Bu çalışma Bitcoin getirileri ile kripto para piyasalarındaki yatırımcı duyarlılığını temsil eden Kripto Korku ve Açgözlülük Endeksi arasındaki kısa ve uzun dönemli ilişkiyi ve bu ilişkinin yönünü ve şiddetini araştırmaktadır. Çalışmada 01.02.2018-07.09.2022 dönemine ait günlük veri setleri A-ARDL (Augmented Autoregressive Distributed Lag) yöntemi ile analize tabi tutulmuştur. Finansal stres ve VIX Korku endekslerinin de kontrol değişkenler olarak kullanıldığı çalışmada yatırımcı duyarlılığının Bitcoin getirilerini kısa ve uzun dönemde pozitif ve önemli seviyede etkilediği bulgusu elde edilmiştir. Buna göre açgözlülük (korku) duygusundaki artışın Bitcoin getirilerini pozitif (negatif) etkilediği belirlenmiştir. Elde edilen bu bulgunun davranışsal finans ve yatırımcı duyarlılığı teorileriyle uyumlu olduğu ifade edilebilmektedir.
Mohammad Abdullah, Mohammad Ashraful Ferdous Chowdhury, G. M. Wali Ullah
This study inspects the asymmetric tail risk dynamics, efficiency, and interconnectedness among FinTech stocks, cryptocurrencies, and traditional assets. Firstly, we employ the Multifractal-Asymmetric Detrended Cross-Correlation Analysis to examine the cross-correlation patterns and efficiency dynamics of the analyzed assets. The findings reveal asymmetries in cross-correlations and the presence of multifractality, highlighting the nonlinear relationships among these assets and find FinTech assets are the most efficient. Secondly, we utilize the time domain quantile connectedness method to investigate tail risk connectedness, offering insights into the network's shock transmission and spillover effects. Our analysis identifies the major risk transmitters (FinTech stocks) and receivers (bond), emphasizing the interconnectedness of the assets. Additionally, the study conducts bivariate portfolio analysis, considering short and long investment horizons, to guide asset allocation and hedging strategies. Our findings have significant implications for facilitating informed investment strategies and improving the stability and resilience of financial markets.
The study examines the relationship between volume and volatility in leading cryptocurrencies i.e. Bitcoin and Ethereum, within the framework of Mixture of Distribution Hypothesis (MDH). It accommodates structural shifts in the cryptocurrency prices and uses fat-tailed distributions. The results show that the MDH is rejected for both cryptocurrencies, and volume alone cannot explain the heteroskedasticity of returns; however, it acts as a significant predictor for volatility, especially when incorporating structural breaks in the model. Further, the forecasting performance improves when fat-tailed distributions, such as the skewed student’s t and Johnson’s Su distribution are used to model the innovations. Thus, volume holds important information in the crypto markets and can affect returns, thereby, raising concerns about market efficiency. Our results are robust across different periods, modelling approaches and forecasting horizons, and hold substantial implications for traders, market participants, regulators, and governments in designing effective policies.
Saralees Nadarajah, Jules Clément, Patrick Rakotomarolahy, Henri T. J. E. Ratolojanahary
The purpose of this study is to conduct an empirical comparative study of volatility models for three of the most popular cryptocurrencies. We study the volatility of the following cryptocurrencies: Bitcoin, Ethereum, and Litecoin. We consider the GARCH-type, boosting-family-tree-based ensemble learning, and ANFIS volatility models for these financial crypto-assets, which some have claimed capture stylized facts about cryptocurrency volatility well. We conduct comparative studies on in-sample and out-of-sample empirical analyses. The results show that tree-based ensemble learning delivers better forecast accuracy. Nevertheless, the performance of some GARCH-type volatility models is relatively close to that of the best model on both training and evaluation samples.
Abstract The study attempts to add to the existing literature on the relationship between uncertainties and Bitcoin by determining the direction of the relationship between Economic Policy Uncertainty (EPU), Geopolitical Risk (GPR), Political Risk (PR) and Bitcoin returns. This is to ascertain if Bitcoin hedges and is a safe haven asset against uncertainties. We employed the use of Ordinary Least Square (OLS), Autoregressive Distributive Lag (ARDL) and Quantile Regression (QR) to achieve the research objective. Having discovered the existence of structural breaks after conducting the Zivot-Andrews unit root for structural breaks, the analysis was divided into full sample period, first sub-period and the second sub-period. Findings show that EPU, GPR and PR hedge and play the role of safe haven against uncertainties in the United States (US). We found that EPU exerts positive influence against Bitcoin returns while GPR and PR negatively influence Bitcoin returns. The result further shows that Bitcoin returns hedges against EPU in the lower and middle quantiles while Bitcoin returns hedges against PR only in the lower quantile. The study therefore concludes that uncertainty and risk in the US influence bitcoin returns. It supports the hedging ability and safe haven properties of bitcoin, emphasising that bitcoin returns react more to EPU US than GPR US and PR US, therefore recommending investment experts and financial analysts focus more on EPU US than GPR US and PR US.
This study is the first to scientifically investigate stock indexes and currency exchanges that affect crypto prices. The purpose is to distinguish between the USA-Japan stock markets and the currency market's short- and long-term effects on bitcoin and ethereum. Auto Regressive Distributed Lag (ARDL) is used to analyze weekly series from 1-1-2016 to 20-10-2024. An asymmetric error-checking framework employing non-linear ARDL statistical approach to study variables affecting bitcoin and ethereum prices. Bitcoin appear to have short- and long-term linear effects on the US-Japan stock markets. Euro, GBP, and USA-Japan stock markets exhibit short-term linear effects with ethereum. Ethereum linearly affects GBP. This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict bitcoin and ethereum price variations.
We explore volatility spillover effects between mainstream cryptocurrencies and energy token markets in the 120 days following three notable Blockchain bridge heists in 2022. Using the DCC-GARCH model, we find significant spillover effects between Bitcoin, Ethereum, and energy tokens like Power Ledger Token and Energy Web Token post-heists. This indicates heightened investor concern and panic trading impacting cryptocurrencies and energy token markets. Our analysis also reveals a herding behaviour in energy tokens under market stress and increased liquidity issues, leading to broader market quality deterioration. Based on these findings, we propose regulatory enhancements and the ‘Energy Future Fund’ to support the stability and growth of energy token markets.
Essa Al-Mansouri, Ahmet Faruk Aysan, Ruslan Nagayev
This paper examines Bitcoin’s viability as money through the lens of its risk profile, with a particular focus on its store of value function. We employ a suite of wavelet techniques, including Wavelet Transform (WT), Wavelet Transform Coherence (WTC), Multiple Wavelet Coherence (MWC), and Partial Wavelet Coherence (PWC), to decompose the risk structure of Bitcoin and analyze its relationship with various systematic risk factors. Our dataset spans from 13 August 2015 to 29 June 2024, and includes Bitcoin, major commodities, global and US equities, Shari’ah-compliant equities, Ethereum, and the Secured Overnight Financing Rate (SOFR). We find that Bitcoin’s risk profile is increasingly aligned with traditional financial assets, indicating growing market integration. While Bitcoin exhibits high volatility, a significant portion of this volatility can be attributed to systematic rather than idiosyncratic factors. This suggests that Bitcoin’s risk may be more diversifiable than previously thought. Our findings have important implications for monetary policy and financial regulation, challenging the notion that Bitcoin’s volatility precludes its use as money and suggesting that regulatory approaches should consider Bitcoin’s evolving risk characteristics and increasing integration with broader financial markets.
Zhi Zhan Lua, Chee Kiat Seow, Raymond Ching Bon Chan, Yiyu Cai · 5 authors
Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) models to address these challenges. By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. The up-to-date price prediction information obtained by the machine learning model is incorporated by custom oracle contracts and is transmitted to portfolio smart contracts. The integration of smart contracts and on-chain oracles ensures transparency and security, allowing real-time verification of portfolio management. The deployed cryptocurrency trading system performs these actions automatically without human intervention, which greatly reduces barriers to entry for ordinary users and investors. The results demonstrate the feasibility of creating a cryptocurrency trading system, with the LSTM model achieving a return on investment (ROI) of 488.74% for portfolio management during the duration of 9 December 2022 to 23 May 2024. The ROI obtained by the LSTM model is higher than the performance of Bitcoin at 234.68% and that of other benchmarking models with RF and Bi-LSTM over the same timeframe. This approach offers significant cost savings, transparent portfolio management, and a trust-free platform for investors, paving the way for broader cryptocurrency adoption. Future work will focus on enhancing prediction accuracy and achieving greater decentralization.
This study delved into the complex world of cryptocurrencies, analyzing their behavior, profitability, and volatility. Through a thorough and meticulous analysis of the 2021 – 2023 period, the volatile nature of these digital assets was revealed, where profits could be suddenly affected by external events. Bitcoin, two of the cryptocurrencies with the largest presence in the market, were the subject of a thorough analysis using sound statistical methodologies. Descriptive statistics were employed to characterize the overall behavior of cryptocurrencies, including measures of central tendency, dispersion, and distribution. Additionally, normality and stationarity tests were used to choose the best variant of the GARCH model, which was EGARCH, to estimate conditional volatility, future volatility and price profitability, allowing to identify patterns and dynamics in their variability. The results of the study revealed that cryptocurrencies, while presenting attractive potential returns, also carry a high degree of volatility. However, thanks to the in-depth analysis of the behavior of these assets we can identify opportune moments to make purchases, sales or strategic investments. The main goal of this study is to provide investors with the information needed to make strategic and informed decisions about their cryptocurrency investment
Abstract Investing in cryptocurrencies is progressively becoming a norm; however, these assets are excessively volatile and often decrease or increase in value instantly. Thus, rational investors holding cryptocurrencies for extended periods firmly search for assets that can diversify their risk, preferably with assets other than cryptocurrencies. In this study, we consider the two most studied cryptocurrencies with the highest capitalization and trading volume/value, namely Bitcoin and Ethereum. Specifically, we examine whether high-performing leading US tech stocks (Facebook, Amazon, Apple, Netflix, Google [FAANG]) can provide any diversification benefits to cryptocurrency investors. To do so, we employ dynamic conditional correlation (DCC), asymmetric DCC, time-varying parameter vector autoregression-based connectedness measures, dynamic correlation-based hedge and safe-haven regression analyses, portfolio optimization and hedging strategies, time- and frequency-based wavelet coherence, and high-frequency 10-min intraday data from January 1, 2018 to January 31, 2023. We find that FAANG stocks can be considered (at least weak) safe havens for Bitcoin and Ethereum during the sample period. Our subperiod analyses reveal that the safe-haven role of FAANG stocks, specifically for Bitcoin, has noticeably increased. While the safe-haven property of Facebook is the most promising, for Netflix it is blurred between a weak–safe-haven and a hedge. Our findings may help investors, policymakers, and academicians to invest in cryptocurrencies, formulate relevant investment guidelines, and extend the literature on cryptocurrencies, respectively.
(1) Background: Cryptocurrencies have a substantial environmental impact. In particular, the mining procedure that is employed to produce and finalize the transaction is energy-intensive and generates carbon emissions. Consequently, the objective of the present investigation is to investigate the function of cryptocurrencies in a sustainable development. This research specifically investigates the function of stablecoins, a novel subject in finance and academia that has the potential to foster a sustainable business environment. (2) Methods: A bibliometric analysis was performed using the R statistical programming language together with the bibliometric tools Biblioshiny and VOSviewer to fulfill the research objective. Data were obtained from the Scopus database, and their selection was completed using the PRISMA methodology. (3) Results: The results of the current research highlight the crucial role of stablecoins in promoting an alternative decentralized financial sector, offering a unique opportunity for the market to create a more inclusive and environmentally friendly financial ecosystem. Moreover, research indicates that stablecoins might convert Ethereum into a stable currency and enhance their ecologically friendly path. (4) Conclusions: Stablecoins have become a crucial tool in the unpredictable bitcoin environment, offering stability in a tumultuous market. The research indicates that users need to acknowledge the sustainability of asset collateral, and so far, only the regulation of stablecoins is progressing in this area.
Muhammad Shahzad Ijaz, Shoaib Ali, Anna Min Du, Mahrukh Khurram
We use event study methodology to examine how the Palestine-Israel Conflict affected equities, metals, energy, fiat, and crypto currencies. The findings highlight the susceptibility of the stock markets in Germany, the United Arab Emirates, Bahrain, and Kuwait to geopolitical shocks by demonstrating notable negative abnormal returns on the event day. This observation is more evident in areas which have direct economic connections to the belligerent nations. Conversely, the fiat and cryptocurrency markets, along with metals and oil, exhibit insignificant abnormal returns, with the exception of a strong reaction observed in Ethereum and oil prices. These findings highlight the fluctuating levels of sensitivity across diverse asset classes as markets beyond Palestine's trading partners demonstrate resilience to the war. Overall, our work underscores the significance of assessing contagion risk especially in areas affected by geopolitical instability. It also holds implications for policymakers and investors to contemplate the geopolitical situation while evaluating market risks and portfolio diversification strategies amid political tensions.
Khalid Khan, Adnan Khurshid, Javier Cifuentes‐Faura
Abstract This study uses the Bayesian structural model to assess the causal effect of the futures exchange (FTX) insolvency on cryptocurrencies from October 2022 to December 14, 2022. Findings show that FTX insolvency negatively impacts cryptocurrencies. Moreover, the results indicate rapid divergence from counterfactual predictions, and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse. Cryptocurrency is reacting strongly to the uncertainty caused by insolvency. In relative terms, the collapse of FTX has been highly detrimental to Solana and Ethereum. Furthermore, the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred. FTX collapsed owing to a mismatch between the assets and liabilities. The industry is still mostly unregulated, and regulators must act quickly, highlighting the need for outstanding innovation and decentralized and trustless technology adoption.
Samar S. Alharbi, Muhammad Naveed, Shoaib Ali, Faten Moussa
Using the TVP-VAR model, this study examines the connectedness between green cryptocurrencies and the individual components of the ESG (Environmental, Social, and Governance) stocks. Our sample period runs from November 10, 2017, to September 12, 2023. Our results indicate a moderate level of return and volatility transmission between green cryptocurrencies and ESG stocks. In line with theoretical argumentation, cryptocurrencies act as receivers of both return and volatility spillovers from the system, while stocks are the main transmitters. Our dynamic results show a substantial rise in total return and volatility connectedness of the system during the outset of the COVID-19 and Russia-Ukraine conflict, suggesting that global event amplifies the system connectedness. Moreover, the time-varying net results also exhibit a similar pattern, where the role of each asset changes during the turmoil period. Finally, our portfolio analysis suggests that green cryptocurrencies provide diversification to green stocks during both normal and turbulent periods. Additionally, they also emerge as effective hedges against ESG stocks across all market conditions. However, the hedge ratio increased during the COVID-19 pandemic, suggesting hedging becomes more expensive during turbulent periods. Our findings provide valuable insights for portfolio managers and policymakers regarding asset allocation, risk management, and the evolving dynamics between green cryptocurrencies and ESG stocks in an increasingly interconnected financial landscape.
The complexities of modern financial markets, characterized by heightened volatility and uncertainty, have necessitated the evolution of advanced risk management strategies. As global markets become increasingly interconnected, financial institutions, investors, and policymakers face unprecedented challenges in identifying, assessing, and mitigating risks. Effective risk management has emerged as a cornerstone of financial stability, requiring a blend of traditional methods and innovative tools. This paper explores comprehensive strategies for navigating volatility in complex financial environments, addressing systemic, credit, market, and operational risks. Traditional approaches, such as portfolio diversification and value-at-risk (VaR) modelling, remain foundational but are now complemented by cutting-edge technologies, including artificial intelligence (AI), machine learning (ML), and big data analytics. These tools enable real-time monitoring, predictive analytics, and stress testing, enhancing the capacity to anticipate and respond to emerging threats. Additionally, the integration of blockchain technology offers improved transparency and resilience in financial transactions, further mitigating systemic vulnerabilities. Case studies from diverse sectors highlight the practical applications of these strategies, illustrating how robust risk management frameworks can minimize losses, enhance profitability, and ensure regulatory compliance. The paper also examines the role of regulatory frameworks in shaping risk management practices and emphasizes the importance of a proactive, adaptive approach in navigating volatile market conditions. By combining traditional methodologies with technological advancements, financial institutions can build resilient systems capable of withstanding shocks and fostering long-term stability. This paper concludes by identifying emerging trends, such as quantum computing and decentralized finance, as transformative forces likely to redefine risk management in the future.
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.
Luís Pedro Freitas, Jorge Cerdeira, Diogo Lourenço
The rise of cryptocurrencies over the past decade has promised to challenge the dominance of fiat money systems and reshape monetary policy. However, recent developments, including market volatility and the collapse of key exchanges like FTX, have eroded public trust, raising skepticism of a feasible transition to a crypto-based monetary system. This paper explores why cryptocurrencies have not met the expectations of their proponents, particularly those who saw them as a step towards Friedrich Hayek’s vision for competitive currency issuance. While cryptocurrencies reflect some aspects of Hayek’s model, their instability—especially in Bitcoin-like assets—undermines their role as a reliable alternative to fiat money. The paper also considers how central bank independence and regulatory gaps further hinder the development of a robust cryptocurrency framework. Despite the continued relevance of Hayek’s ideas in today’s monetary landscape, the entrenched structures of modern central banks and the rise of Central Bank Digital Currencies suggest that a decentralised currency order remains unlikely in the near future.
Abstract The asymmetries of factors influencing the return of cryptocurrencies have already been well documented; however, in the case of NFTs, only information asymmetries and hedging properties related to asymmetries were studied. Therefore, the present study examines factors affecting NFT returns, from market-related factors (crypto-market index return and stock market index return) to the Amihud illiquidity ratio and Google search trends during different market conditions. The wavelet coherences-based methodology was applied separately during the boom, bust, normal, and turbulent periods identified by structural breakpoints. Based on 14 NFT projects between April 2019 and July 2022, results show two fundamental asymmetries influencing these NFT returns. First, there is an asymmetry in the behavior of the factors in different periods; second, there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.
The field of cryptocurrencies is in existence and dynamically evolving for over 14 years. Each year introduces new cryptocurrencies, with their total number exceeding 8,500. However, to date, there is no exhaustive categorization of cryptocurrencies that could possibly fully describe the landscape of the cryptocurrency market, which underscores the relevance of this research. The objective of this study is to construct a hierarchical categorization (taxonomy) of cryptocurrencies based on their main characteristics and functions. The principal research method is a retrospective analysis of the development of the cryptocurrency field from the creation of Bitcoin to the present day. As the industry evolved, new projects emerged, which significantly differed in their properties from what existed before, thus forming entirely new categories and niches in the cryptocurrency space. Moreover, the emergence of certain types of cryptocurrencies could lead to changes in the existing classification. The outcome of this research is a taxonomy of interchangeable cryptocurrencies/tokens. The proposed taxonomy is accompanied by a detailed examination of the cryptocurrencies associated with each category, as well as a consideration of the largest cryptocurrencies in terms of capitalization through its prism. The scientific novelty of this research lies in the absence of similar studies that look at the issue of categorizing cryptocurrencies through a historical lens.
This study examines the volatility dynamics of Bitcoin (BTC-USD) and MicroStrategy (MSTR) from September 2019 to September 2024 using the GARCH (1,1) model. Volatility is a key measure of risk in the financial market, understanding its patterns is crucial for effective portfolio management, risk management, and corporate financial strategies. Bitcoin, while known to be volatile, is very unpredictable, and given the high holding of that on MicroStrategy's balance sheet, it is closely tagged to the volatility of Bitcoin. Critical periods, such as the COVID-19 and the subsequent crypto market downturn between 2022 and 2023, demonstrate the linkages between traditional equities and digital assets. The findings of such analysis will prove that MicroStrategy's volatility has indeed closely followed the footsteps of Bitcoin, especially during the 2024 rally in that market, including all its shocks and recoveries. These find great importance in understanding volatility due to the growing integration of digital assets into corporate portfolios. This research will offer investors and corporate managers alike extensive insight into risk management and portfolio diversification by accounting for volatility dynamics between cryptocurrencies and stocks.
Net-zero emission targets require transparent and efficient carbon credit trading systems. This paper introduces a blockchain-based data visualization framework to enhance decision-making in the production and logistics sectors by simplifying blockchain transaction records and identifying potential arbitrage activities. The framework integrates real-time decision support tools, enabling production system managers to monitor carbon offset activities, detect fraudulent behaviors, and streamline operations. This research provides actionable insights into supply chain emissions management and operational risk reduction by leveraging advanced visualization techniques. The proposed approach offers innovative solutions to address the complexities of blockchain-based carbon trading, emphasizing transparency and sustainability. Our analysis demonstrates the effectiveness of these techniques in mitigating fraud and supporting compliance with international carbon trading standards. The findings contribute to integrating advanced technologies into sustainable production systems, offering practical implications for achieving global climate change mitigation goals and fostering a more efficient and secure carbon credit market.
Abstract This study examines the return connectedness between decentralized finance (DeFi)’s and the Association of Southeast Asian Nations (ASEAN) stock markets using the quantile vector autoregressive framework, which allows us to investigate the connectedness at conditional quantiles. Our sample includes four major DeFi’s and six ASEAN stock markets, spanning from March 2018 to December 2022. The static results indicate a moderate level of return transmission between the system at mean and median quantile. This propagation increases substantially under extreme market conditions, establishing an asymmetric transmission across quantiles. Despite being a relatively new asset class, DeFi dominates the equity market and acts as the primary shock transmitter to the system in most instances. The dynamic analysis reveals that total system connectedness fluctuates over time and quantiles. The total system connectedness peaked during the COVID-19 and the Russia–Ukraine conflict period, indicating the impact of global events on system transmission. The optimal weight and hedge ratio estimated using the DCC-GARCH model indicate that DeFi is beneficial for portfolio construction and risk management. The rising trend in dynamic optimal weight and hedge ratio during the COVID-19 pandemic demonstrates that investors should decrease their investments in DeFi and increase hedging costs. Therefore, portfolio managers and investors should readjust their portfolio allocation in a timely manner according to different market states to build additional effective hedging and diversification strategies to avoid large losses and to reduce portfolio risk exposure.