sentiment embeddings derived from full-text analysis using the DeepSeek language model are fused with traditional numerical features through early and late data fusion techniques. Empirical results demonstrate that the proposed model significantly outperforms baseline strategies, including Buy & Hold and Random Trading, in cumulative return and risk-adjusted performances. Feature ablation experiments further reveal the critical role of sentiment and macroeconomic inputs in improving forecasting accuracy. The sentiment-enhanced model also exhibits strong performance in identifying high-return market movements, suggesting its practical value for data-driven investment decision-making. Overall, this study highlights the importance of incorporating soft information, such as investor sentiment, alongside traditional quantitative features in financial forecasting models.
Cryptocurrencies have experienced exponential growth within the last decade, with market capitalization hovering above the one-trillion-dollar mark since 2022. One area of concern for current and potential crypto users and investors is their unprecedented price volatility. As cryptos become interlinked with the regulated financial system, questions emerge regarding the possibility of linkages of their prices to the external environments. Financial and macroeconomic factors of inflation, economic growth, interest rates, currency exchange rates, equity market returns, corporate bond yields, gold and oil prices are examined against the cryptocurrency returns. This study encompasses a multi-analytical approach, firstly with the empirical tests of Spearman’s correlational analysis to discover the most pertinent relationships, followed by the PCA analysis to reduce redundancy. The predictive regression model of the Granger Causality test, a vector autoregression (VAR) time series forecasting method, is applied to examine whether the highly effective factors Granger cause the crypto price movements. The Machine Learning Random Forest Regression is also applied where a nuanced understanding of the external factors affecting cryptos prices is gained. The findings of this study pertain to more recent times when the pandemic crisis has subsided and stable economies are in place. The results examined four major cryptos of Bitcoin, Binance Coin, Ripple and Tether, where most behaviours suggest that users and investors are willing to take on riskier assets during periods of economic growth, a strong equity market complements crypto demands and gold and oil are good substitutes for cryptos. Tether, a stablecoin, was the least impacted by external factors and behaved similarly to a fiat currency. This investigation into external factors will empower cryptocurrency users and investors with valuable insights into the crypto price mechanisms, enabling them to refine their investing and portfolio diversification strategies.
The environmental concerns associated with energy-intensive cryptocurrencies have led to the rise of clean cryptocurrencies, which aim to balance financial innovation and sustainability. This study investigates whether clean cryptocurrencies improve portfolio resilience while promoting environmental goals in the cryptocurrency market. Using dynamic correlation-based hedge and safe-haven regression models, relative risk ratio analysis with higher-order moments risks, and multiple portfolio optimization strategies, we assess the impact of integrating clean cryptocurrencies into portfolios composed mainly of traditional cryptocurrencies. The results show that clean cryptocurrencies consistently reduce tail risk during periods of market stress; however, this risk reduction does not always result in higher returns or better risk-adjusted performance. These findings have important implications for both investors and policymakers. Clean cryptocurrencies can help investors manage tail risk and align with ESG goals, but their implementation requires a careful assessment of return expectations and investment constraints. Policymakers are encouraged to create a regulatory framework that fosters sustainable digital asset development while protecting investors and ensuring market stability. This study contributes to a deeper understanding of clean cryptocurrencies’ role in sustainable investment strategies within the evolving digital asset landscape.
Purpose The study aims to examine the role of cryptocurrency, specifically Bitcoin, as an asset and a currency. Design/methodology/approach The dynamic conditional correlation-generalised autoregressive conditional heteroskedasticity model was used to assess the role of Bitcoin as an asset. The study assesses the dynamic correlation between Bitcoin, bonds, Gold, the S&P 500 and crude oil in the extreme market events. A theoretical approach was used to evaluate Bitcoin on the functions of money. Findings The study found that cryptocurrency functions more like an asset, as it does not yet fulfil the role of money and still has a long way to go on this front. As an asset, cryptocurrency plays an effective role as a diversifier in the case of gold, as well as a weak safe hedge and a weak safe haven in the case of bonds. In the case of the S&P 500, Bitcoin plays the role of a diversifier, whereas it plays the role of a diversifier and a weak safe haven for crude oil investments. Practical implications We suggest that Bitcoin be included solely as a diversifier within a portfolio of traditional assets and that a cautious investment approach be adopted, considering its volatile nature. For regulators, we emphasise the necessity of promoting the innovative aspects of cryptocurrencies, particularly regarding cross-border transactions. Originality/value The study provides evidence about the dynamics of cryptocurrency markets, indicating that even after the pandemic, cryptocurrency acts mainly as a diversifier and does not yet perform the functions of money.
Abdullah A. Aljughaiman, Mosab I. Tabash, Suzan Sameer Issa, Abdulateif A. Almulhim
Most prior studies explain cross-country volatility interconnectedness without accounting for exogenous global uncertainty factors that influence equity returns. This study is the first to explore how major global uncertainty indicators such as U.S. and European financial market uncertainty indices (CBOE volatility index (VIX), VSTOXX-50), Global Financial Stress Indices (FSI) and Bitcoin Sentiment Indices (BSI) transmit shocks to the conditional volatility of Gulf Cooperation Council (GCC) stock markets. Using a novel ‘Extended Joint’ time-varying parameter vector autoregression (TVP-VAR) connectedness framework, the analysis addresses rolling-window limitations, enhances robustness to outliers, accommodates structural shifts and explains the shock transmission mechanism for the overall investment horizon. To capture transitory (short-term) and enduring (long-term) shock transmission channels from global uncertainty indicators toward the GCC financial system, a frequency-domain TVP-VAR is also employed. Furthermore, for the portfolio optimization, we also employ the hedge ratio and optimal portfolio weight strategy based on the DCC-GARCH-t copulas. Findings reveal that the conditional volatility of equity markets in Oman, Qatar, Saudi Arabia and the UAE is more sensitive to shocks from global uncertainty indicators such as VIX, VSTOXX-50 and the FSI, while Bahrain’s market shows relatively lower exposure. Kuwait’s equity market volatility exhibits the highest long-term sensitivity to FSI, VIX and VSTOXX-50, whereas the UAE demonstrates the highest sustained exposure to VIX and VSTOXX-50. Results from the DCC-GARCH-t copula model indicate that in stable periods (pre-COVID-19), optimized portfolio allocations significantly improved diversification, reducing risk by up to 83%. However, during financial stress events like COVID-19, hedge ratio strategies provided more effective risk mitigation, with reductions ranging from 3% to 43%.
Bu çalışmada ortaya ilk çıkarılan on kripto para getiri ve işlem hacimleri ile birlikte varil başına Batı Teksas (WTI) ham petrol getirileri arasındaki ilişki test edilmiştir. Analiz için 29 Nisan 2013 – 04 Ağustos 2024 arası günlük veriler kullanılmıştır. Çalışmada ampirik olarak Granger ve Toda Yamamoto Nedensellik Analizi' nden yararlanılmıştır. Her iki analize göre WTI ile Bitcoin (BTC) arasında negatif tek yönlü ilişkiye rastlanmıştır. Granger nedensellik analizine göre WTI ile Ethereum (ETH) arasında, Toda Yamamoto nedensellik analizine göre ise WTI ile Filecoin (FIL) getirisi arasında negatif çift yönlü bir nedensellik ilişkisi olduğu sonucuna ulaşılmıştır. Elde edilen bulgular enerji fiyatlarında yaşanan dalgalanmaların küresel finansal istikrara etkilerini ortaya koymuştur. Enerji piyasalarındaki sürdürülebilirlik hedefleri ile blok zinciri teknolojisinin çevresel etkilerini en aza indirgemek için uluslararası regülasyonların geliştirilmesi ve bütüncül politikalar oluşturulması gerekmektedir. Bu öneriler kripto para birimlerinin, enerji piyasalarından kaynaklanan volatiliteye karşı daha dayanıklı hale getirilmesi için stratejik bir yol haritası sunmaktadır.