• A recently developed advanced stochastic volatility modeling is utilized for cryptocurrency volatility analysis. • The suggested Bayesian Markov Chain Monte Carlo (MCMC) sampling approach proves to be effective. • The modeling accurately captures the dynamics of stochastic volatility. • We incorporate the market risk method within the Basel IV regulations. We apply stochastic volatility modeling enriched with leverage and an asymmetrically heavy-tailed distribution to analyze the returns of Bitcoin and Ethereum. Our methodology leverages the generalized hyperbolic skew Student’s t-distribution (GH-ASV-skw-st) framework, as proposed by Nakajima and Omori (2012), employing a Bayesian Markov chain Monte Carlo (MCMC) sampling technique for effectiveness evaluation. The GH-ASV-skw-st model is demonstrated to adeptly capture the stochastic volatility patterns present in the returns of cryptocurrencies. After validation with several diagnostics and robustness checks, we illustrate the model’s suitability for high-volatility series by capturing asymmetry, leverage effects, and tail risk. Our findings indicate that the model fits the data more precisely than traditional models and provides a more reliable foundation for risk measures essential to portfolio management, such as Value at Risk (VaR) and Expected Shortfall (ES).
Ken Huang, Youwei Yang, Fan Zhang, Xi Chen · 5 authors
Chapter 6 provides a comprehensive overview of tokenomics, analyzing the economic models and incentive mechanisms underlying tokens and cryptocurrencies. It explores the classification, functions, supply and demand dynamics, and financial aspects of various token types, including utility, governance, platform, stablecoins, NFTs, and meme coins. The interplay between tokenomics and Decentralized Finance (DeFi) is examined, highlighting considerations such as staking rewards and yields. Innovations such as "play to earn" gaming are covered but also their risks such as sustainability and Ponzi schemes. The impact of community sentiment and conviction on valuation is analyzed through meme coins and viral hype. Overall, this chapter offers crucial insights into the foundational economics of the Web3 ecosystem, grounded in a nuanced understanding of the incentive structures and value drivers behind diverse crypto tokens and assets. Both the technological potential and limitations are critically appraised. The key takeaway is an ability to comprehensively evaluate tokenomics across dimensions such as utility, supply, demand, distribution, financials, sentiment, and regulation.
Cryptocurrency trading has gained significant adhesion in financial markets, making it essential to understand the factors influencing trading intentions. This study investigates the psychological and knowledge-based determinants of trading intentions towards Beldex coins among crypto traders in India. This study aims to evaluate how risk management, hedonic motivation, investment desire, market knowledge, peer participation, and earning desires impact trading intentions. A survey was conducted with 369 crypto traders in India, and multiple regression analysis was employed to analyze the data. The results indicate that all six factors significantly influence trading intentions, with risk management (β = 0.342, p < 0.001) and earning desires (β = 0.378, p < 0.001) having the strongest impact on Indian Cryptocurrency market arbitrage. The regression model explained 53% of the variance in trading intentions (R² = 0.53). Cryptocurrency market information is analyzed through the CoinGecko tool that provides charts, market capitalization, and blockchain data; multiple regression analysis is utilized to test the hypothesized relationships. This study reveals that traders’ investment decisions in cryptocurrencies are primarily driven by financial motivations, including potential high returns, diversification, and inflation hedging, as well as technological factors of decentralized finance, blockchain technology, and digitalized transactions. AcknowledgmentThe authors would like to convey their gratitude to Prof. Balakumar Pitchai, Director/Research, Training & Publications at the Office of Research & Development, Periyar Maniammai Institute of Science & Technology (Deemed to be University), India for his suggestions to improve the language of the manuscript.
This research examines the influence of herd mentality on market volatility, with a particular emphasis on the behavioral finance principles that contribute to the volatility of cryptocurrency markets. The participation of a diverse and global group of participants in cryptocurrency markets, in contrast to traditional financial markets, frequently results in significant volatility and speculative trading. This research investigates the extent to which investors exhibit a flocking tendency, which is the propensity of individuals to follow the actions of the majority rather than relying on their own independent analysis. The research employs sentiment analysis on market data and social media activity, as well as econometric models, to quantify the extent of herding during periods of elevated market volatility. The results should enable the development of risk-mitigation strategies during these unpredictable periods and provide insight into the cognitive factors that influence decisions regarding bitcoin investments.
In this research work, we developed a predictive model for digital currency prices, involving daily closing price as a function of time. We used the Geometric Brownian motion stochastic differential equation which was solved using inbuild functions in Microsoft Excel. While we used the Bitcoin as our case study, our model was able to predict the daily closing prices of Bitcoin to a reasonable degree of accuracy. We equally observe that the time dependent Geometric Brownian motion stochastic differential equation cannot give digital currency traders and investors a clue on when to trade off their digital assets. Thus, it become very risky using our model to make well informed trading decisions. We therefore, recommend that for minimum risk, trades and investors in digital currencies should consider a combination of other signal tools to take more informed and less risky trading decisions.
Agoestina Mappadang, Bayu Adi Nugroho, Setyani Dwi Lestari, Elizabeth Elizabeth · 5 authors
A significant amount of historical returns is needed for the generalized autoregressive conditional heteroscedasticity (GARCH) models to be calibrated. Newer cryptocurrencies, such as non-fungible tokens (NFTs), have relatively limited data to create robust parameter estimates. This study uses a newly developed method, the exponentially weighted moving average (EWMA) model, that takes into account the fat-tailed distributions of returns and volatility response to forecast Value-at-Risk (VaR) and Expected Shortfall (ES). We employ thorough back tests of daily VaR and ES forecasts, which are widely utilized for regulatory approval and are considered to be industry standards. We also use loss function ratios to select the best model. Our results indicate that simpler models are just as good as the complicated ones, provided the simpler models capture fat-tailed distributions of returns. The primary findings hold up through several tests.
Marcin Wątorek, Marcin Królczyk, Jarosław Kwapień, Tomasz Stanisz · 5 authors
Multifractality is a concept that helps compactly grasping the most essential features of the financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on the centralized exchanges. A new element that adds complexity to cryptocurrency markets is the possibility of decentralized trading. Based on the extracted tick-by-tick transaction data from the Universal Router contract of the Uniswap decentralized exchange, from June 6, 2023, to June 30, 2024, the present study using Multifractal Detrended Fluctuation Analysis (MFDFA) shows that even though liquidity on these new exchanges is still much lower compared to centralized exchanges convincing traces of multifractality are already emerging on this new trading as well. The resulting multifractal spectra are however strongly left-side asymmetric which indicates that this multifractality comes primarily from large fluctuations and small ones are more of the uncorrelated noise type. What is particularly interesting here is the fact that multifractality is more developed for time series representing transaction volumes than rates of return. On the level of these larger events a trace of multifractal cross-correlations between the two characteristics is also observed.
The Bitcoin halving is a key event in the Bitcoin ecosystem that occurs approximately every four years or after all 210,000 blocks have been mined. This event is reduced to a double reward that miners receive for adding a new block to the blockchain, with the aim of controlling inflation and maintaining Bitcoin's deflationary model. This event helps to reduce the rate of issuance of new coins, which could theoretically lead to an increase in the price of Bitcoin, and to reduce the supply of constant or growing demand.
This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distribution (GPD) and the Gaussian kernel is fitted to the standardised residuals from an eGARCH(1, 1) model. The GPD is fitted to the tails while the Gaussian kernel is used in the central parts of the data set. The Gumbel copula parameter is estimated to be α=1.007, implying that the two currencies are independent. At 90%, 95%, and 99% levels of confidence, the portfolio’s diversification effects (DE) quantities using value at risk (VaR) and expected shortfall (ES) show that there is evidence of a reduction in losses (diversification benefits) in the portfolio compared to the risk of the simple sum of single assets. These results can be used by fund managers, risk practitioners, and investors to decide on diversification strategies that reduce their risk exposure.
Barbara Będowska-Sójka, Piotr Wójcik, Sabrina Giordano
Abstract This study aims to explore the dependencies on the cryptocurrency market using social network tools. We focus on the correlations observed in the cryptocurrency returns. Based on the sample of cryptocurrencies listed between January 2015 and December 2022 we examine which cryptos are central to the overall market and how often major players change. Static network analysis based on the whole sample shows that the network consists of several communities strongly connected and central, as well as a few that are disconnected and peripheral. Such a structure of the network implies high systemic risk. The day-by-day snapshots show that the network evolves rapidly. We construct the ranking of major cryptos based on centrality measures utilizing the TOPSIS method. We find that when single measures are considered, Bitcoin seems to have lost its first-mover advantage in late 2016. However, in the overall ranking, it still appears among the top positions. The collapse of any of the cryptocurrencies from the top of the rankings poses a serious threat to the entire market.
The acceleration of the globalization process and the structural changes in technology that emerged in the 2000s have affected financial markets. This interaction in the financial markets has made the emergence of new financial assets necessary. According to the ARDL boundary test results, there is no significant relationship between cryptocurrency markets and stock returns in both the long and short term for the UK financial markets. For the German financial markets, it has been determined that there is a significant and positive long-term relationship between the cryptocurrency market assets Bitcoin and Tether and stock market returns. In the short term, no significant relationship has been detected. For the long term in the U.S. financial markets, it has been determined that there is a significant and positive relationship between Bitcoin, a cryptocurrency market asset, and stock market returns, while there is no significant relationship between Ethereum and Tether with stock market returns. In the short term, no significant relationship has been detected. These findings offer significant implications for policymakers, investors, and market analysts.
Mai H. Bui, Huy Pham, Binh Nguyen Thanh, Aviral Kumar Tiwari
Cryptocurrencies have emerged as a new financial asset class, and the literature in this area is increasing rapidly. This study examines the determinants and proposes a new approach to capture the scarcity effect of proof-of-work cryptocurrency return. We find that the scarcity effect is one of the major determinants of excess return. Besides the scarcity effect, our results indicate that market risk premium, momentum effect, size effect, investor attention, and mining costs effect are significant determinants of proof-of-work cryptocurrency excess return. In addition, we compare the effectiveness of three mimicking portfolios: size effect, momentum effect, and scarcity effect to their background factors. The findings show that compared to their background factors, size effect and scarcity effect mimicking portfolios have better-explaining power.
Arslan Farooq, M. Irfan Uddin, Muhammad Adnan, Ala Abdulsalam Alarood · 6 authors
This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhanced Temporal Fusion Transformer (ADE-TFT) model to estimate Bitcoin values more accurately. This research employs cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to comprehensively examine various aspects of cryptocurrency forecasting, including geopolitical implications, market sentiment analysis, and pattern detection in transactional datasets. The study demonstrates that the ADE-TFT model outperforms its lower-layer counterparts in terms of forecasting accuracy, with reduced Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) values, particularly when using a higher hidden layer configuration (h=8). The study emphasizes the importance of experimenting with different normalization strategies and utilizing various market-related data to enhance the model's performance. The results suggest that improving forecasting accuracy may require addressing these limitations and incorporating additional factors, such as market sentiment. By providing investors with more precise market predictions, the techniques and information presented in this research have the potential to significantly increase investor power in an unpredictable digital currency market, enabling wise investment choices.
This study presents a multifractal analysis of the Bitcoin price time series over the period of 2015 to 2024. The multifractal fluctuation analysis with detrending (MFDFA) method is widely used to study fractal properties in financial time series. The results of the MFDFA indicate that the multifractal spectrum of the Bitcoin price time series has a positive slope. The multifractal spectrum demonstrated greater volatility at small time intervals and more predictable behavior at large. The Hurst exponent, which is a measure of the long-term memory of the time series, is found to be 0.5191. This implies that the Bitcoin have weak autocorrelation and little tendency to trend. The results of the study provide new insights into the complexity of the Bitcoin market and contribute to the ongoing debate on the market efficiency of cryptocurrencies.
This paper examines the price impact of large block trades in cryptocurrency markets by using a natural experiment in Bitcoin provided by the Gemini exchange. The exchange introduced a block trading facility in 2018, but in December 2019, it changed the minimum size threshold that allows market participants to trade a block and report it with a delay. Consistent with theoretical predictions and earlier empirical findings, we largely confirm that the information content of large trades is significantly lower in the upstairs market than in the downstairs. In contrast with prior research in traditional markets, we find that delaying the reporting of a block traded away from the continuous book discourages informed trading and potentially decreases the informativeness of trading and, therefore, information efficiency. Further, we find that the newly implemented size requirement for upstairs trades increases the total market impact, thereby not working as the intended introduction of a block trading facility.
Cryptocurrencies wield significant influence in the financial sector, captivating the attention of investors and researchers alike. This comprehensive investigation delves deeply into the analysis of cryptocurrency data, aiming to unveil the intricate trends and patterns defining these dynamic digital assets. Employing a multifaceted strategy that integrates statistical methodologies, machine learning techniques, and visualisation tools, the study seeks profound insights into the cryptocurrency market. The dataset under scrutiny covers a broad spectrum of cryptocurrencies beyond Bitcoin, Ethereum, and Ripple. The exploration spans various dimensions of analysis, encompassing price volatility, trading volume, market capitalisation, and their correlation with external factors such as regulatory changes and macroeconomic indicators. Leveraging advanced statistical models, the research aims to identify correlations, anomalies, and predictive indicators crucial for informed decision-making in the volatile crypto landscape. Through the application of machine learning algorithms like clustering and time series analysis, it endeavours to uncover underlying patterns and forecast future market movements. Moreover, it scrutinises the impact of social media sentiment on cryptocurrency prices, recognising the mounting influence of online communities in shaping market perceptions. Utilisation of visualisation tools allows for the presentation of findings in an easily understandable manner, empowering stakeholders to grasp intricate relationships within the cryptocurrency ecosystem.
Understanding the interconnectedness of cryptocurrencies based on their underlying technology is crucial for effective portfolio management and risk assessment. To establish the tail dependence structure and risk spillover between cryptocurrencies, this paper used the daily closing prices of the top eight proof-of-stake-based cryptocurrencies and the top ten proof-of-work-based cryptocurrencies from September 22, 2020 to April 7, 2023. This study applied the C-vine copulas and CoVaR measures. The outcome of the copula findings for the proof-of-stake cryptocurrencies illustrates that Ethereum exhibits strong resilience during market downturns, acting as a buffer for other proof-of-stake cryptocurrencies with pairwise tail dependence coefficients ranging from 0.45 to 0.67. Bitcoin Cash emerges as a portfolio diversifier within the proof-of-work ecosystem, absorbing 45% to 75% of volatility spillovers. However, from the proof-of-stake CoVaR analysis, ETH, DOT, and MATIC rank highest in systematic importance before April 2022, signifying their significant risk transmission role, and for the proof-of-work CoVaR analysis, Bitcoin (BTC) is the primary risk transmitter in the cryptocurrency portfolio, having a positive CoVaR of 0.15. Ethereum and Bitcoin are identified as the dominant risk transmitters within their respective groups, highlighting their potential to amplify systemic risk. This study provides valuable insights for investors and policymakers navigating the increasingly complex cryptocurrency landscape.