Oleg P. Kultygin, Natalia N. Lyublinskaya, Elena N. Tokmakova, Alexander E. Trubin
The article deals with the research on global crypto-currency market and analysis of its development trends. Special attention was paid to national regulation of crypto-currency and activity connected with it, which is topical today due to introduction of new sanctions against Russia, including those in the field of crypto-exchanges. Through analyzing the development of finance blockchain-systems the authors identified the vector of their developing in the direction of raising the speed of work, upgrading algorithms of consensus, increasing security and control over the mining sphere and turnover of crypto-currency in Russia. A conclusion was drawn that it can foster the extended use of crypto-systems of distributed ledger by present day banks. As goals of the research the authors mentioned problems connected with the opportunity to use blockchain technologies in the credit and finance sector and forecasting effects of such use.
Volatility as a measure of financial risk is a crucial input for hedging, portfolio diversification, option pricing and the calculation of the value at risk. In this paper, we estimate the asymmetric and time-varying volatility for Bitcoin as the dominant cryptocurrency in the world market. A novel approach that explicitly separates the falling markets from the rising ones is utilized for this purpose. The empirical results have important implications for investors and financial institutions. Our approach provides a position-dependent measure of risk for Bitcoin. This is essential since the source of risk for an investor with a long position is the falling prices, while the source of risk for an investor with a short position is the rising prices. Thus, providing a separate risk measure in each case is expected to increase the efficiency of the underlying risk management in both cases compared to the existing methods in the literature.
Purpose This paper aims to improve how investors can better manage their exposure to bitcoin (BTC), given the growing importance of BTC and the accompanying high volatility of BTC. This paper tests whether altcoins can serve as safe havens and diversifiers against exposure to BTC. Design/methodology/approach Using daily returns of altcoins and BTC from 2014 to early 2022, this paper examines the relationship between altcoins and BTC in a GARCH regression framework. Findings This paper finds that altcoins act as reliable safe havens during periods of extremely negative BTC returns and provide BTC investors with diversification benefits during normal periods. The safe haven effect of altcoins is superior to that of conventional assets. This paper presents evidence that this safe haven property of altcoins can be attributed to the informational efficiency channel, which arose from the increased adoption of BTC by institutional investors. Research limitations/implications The study uses a data set from 2014 to early 2022. While the sample is among the largest samples in the literature on crypto assets and includes adequate BTC tail events to test the hypotheses, it may not capture more recent changes in the crypto markets. Practical implications The findings suggest that BTC investors can enjoy diversification and safe haven protections by including altcoins in their portfolios. Originality/value This paper’s focus on alternative cryptocurrencies (altcoins) as potential diversifiers and safe havens is original. The hypothesis about altcoins being better alternatives during extreme negative movements in BTC prices is a unique contribution. The test of the role of the information efficiency channel further enhances the paper’s originality.
Hamid Cheraghali, Péter Molnár, Mattis Storsveen, Florent Veliqi
We investigate the impact of cryptocurrency-related cyberattacks on the cryptocurrency market and traditional financial markets. The dataset consists of historical cyberattack data and trading data for twenty cryptocurrencies, three cryptocurrency uncertainty indices, five payment companies, four stock indices, a commodity index, and gold. We find that cyberattacks are associated with negative returns, increased volatility, and increased trading volume not only for the cryptocurrencies but also for the payment companies, the financial and technology sectors, and the general stock market. However, the impact of cyberattacks on cryptocurrencies has been decreasing over time, while the impact on payment companies and the financial sector has been increasing. Moreover, gold prices have shown a positive response to these cyberattacks. These results underscore the need for enhanced cybersecurity measures in the fintech sector and may inform both policymakers and market participants.
Shinta Amalina Hazrati Havidz, Maria Divina Santoso, T. Alexander, Caroline Caroline
Purpose This study aims to identify the financial attributes of non-fungible tokens (NFTs) as safe havens, hedges or diversifiers against traditional (stock indices, foreign exchange, gold and government bonds) and digital (Bitcoin and Ethereum) assets. Design/methodology/approach The quantile via moments was utilized, and the data spanned from 20 September 2021 to 31 January 2022. The authors incorporated feasible generalized least squares (FGLS) and difference-generalized method of moments (diff-GMM) as the robustness check. Findings Overall, NFTs offer strongly safe havens, hedging and diversifier attributes against cryptocurrencies, while weak properties for traditional assets. The specific findings are: (1) Bored Ape Yacht Club (BAYC) serves as a strong hedge for Bitcoin during market rise; (2) Mutant Ape Yacht Club (MAYC) serves as a strong safe haven against Bitcoin during market bull; (3) Crypto punk (CP) provides strong safe havens properties for gold during market turmoil while serving as a strong hedge against gold and Bitcoin on average and (4) the three blue-chip NFTs are powered by Ethereum blockchain, thus serving as a diversifier against Ethereum. Practical implications Bitcoin investors are suggested to include NFTs in their investment portfolio to mitigate the losses when Bitcoin falls. Meanwhile, the inclusion of crypto punk is advised for risk-averse investors who invest in gold. NFTs are powered by the Ethereum blockchain, indicating co-movement among them and thus, serve as diversifiers. Policymakers and regulators are suggested to watch closely over NFTs' great development and restructure the existing policies and thus, stabilization of asset markets can be achieved. Originality/value The originality aspects are: (1) focusing on the three blue-chip NFTs (i.e. BAYC, MAYC and CP) that are categorized as the largest NFTs by floor market capitalization; (2) testing the NFT attributes (safe havens, hedges or diversifiers) against traditional and digital assets, a.k.a., cryptocurrencies and (3) panel setting on 14 countries with the highest NFT users.
The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. FRM@Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.
Taha Zaghdoudi, Kais Tissaoui, M. Maâloul, Younés Bahou · 5 authors
This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin’s energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin’s energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin’s future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin’s energy consumption.
Zahra Ghorrati, Kourosh Shahnazari, Ahmad Esmaeili, Eric T. Matson
One of the simplest approach in Reinforcement Learning (RL) is updating Q-table using Bellman operator. While theoretical expectations hint at the potential convergence achieved by modeling the discrete Q-table with the Bellman operator, practical limitations surface in real-world scenarios. The main challenges associated with it include the exponential growth of the Q-table size with an increasing number of state dimensions and the inability to use the Q-table in continuous state spaces. Alternative approaches, such as employing neural networks to approximate the parameterized Q-function, may not necessarily result in convergence.In response to these challenges, this paper introduces an simple innovative methodology inspired by the Bellman method updating. The proposed method utilizes fuzzy rules to discretize the state space, leading to the direct use of the Bellman operator for updating the fuzzy neural network weights, effectively acting as the Fuzzy Q-table. Instead of approximating the Q-function utilizing neural network/deep neural network based on gradient approaches, the proposed method establishes a Fuzzy Q-table and updates it using the Bellman equation. This strategic decision helps to solve the convergence problem in addition to prevent entrapment in local minima problems, a common challenge faced by conventional gradient methods. The efficacy of the proposed approach is demonstrated through its application to trading in the Bitcoin Futures Market, showcasing its ability to navigate complexities and uncertainties. Beyond financial markets, this methodology presents a versatile solution applicable to a diverse range of reinforcement learning problems, addressing limitations faced by traditional Q-tables or DQN.
Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.
Recent studies document that cryptocurrencies offer an alternative store of value, medium of exchange and can be used to hedge against currency and price fluctuations. However, the frequent collapse of the crypto-market undermines its safe-haven characteristics, as investors’ fear and anxiety could intensify market volatility and trigger a financial crisis. Motivated by the current global vicissitudes, this study examines the impact of uncertainty and sentiment factors on price behaviour of cryptocurrencies. To estimate our model, we used daily, low, high and closing price data for major crypto projects, from January 2018 to January 2023. We show that economic and political uncertainty factors significantly drive crypto prices. Furthermore, the interaction between sentiment dynamics as expressed by investors on different social platforms has a significant adverse effect on the returns of the cryptocurrency market, and the impact is more pronounced for tokens within the same ecosystem. Using the asymmetric GARCH-MIDAS model and TVP-VAR, we also demonstrate the existence of a significant contagion among tokens within the same ecosystem when bad (or good) news occurs. Considering the massive unprotected losses incurred by crypto investors during crises, our results provide important insights into how portfolio managers can effectively design investment strategies.
A. M. Benarous, İ̇hsan Tolga Medeni, Tunç D. Medeni, Vildan Ateş
This study sheds light on the achievements of digital financial technologies and blockchain technology in the stock market. This study aims to examine the relationship between blockchain technology and macroeconomic variables, as well as the impact these variables have on stock market performance. For this, authors used the methodology of correlation and regression analysis, analyzing data on cryptocurrencies, the stock market and key paper exchange rates. The study confirms a significant correlation between blockchain dynamics, particularly cryptocurrency price fluctuations, and stock market performance, indicating that movements in digital asset classes such as Bitcoin and Ethereum have measurable impacts on traditional financial markets. Traditional economic indicators continue to play a crucial role in stock market behavior, with variables like inflation rates and GDP growth showing strong correlations with market performance. The results suggest a complex interplay between blockchain technology and macroeconomic indicators, emphasizing a growing interconnectedness between emerging digital financial products and economic measures. In addition, the findings are particularly relevant for investors, financial analysts, and policymakers, highlighting the need for a holistic market analysis approach that integrates both new technological advancements in blockchain and economic indicators. The study underscores the evolving influence of blockchain technology on traditional stock markets that encompass both new digital assets and economic frameworks. Moreover, further studies could explore the impact of blockchain technology on specific sectors within the stock market, such as technology, finance, and consumer goods.
This article is dedicated to a detailed analysis of market cycles in cryptocurrencies and their impact on investment strategies. The article thoroughly examines various stages of these cycles, their characteristics, and their interconnections with other economic factors. It explores the factors influencing the duration and intensity of these cycles, as well as methods of utilizing them to develop successful investment strategies. The research findings highlight the importance of understanding psychological factors such as FOMO (fear of missing out) and FUD (fear, uncertainty, and doubt), as well as the impact of halving on the cryptocurrency market. Investors who comprehend these aspects and adapt their strategies to the volatile market conditions can achieve success in their investments. The article also emphasizes the importance of in-depth analysis of market cycles for developing effective investment strategies. Special attention is given to the stages of accumulation, markup, distribution, and markdown in the cryptocurrency market, each of which has its unique characteristics and can be leveraged for profit maximization. The influence of regulatory changes, technical innovations, and global financial events on these cycles is examined. The authors also analyze the interaction of supply and demand, particularly how the reduction in mining rewards (halving) affects cryptocurrency values. The study shows that understanding market cycles allows investors to better predict market movements and make more informed decisions. Examining the impact of psychological factors on investor decisions is crucial for avoiding unjustified losses and maximizing gains. Additionally, the article considers long-term investment strategies that take into account halving periods, which can lead to significant increases in asset values. Based on the analysis of cryptocurrency market cycles and the influence of various factors, the work concludes that a deep understanding of these processes is necessary for successful investing. The recommendations provided in the article can be useful for investors looking to develop resilient and effective strategies in the highly volatile cryptocurrency market.
Following the 2008 financial crisis, cryptocurrencies have brought significant innovations to the financial system and created an important test for central banks. Adopted by users in a short period of time and traded in high volumes, cryptocurrencies have a more liberal paradigm than the current financial system. Central banks have initiated studies on CBDCs in order to prevent the risks posed by cryptocurrencies and to take advantage of the opportunities created. Therefore, the development of CBDCs, which are characterized as the turning point of the financial system, is associated with the risks and opportunities created by cryptocurrencies. In this sense, this study aims to evaluate the risks and opportunities in the current situation by first addressing the relationship between the development process of CBDCs and cryptocurrencies. In addition, the study also aims to contribute to the development of CBDCs and literature by theoretically analyzing the possibilities that may occur in the future.
In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.
Dzuljastri Bin Abdul Razak, Mustafa Omar Mohammed, Yavuz Türkan, Ethem KILIÇ
With technology development, investment tools also vary. Money and capital market instruments are at the forefront of these, and virtual currencies have become investment tools. Because virtual currencies are not religiously permissible by many organizations causes the devout people to stay away from them. This study investigates the return and volatility interaction between Islamic Indices and Bitcoin in Türkiye and Malaysia. The study uses weekly data for the period 24 November 2013 – 2 January 2022 obtained from investing.com. Multivariate Dynamic Conditional Correlation (DCC-GARCH) and multivariate dynamic stochastic volatility models were used to determine the volatility dispersion between Islamic indices and Bitcoin. Results show that the volatilities of Türkiye Islamic Index, Malaysia Hijrah Shariah Index and Bitcoin are permanent. Volatility of Bitcoin, however, has no effect on the return of the Türkiye Islamic Index and the Malaysian Hijrah Shariah Index. Likewise, the volatility of Islamic indices does not affect the return of Bitcoin. According to the results of the DC-MSV model, the volatility of Islamic indices and the volatility of Bitcoin do not affect each other. This indicates that Islamic index investors and Bitcoin investors differ.
Non-fungible tokens are transferable rights to digital assets such as artwork, videos, in-game items, collectibles or music. Non-fungible tokens relate only to a specific unique item and carry information about the owner. The non-fungible token market has received widespread attention and has grown enormously since the beginning of 2021. Despite significant growth in the market, there needs to be more surveys, especially in the context of the Czech Republic. This article, therefore, aims to evaluate the level of awareness of non-fungible tokens in the Czech Republic. The paper presents the basics of the non-fungible token market, its potential and uncertainty, and the interdisciplinary nature of non-fungible token research. First, the characteristics of non-fungible tokens are described based on a literature review. The methodological part outlines an empirical analysis based on a quantitative survey in which 103 respondents in the Czech Republic took part. Based on the research results, it was found out that in the Czech Republic, there is low level of awareness of non-fungible tokens and also low level of trust in digital assets in general. In conclusion, it is possible to say that this article provides an overall understanding of the phenomenon of non-fungible tokens in the Czech Republic.
This study primarily explores the mechanisms of risk propagation among cryptocurrencies, unveiling for the first time the frequency dimension of risk propagation within the cryptocurrency market and identifying the role of oscillation frequency in this process. By employing Variational Mode Decomposition (VMD) and the DY spillover matrix to construct a complex network, the paper analyzes the frequency dimension risk propagation mechanisms of nine major cryptocurrencies from 2017 to 2023. Key findings include the significant risk propagation capabilities of Ethereum (ETH) and Bitcoin (BTC) during periods of high market volatility, while stablecoins such as Tether and USD Coin exhibit minimal risk propagation ability. Additionally, the characteristics of cryptocurrency risk propagation have been enhanced following the COVID-19 pandemic. Overall, the risk propagation of most cryptocurrencies is primarily realized through high-frequency oscillations. The robustness of the conclusions is verified using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results are significant for understanding the dynamic characteristics of the cryptocurrency market, predicting future market risks, and formulating risk management strategies. Furthermore, the methodology and findings of this study provide new perspectives and tools for exploring the risk propagation relationships among cryptocurrencies.
To what extent does the collapse of a digital token spread contagion across cryptocurrency markets? How do markets incorporate information in this turbulent setting? We examine contagion effects across major digital exchanges during the collapse of the FTX exchange and its token, FTT. We find evidence of contagion across crypto exchanges. We also examine the information cascade effects of other crypto assets on FTX when nearly all withdrawals were prohibited. We find abnormal returns for major assets, indicating a flight to safety from less to more authoritative digital assets. The implications for traders, exchanges, and policymakers are discussed.
In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.