Cryptocurrencies have rapidly emerged as a significant financial asset class, influencing global monetary systems and financial markets. However, their extreme volatility, speculative nature, and evolving regulatory landscape pose challenges to investors, policymakers, and financial analysts. This study presents an in-depth quantitative analysis of cryptocurrency volatility and risk assessment, focusing on Bitcoin (BTC-USD) and its correlation with traditional financial assets, including the EUR/USD exchange rate and S&P 500 index. Our research employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to measure the dynamic volatility patterns of Bitcoin, revealing the assetâs substantial fluctuations over time and its sensitivity to market shocks. Additionally, we utilize Monte Carlo simulations to forecast potential future price movements of Bitcoin, highlighting risk scenarios and the probability distribution of price trajectories over a one-year period. The Value-at-Risk (VaR) model is implemented to estimate potential losses within a given confidence interval, providing a robust measure of downside risk. Furthermore, the study examines the integration of cryptocurrency markets with traditional financial instruments by analyzing cross-asset correlations and volatility spillover effects. The findings suggest that while Bitcoin remains a highly volatile asset, its correlation with the broader financial system is increasing, indicating a potential shift towards mainstream financial adoption. The results contribute to the ongoing debate on whether cryptocurrencies serve primarily as speculative instruments or as viable components of diversified investment portfolios. These insights are valuable for institutional investors, risk managers, and policymakers in designing more effective risk mitigation strategies for cryptocurrency investments.
Klaus Grobys, James W. Kolari, Davide Sandretto, Syed Jawad Hussain Shahzad ¡ 5 authors
Abstract This paper explores the tail behavior of cryptocurrency momentum strategies and the profitability of volatility-managed momentum portfolios. Our main results derived from using a sample of large-cap cryptocurrencies and equal-weighted momentum portfolios indicate that cryptocurrency momentum is subject to severe crashes. Even a single cryptocurrency can cause insignificant momentum portfolio returns. In line with the literature on volatility-managing equity portfolios, our findings suggest that volatility management is a useful tool for mitigating cryptocurrency momentum crashes. Further corroborative evidence suggests that cryptocurrency momentum appears to be a phenomenon associated with large-cap cryptocurrencies.
Wided Khiari, Azhaar Lajmi, Amira Neffati, Ahmed El Fahem
Purpose This study aims to examine the effect of cryptocurrency frauds on the price fluctuations of the cryptocurrency market. Design/methodology/approach To examine the effects of cryptocurrency frauds on cryptocurrency market, the authors have collected data of 38 cryptocurrencies price fluctuations between 01/01/2020 and 28/11/2021. The authors have used the multidimensional scaling method (MDS) to explore the price fluctuations of the cryptocurrency market and its relationship with the cryptocurrency market between 2020 and 2021. Findings The study results showed that even though cryptocurrencies are categorised into Bitcoin, Altcoins and Stablecoins, the effect of the frauds is specific to their usage cases. Bitcoin and certain Altcoins were affected in a certain way compared to Ethereum and cryptocurrencies specialised in smart contracts. Cryptocurrencies such as Tron and Elrond with the specifications of staking had a different reaction and cryptocurrencies that contribute to the development and enhancement of blockchain infrastructure had a different reaction throughout these incidents. Stablecoins, however, were unaffected by the fraud incidents because of their reliability and their correlation to real assets such as fiat money, petrol and gold. Practical implications The study enables financial institutions to understand how to react to cryptocurrencies, which are both an opportunity and a challenge for them. Consequently, banks should strengthen their security measures to protect customer funds from the risks associated with fraud and cyber-attacks. They should also implement risk management measures and guarantee the integrity of their systems to ensure stability and confidence in the use of cryptocurrencies. In addition, institutions should work in collaboration with the authorities to overcome regulatory challenges and create a favourable framework for the use of cryptocurrencies. Originality/value The main contribution of this paper is to examine this topic, which has been very little explored in previous work. The lack of theoretical and empirical evidence concerning this study represented a challenge, and an originality as studies concerning cryptocurrency fraud are limited, if not non-existent. The second contribution is quantitative and uses a MDS to examine price fluctuations in the cryptocurrency market and its relationship with the cryptocurrency market.
Abstract This paper examines the dependence, systemic risk spillover, return and volatility spillover, and portfolio implications across various timescales between the Green Bond (GB) and U.S. S&P 500 Stock (SP), Vanguard Total World Stock Index Fund (VT), Bitcoin (BTC), Ethereum (ETH), Ripple, OIL, and GOLD markets. The sample period is August 07, 2015âOctober 6, 2023, covering periods of instability during the COVID-19 pandemic and the RussiaâUkraine conflict. Using the waveletâcopulaâconditional value-at-risk and wavelet-multivariate asymmetric-GARCH framework, our main results show that the systemic risk and return, volatility spillovers, and diversification opportunities are portfolio-specific and timescale-dependent. Specifically, there is a negative long-term correlation for the pairs GB-SP and GB-OIL, whereas the pair GBâGOLD pair is positively correlated in the short term. GB can mitigate the risk of other markets. In terms of the portfolio implications, GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term. Regarding hedging effectiveness, the role of GB for GOLD and VT is noted.
This study aims to understand the contagion effect of the major equity market sentiment events, defined as jumps in the VIX index, on cryptocurrency price jumps and the corresponding feedback effect on investors' sentiment. Using recent high frequency intraday data with multivariate Hawkes processes, we find that several noteworthy contagion effects exist between Bitcoin and the market sentiment proxied by the VIX index. First of all, we find that positive market sentiment jumps tend to trigger a moderate level of cross-contagion on both positive and negative jumps in the Bitcoin market, whereas negative market sentiment jumps have no contagion effect on the Bitcoin prices. We also find that the Bitcoin market shows stronger positive self-contagion and cross-contagion effects than the equity market, in general. We also observe a lasting fear of missing out (FOMO) phenomenon in Bitcoin. It is supported by the evidence that the effect of positive jumps in the Bitcoin market lasts three times as long as that in both the equity market and the negative Bitcoin price jumps, and these positive jumps in Bitcoin may serve as a harbinger of equity market movement. These findings provide a better understanding of risk control and policy guidance for the cryptocurrency market.
This article presents a conventional asset penalizing game model in green finance. We use a Mean Field Game (MFG) approach to investigate how a large population of small interacting conventional asset holders influences green asset prices, resulting in a green asset premium. Firstly, we formulate the green premium for green assets in a decentralized exchange pool setting. Using a virtual curve to model how collective behavior affects green asset prices, we analyze green premium cost implications for agents. Then, we develop our MFG model for the transition of green assets. To address the MFG problem, we use the Pontryagin maximum principle to find the fix-point solution in a deterministic conventional asset price setting. In the numerical experiment, we expand it to the stochastic price case and use Monte Carlo simulations to obtain the equilibrium collective asset transfer rate under certain market conditions. For empirical analysis, we examine the explanatory power of our model concerning the real market's green premium.
This study presents an advanced adaptive trading framework that integrates Deep Reinforcement Learning (DRL) with the Iterative Model Combining Algorithm (IMCA) to overcome the critical limitations of static ensemble methods in global portfolio optimization. Using a diverse cross-market dataset of 39 stocks from the US, Australia, Europe, Thailand, and one cryptocurrency (BTC-USD), the research rigorously evaluates modelsâ adaptability under volatile market conditions. Volatile market conditionsâsuch as COVID-19, SVB crisis, and the 2022 crypto crashâare captured via volatility metrics (e.g., drawdown), with DRL models like PPO/TD3 adapting through dynamic reward signals. This cross-asset integration is particularly critical, as it captures the complex dynamics and correlations between traditional financial markets and emerging digital assets. Although DRL models like PPO and TD3 outperform traditional strategies, they remain vulnerable to market drawdowns and high volatility. IMCA significantly surpasses these models, achieving the highest cumulative return of 29.52% and a superior Sharpe ratio of 0.829 by dynamically recalibrating model weights in response to real-time market dynamics. This study addresses a substantial research gap, highlighting the failure of traditional ensemble modelsâreliant on static weightingsâto adapt to evolving financial conditions, resulting in suboptimal risk-adjusted returns. IMCA offers a dynamic, data-driven approach that continuously optimizes portfolio strategies across fluctuating market regimes, demonstrating its scalability and robustness across diverse asset classes and regional markets, and providing an empirical framework for adaptive portfolio management. Policy recommendations underscore the need for financial institutions to adopt AI-driven adaptive models like IMCA to enhance portfolio resilience, profitability, and responsiveness in uncertain markets.
Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali
Despite the introduction of several adjustments, mitigating data anomalies in financial datasets has proven challenging, particularly in the context of cryptocurrencies with extreme values and increased volatility. The progress in properly addressing these anomalies prior to testing remains restricted, highlighting the unique and complex nature of financial data in this domain. Thus, in this paper we propose a hybrid approach called the Win-IS strategy. It is meant to address the influence of extreme outliers in the tail and subsequently identify breaks, trend breaks and outliers in cryptocurrencies. This methodology uses the winsorization (Win) process to enhance the effectiveness of the indicator saturation (IS) approach. The study uses cryptocurrencies like Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Tether (USDT), and Ripple (XRP). The results of the research indicate that the winsorization strategy improved the detectability of the IS approach, with Win-IS outperforming the IS method in terms of the Bayesian Information Criterion. Furthermore, the Win-IS technique uncovered additional breaks, trend breaks and outliers that were previously unknown and repeated in some cases as detected by the IS strategy. The effect of winsorization is dependent on the chosen percentile and dataset attributes. Through detailed examination and comparison, the findings of this research contribute to the improvement of other detection approaches, providing a valuable perspective for researchers and practitioners in the field. Additionally, this hybrid approach can improve decision-making, risk management and model creation, benefiting investors, legislators and scholars.
This paper focuses on automating the analysis of financial news for stocks and cryptocurrencies, thereby providing traders and analysts with actionable insights. In today's fast-paced markets, being up-to-date is the need of the hour; however, manual analysis takes time. To overcome this challenge, the paper employs Python and deep learning tools to make the process of collecting, summarizing, and conducting sentiment analysis on relevant news articles much easier. The key assets analyzed are popular stocks and cryptocurrencies, such as Tesla, Gamestop, Bitcoin, and Ethereum. The system uses Hugging Face's Pegasus model, which is a state-of-the-art transformer, to summarize lengthy financial news into concise, manageable summaries. This reduces the effort required to sift through large amounts of information while preserving essential details. Moreover, the system applies pre-trained sentiment analysis models to gauge the market's overall sentiment-positive, negative, or neutral-toward specific assets, thus making quick and informed trading decisions possible. The paper workflow includes automatically scraping web sources such as Google News and Yahoo Finance, cleaning and processing the data, and exporting results in structured CSV files for further analysis. The files include the ticker symbols, sentiment scores, confidence levels, URLs, and summary text. It is scalable and flexible so that users can input their stock tickers and run real-time analysis with changes in market conditions. Overall, this paper provides an efficient, end-to-end solution for financial news analysis, allowing users to make informed decisions with reduced time and effort on data gathering and interpretation.
Thomas Conlon, Diego VĂctor de MingoâLĂłpez, Andrew Urquhart
ABSTRACT Growth in cryptocurrency funds has followed the wider expansion of the cryptocurrency sector. In this paper, we study the performance persistence and market timing ability of cryptocurrency fund managers. We show that cryptocurrency funds produce remarkable levels of abnormal returns. Moreover, sorting by previous alpha provides compelling evidence of persistence in abnormal returns. Funds with previous excess abnormal returns have high ex post abnormal returns, while cryptocurrency factors explain only a small proportion of the variation in these returns. An ex post outperformance among funds displaying ex ante market timing skills is found, while these ex post abnormal returns can, in turn, be attributed to managerial timing abilities.
The paper demonstrates the nonsense of using Bitcoin in financial investments. By using mean-variance financial analysis, stochastic dominance, CVaR, and the Shapley value theory as analytical statistical models, I show how Bitcoin performs poorly by comparing it against other traded assets. The conclusion is reached by analyzing daily freely available market data for the period 2018â2023.
The research paper will focus on the influence of major cryptocurrencies, especially Bitcoin and Ethereum, on world financial markets and traditional financial systems. It looks at how, because of their decentralized nature, these digital assets have brought new dynamics to financial markets in the price of other assets, their volatility, and their means of investment. The research design is of a mixed-methods nature, combining quantitative data from financial market indices with qualitative insights from expert interviews. Some of the main lessons learnt are declining value with other financial assets, the interdependency between movements in crypto assets and other linked assets and disruptions in banking, payments and investment. Besides, there are regulation decisions that should consider the fluctuations of the market and security requirements, as well as the analysis of many initiatives in order to provide sufficient regulation frameworks on the international level. The concluding advice proposed how not only to accommodate the disturbance of current financial stability through innovations but also to integrate the utilization of cryptocurrencies.
Steve Springer Laryea, Kofi Agyarko Ababio, Jules ClĂŠment, Marno Booyens
This paper aims to investigate investorsâ prospects in adding value to their portfolios by considering investorsâ behavioural score (Cumulative Prospect Theory (CPT) score) and a clustering technique in the selection of assets. The universe of assets constitutes 63 cryptocurrencies sourced from Bloomberg from Jan 01, 2020, to July 31, 2022. The study period was segmented into two distinct and mutually exclusive periods, namely COVID-19, and post-COVID-19. Nine portfolios were constructed of which six were based on the CPT and the remaining on the K means Clustering technique. Using the copula-based Differential Evolution (DE) algorithm for the optimisation, the results show that portfolios consisting of assets with extremely high CPT scores were preferred during the post-COVID-19 and full sample periods, except for portfolios comprising assets with extremely low CPT scores during the COVID-19 period. The most optimised portfolio was composed of classified assets with extremely high CPT scores in the post-COVID-19 period. These findings provide intuitive and coherent investment strategies to guide investors in the cryptocurrency market.
Within the framework of high-frequency volatility modeling, this study investigates the realized volatility spillover dynamics across major cryptocurrencies over an extended period of time. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model of the realized volatility (RV), this work constructs the Total Connectedness Index (TCI) and Pairwise Connectedness Index (PCI) to measure the intensity and direction of realized volatility transmission within this digital asset network. Our findings reveal a consistently high level of spillovers among these leading cryptocurrencies, with notable peaks during periods of global market turbulence. Notably, Ethereum emerges as the most influential volatility transmitter, challenging the traditional view of Bitcoin as a primary driver of volatility spillovers. This reflects Ethereumâs pivotal role in decentralized finance (DeFi), decentralized applications (dApps), and its growing trading activity, suggesting a shifting influence in the increasingly diversified cryptocurrency ecosystem.
Non-fungible tokens (NFTs) have gained mainstream attention in the fintech community, but there is little research on their statistical properties. This study investigates the long-memory characteristics of NFT returns and volatility, focusing on their potential for predicting price movements. As NFTs do not conform to traditional models, understanding their unique features is crucial for comprehending complex market dynamics. This study aims to reveal the impact of macroeconomic factors on NFT prices, understand their correlation and develop predictive models using autoregression and artificial intelligence (AI) technology. This research utilized datasets from the Centers for Disease Control and Prevention (CDC), U.S. Bureau of Labor Statistics, Bureau of Economic Analysis, Christieâs, Dune, and Google Trends. Correlation and p value tests revealed strong relationships between NFT prices and variables such as weekly volume, pandemics, inflation and security. The Baseline Model using autoregression with NFT volume, security and technology factors outperformed all other models demonstrating the speculative volatility of NFTs. The Transformer Model using transformers, an architecture used by ChatGPT, Gemini and Stable Diffusion, showed high accuracy with less feature selection and preprocessing efforts. This study provides a novelty using a systematic approach for researchers to perform financial forecasting and contributes to the scarce literature on NFTs. This research offers valuable insights to investors and private agents regarding the right economic conditions for NFT investments by reducing portfolio risks and making informed decisions. To the authorsâ best knowledge, this is the first study to utilize time-series transformers for forecasting NFTs based on macroeconomic factors.
The recent economic downturn due to the labour disruption caused by the COVID-19 pandemic has again brought challenges to individual and household financial stability. Motivated by the controversial view regarding the reliability of crypto investments in times of market turbulence, we examined whether investing in cryptocurrencies would be a good option for improving individual financial satisfaction during an economic downturn. Utilizing data from the most recent 2021 cohort of the National Financial Capability Study (NFCS2021) and an instrumental variable design, we find that crypto investments relate to a lower level of financial satisfaction. Moreover, while people with higher income levels are more financially satisfied, working during the pandemic is associated with a lower level of financial satisfaction. In addition to the well-known volatile feature of cryptocurrencies, our findings provide additional insights about their influence on individual financial well-being during economic downturns.