The finite sample effect on the Hurst exponent (HE) of realized volatility time series is examined using Bitcoin data. This study finds that the HE decreases as the sampling period $Ī$ increases and a simple finite sample ansatz closely fits the HE data. We obtain values of the HE as $Ī\rightarrow 0$, which are smaller than 1/2, indicating rough volatility. The relative error is found to be $1\%$ for the widely used five-minute realized volatility. Performing a multifractal analysis, we find the multifractality in the realized volatility time series, smaller than that of the price-return time series.
Contemporary financial systems operate in complex environments marked by volatility, uncertainty, complexity, and ambiguity, which elevates the cost of trial and error and exposes the limits of siloed data and centralized control. Considering the complexity challenges for the legacy financial system, we propose the parallel financial systems, which are grounded in the theory of parallel intelligence and utilizes the ACP (Artificial systems, Computational experiments, and Parallel execution) methodology to conduct financial execution and resource coordination through the interaction and co-evolution of real-world and artificial systems. Based on this paradigm, we construct a corresponding technical architecture that integrates cutting-edge intelligent technologies represented by AI agents with decentralized technologies provided by blockchain, smart contracts and decentralized autonomous organizations (DAOs). To further demonstrate the applicability for the legacy enterprise, we detail the system workflow, from individual operation to group coordination, with an illustrative example of how the parallel financial systems are utilized along a project lifecycle. This work serves as a theoretical basis and design roadmap for verifiable, privacy-preserving, and interoperable financial intelligence.
The decentralized financial system that forms the basis of crypto assets is highly volatile and constantly changing, making the process of valuation one of the thorniest challenges of the modern-day finance sector. This study deals primarily with the analysis of several factors through which the value of cryptocurrency is determined and also seeks to stress how much caution is needed while addressing both risks and rewards. By employing multivariable and stochastic analyses, the study deconstructs the various factors at play in the bitcoin market. It is clear from the result that the volatile environment of the digital currency is a combination of oneās mood, new laws, and technological development. Thus, the findings underline the information that while it is possible to become an exception and make a brilliant rise at the financial top, there is also a large and often unpredictable downside involved. It is a valuable resource for the policymakers who have to come up with the legislation to regulate innovation without compromising the marketsā veracity, these results can be a useful tool for all investors who struggle to make the right decisions in the world of decentralized cryptocurrencies.
Forecasting the price of cryptocurrencies is a notoriously hard and significant problem, due to the rapid market growth and high volatility. In this article, we propose a methodology for predicting future values of cryptocurrency exchange rates by developing a Non-linear Autoregressive with Exogenous Inputs (NARX) prediction model that uses the most adequate external information. The exogenous variables considered are historical values of the exchange rate and a series of technical indicators. The selection of the most relevant external inputs is based on the computation of the mutual information indicator and estimated using the k-nearest neighbor method. The methodology employs a fine-tuned Long Short-Term Memory (LSTM) neural network as the regressor. We have used quantitative and trend accuracy measures to compare the proposed method against other state-of-the-art LSTM-based models. In addition, regarding the input selection process, the proposed approach was compared against the most commonly used one, which is based on the cross-correlation coefficient. A long series of experiments and statistical analyses proved that the proposed methodology is highly accurate and the resulting model outperforms the state-of-the-art LSTM-based models.
Purpose Cryptocurrency, an emerging asset class, is a virtual form of currency that uses cryptography for security and operates on decentralised networks based on blockchain technology. It offers both challenges and opportunities for investors, particularly in terms of diversification, risk management and potential returns. Considering this, the present study attempts to investigate the sentimental factors influencing cryptocurrency while unravelling the intricate interplay among these factors. Design/methodology/approach To achieve this, interpretive structure modelling (ISM) identifies the hierarchical model of critical sentimental factors, while Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) explores their dependency and driving power. Analytic hierarchy process (AHP) is adopted to rank the drivers. Findings Findings reveal that the pandemic, war, religiosity and economic uncertainty are top-level factors dominantly shaping cryptocurrency trends. Simultaneously, Google Search Trends and Herding emerge as the most dependent factors, influenced by sentiments that emerged from other factors. Practical implications The study unpacks implications, acknowledges limitations and proposes avenues for future research. Originality/value By exploring the interactive interrelationships among identified sentimental factors through ISM-MICMAC analysis and ranking via the AHP, this paper will have a great influence while contributing towards this evolving field.
Cryptocurrencies fluctuate in markets with high price volatility, which becomes a great challenge for investors. To aid investors in making informed decisions, systems predicting cryptocurrency market movements have been developed, commonly framed as feature-driven regression problems that focus solely on historical patterns favored by domain experts. However, these methods overlook three critical factors that significantly influence the cryptocurrency market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations, which can affect investorsā collaborative behaviors, 2) overall market sentiment, heavily influenced by news, which impacts investorsā strategies, and 3) technical indicators, which offer insights into overbought or oversold conditions, momentum, and market trends are often ignored despite their relevance in shaping short-term price movements. In this paper, we propose a dual prediction mechanism that enables the model to forecast the next dayās closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Furthermore, we introduce a novel refinement mechanism that enhances the prediction through market sentiment-based rescaling and fusion. In experiments, the proposed model achieves state-of-the-art performance (SOTA), consistently outperforming ten comparison methods in most cases. Our code and data can be found at https://github.com/aamitssharma07/SAL-Cryptopulse
Abstract This paper tests the extent to which the ability to correctly predict subsequent bitcoin (BTC) return signs is dependent upon historic BTC return trajectories. Using topological data analysis ball mapper (TDABM), we demonstrate that the performance of random forest and logit regression models varies according to return trajectory. A novel use of TDABM as a forecast model shows that mapping historic return trajectories can also produce more accurate directional return forecasts. Our approach highlights how the predictability of BTC price change direction is dependent on return trajectories. Visualizing historic return trajectories when forming and evaluating return forecasts is imperative.
We study the temporal evolution of the holding-time distribution of bitcoins and find that the average distribution of holding-time is a heavy-tailed power law extending from one day to over at least 200 weeks with an exponent approximately equal to 0.9, indicating very long memory effects. We also report significant sample-to-sample variations of the distribution of holding times, which can be best characterized as multiscaling, with power-law exponents varying between 0.3 and 2.5 depending on bitcoin price regimes. We document significant differences between the distributions of book-to-market and of realized returns, showing that traders obtain far from optimal performance. We also report strong direct qualitative and quantitative evidence of the disposition effect in the Bitcoin Blockchain data. Defining age-dependent transaction flows as the fraction of bitcoins that are traded at a given time and that were born (last traded) at some specific earlier time, we document that the time-averaged transaction flow fraction has a power law dependence as a function of age, with an exponent close to ā1.5, a value compatible with priority queuing theory. We document the existence of multifractality on the measure defined as the normalized number of bitcoins exchanged at a given time. ā¢ Age (holding time) distribution of bitcoin is significant character in bitcoin transaction. ā¢ We find that the average distribution of bitcoin holding time is a heavy-tailed power law with an exponent about 0.9. ā¢ The analysis of book-to-market and realized return shows that bitcoin traders obtain far from optimal performance. ā¢ The time-averaged transaction flow fraction has a power-law dependence as a function of age, with an exponent about 1.5. ā¢ The multifractality exists on the measure defined as the normalized number of bitcoins exchanged at a given time.
Md Zahidul Islam, Md. Shahidul Islam, Md Abdullah Al Montaser, Md Rasel Ā· 7 authors
The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learning techniques provide the most accurate and reliable predictions under different market conditions, with a full understanding of their strengths and limitations. The dataset gathered for analyzing and forecasting cryptocurrency prices entailed diverse and extensive data points, affirming a well-rounded foundation for machine learning algorithms. Particularly, current and historic price data from cryptocurrency exchanges such as Binance, Coinbase, and Kraken, together with trading metrics important for the definition of market dynamics. Aggregated data from financial databases such as Coin-Market-Cap, Crypto-Compare, and Yahoo Finance comes in structured form and presents historical consistency, hence perfectly fitting for machine learning applications. Models considered for the study ranged from simple, linear methods to complex ensemble and gradient-boosting algorithms. Precise performance evaluation is a proxy of its reliability and correctness of effectiveness in price predictions in a cryptocurrency market. Several measures of the effectiveness of prediction have been used here for assessing the different properties of models' performance: Precision, Recall, and F1-Score. Additional performance metrics were applied to evaluate the models in this study including Mean Absolute Error, Root Mean Squared Error, and R-squared. The gradient Boosting model did an excellent job as compared to other algorithms, as the values of accuracy, precision, recall, and F1-score for both classes were quite high. All three models have quite a relatively low MAE and RMSE, which means that each model is remarkably good at predicting the target variable. The application of machine learning models in the sphere of cryptocurrency price prediction might finally give very important implications to investors and stakeholders of the financial market in the USA, especially since recently, cryptocurrencies have been made integral parts of both individual and institutional investors' portfolios and trading strategies. To investors, it may provide indications of the entry and exit points, diversification of portfolios, and risk management by using machine learning models. Consolidation with the financial system will indeed mark a strategic shift toward data-driven decision-making in investment management and trading by integrating machine learning models into the financial systems.
This study analyses the effect of cryptocurrency on the Nigerian economy. The development of crypto-currency as a means of exchange without legal backing and invisibility of the identity of operators has posed peculiar challenges, such as illicit financial flow and terrorism, amongst others, to the country. This study, therefore, sought to examine the effect of crypto-currency on the Nigerian economy. The study hinged on social exchange theory. Secondary data were obtained from the CBN statistical bulletin and Global Financial Integrity Report for a period of six years from 2015 to 2020. The data were analyzed using a simple regression model. The result shows that R is 7.9%, which means that there is a low positive relationship between crypto-currency and the level of economic development in Nigeria. It further shows an adjusted R square of -38.4 which depicts that crypto-currency has a low inverse effect on the level of economic development in Nigeria. In conclusion, the computed p-value of 0.945, which is higher than the set p-value of 0.05, shows that crypto-currency does not have a significant effect on the level of economic development in Nigeria. Hence, it is recommended that, in order to sustain economic development from the activities of crypto-currency in Nigeria, the CBN needs to ensure that laws and mechanisms are put in place to capture the activities of crypto-currency in the country adequately.
Sibtain Syed, Syed Muhammad Umar Talha, Arshad Iqbal, Naveed Ahmad Ā· 5 authors
Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by expectations of value, risk assessment, and potential returns. This study also aims to identify a resourceful technique to efficiently forecast prices of cryptocurrencies such as Bitcoin (BTC), Binance (BNB), Ripple (XRP), and Tether (USDT) using optimal data-driven models (LSTM, GRU, and BiLSTM models) using bias correction. The proposed methodology includes collecting cryptocurrency data and precious metal data from Coindesk and BullionVault, respectively, and then finding the optimal model input combination for each cryptocurrency by lag adjustment and correlating feature selection. Hyperparameter tuning was performed by trial-and-error technique, and an early stopping function was applied to minimize time and space complexity. Bias correction (BC) is applied to model-forecasted price trends to reduce errors in evaluation and to enhance accuracy by adjusting model outputs to reduce prediction bias, providing a refined alternative to traditional unadjusted deep learning methods. GRU-BC outperformed other models in forecasting Bitcoin (with MAE 25.291, RMSE 31.266, MAPE 2.999) and USDT (with MAE 0.0006, RMSE 0.0012, MAPE 0.0622) price trends, while BiLSTM-BC was superior in predicting XRP (with MAE 0.0129, RMSE 0.0171, MAPE 2.9013) and BNB (with MAE 2.2759, RMSE 2.8357, MAPE 1.9785) market price flow.
This study investigates the efficiency of cryptocurrency markets by examining the presence and exploitability of arbitrage opportunities. Using high-frequency data from the Binance Exchange, we implement a triangular arbitrage strategy, considering Bitcoin, Litecoin, and the U.S. Dollar. We find 4,879 possible arbitrage opportunities. Although these findings suggest potential inefficiencies, transaction costs and limited trading volumes in the order book eliminate their profitability. Consequently, centralized cryptocurrency markets exhibit a high degree of efficiency. Moreover, our results suggest that the mere number of triangular arbitrage opportunities is not a reliable indicator of market inefficiency. ā¢ Triangular arbitrage opportunities at cryptocurrency exchanges do exist. ā¢ Transaction costs, potential slippage and limited trading volumes in the order book eliminate their profitability. ā¢ Centralized cryptocurrency markets exhibit a high degree of efficiency. ā¢ The mere number of triangular arbitrage opportunities is not a reliable indicator for market efficiency.
Iulia Cristina Iuga, Raluca Andreea NeriÅanu, Larisa-Loredana Dragolea
This study explores the volatility spillover effects between clean and dirty cryptocurrencies and key financial indices, specifically focusing on Green Finance Indices (such as solar, wind, and nuclear) and Economic Indices (like the Baltic Dry Index and CRB Index). Employing the diagonal BEKK model and the DCC GARCH model, the study spans data from February 17, 2020, to September 30, 2024, to analyze how cryptocurrencies, categorized by their environmental impact, influence these indices. The results reveal significant volatility spillovers from both clean and dirty cryptocurrencies, with clean cryptocurrencies such as Cardano showing a stabilizing effect, while dirty cryptocurrencies like Bitcoin exhibit more pronounced and asymmetric volatility impacts on green finance indices. Furthermore, the persistent correlations identified through the DCC GARCH model highlight the dynamic relationships between cryptocurrency markets and green finance, suggesting that shocks in cryptocurrency volatility can significantly affect the financial dynamics of renewable energy investments. These insights are valuable for portfolio diversification and risk management, indicating that certain cryptocurrencies may serve as effective hedging instruments against risks in green finance. This study contributes to a deeper understanding of the interaction between digital financial assets and sustainable investments, offering practical implications for investors, financial managers, and policymakers committed to achieving Sustainable Development Goals (SDGs).
Francesco Maria De Collibus, Carlo Campajola, Guido Caldarelli, Claudio J. Tessone
We explore patterns, regularities, and correlations in the evolving landscape of Ethereum-based tokens, both ERC-20 (fungible) and ERC-721 (non-fungible) to understand the factors contributing to the rise in certain tokens over others. By applying network science methodologies, minimum spanning trees, econometric autoregressiveāmoving-average (ARMA) models, and the study of accumulation processes, we are able to highlight a rising centralisation process. Not only do ārichā tokens get richer, but past transactions also emerge as more reliable predictors of new transactions. Our findings are validated across different samples of tokens.
Muhammad Yousaf, Muhammad Imran Tariq, Abdul Jabbar, Syed Qaiser Jalil
This comprehensive review paper explores the diverse landscape of cryptocurrency forecasting, tracing its evolution from an alternative to traditional monetary systems to its significant growth in the global financial arena. It consolidates existing research by categorizing and analyzing 234 scholarly articles, organizing them into machine learning, deep learning, deep reinforcement learning, and statistical methodologies, and evaluating the related metrics. The case study titled āExamining the performance differences between backtesting and forward testingā highlights the challenges investors face, as strategies that appear effective in backtesting often fail in practical use. Another case study, āSocial Data Exploration in Cryptocurrency Trends,ā examines how social media data can provide insights into market movements and investor sentiment, revealing the impact of social trends on cryptocurrency prices. The findings section provides a detailed view, illuminating trends such as yearly publication rates, methodological distributions, input features, training/testing splits, the total number of data samples considered, and forecasting time horizons. This survey paper serves as a valuable resource, providing researchers and investors with a solid foundation for understanding and navigating the dynamic field of cryptocurrency forecasting.
Decentralized Finance (DeFi) has undergone significant expansion, evolving from a niche market into a complex alternative financial ecosystem. This burgeoning landscape now encompasses a diverse array of financial services, including decentralized exchanges, lending and borrowing platforms, stablecoins, derivatives, yield optimization services, prediction markets, and privacy-enhancing technologies such as token mixers. While the total value locked in DeFi protocols-estimated at approximately 77 billion USD-underscores its increasing significance, it simultaneously highlights the critical necessity for robust security measures.
Yelizaveta Vitulyova, Inabat Moldakhan, P. E. Grigoriev, Ibragim Suleimenov
It is shown that the statistics of transactions of the Ethereum cryptocurrency obeys well-defined patterns. Log dependency <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ln</mml:mi><mml:mo>ā”</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math> of the number of users <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:math> who carried out <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> transactions with the use of Ethereum cryptocurrency during a specific month on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:mi>ln</mml:mi><mml:mo>ā”</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math> is a linear one with high accuracy: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:mrow><mml:mi>ln</mml:mi><mml:mo>ā”</mml:mo><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mi>b</mml:mi><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mfenced></mml:mrow><mml:mi>ln</mml:mi><mml:mo>ā”</mml:mo><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mi>a</mml:mi><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math> . Similar statistical patterns are obtained for bitcoin transactions. It has also been established that the behavior of the coefficient <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:math> appearing in this dependence corresponds with high accuracy to the Bass diffusion model, which describes the dynamics of innovation implementation. It is shown that after the completion of the initial stage of the implementation of the Ethereum and bitcoin cryptocurrencies (since the beginning of 2018), the values of the coefficients a and b are approaching constants. On this basis, a method is proposed for identifying space weather factors on the economic behavior of the human population. In particular, it is shown that the analysis of the cross-correlation between the ratio <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math> for the Ethereum cryptocurrency and the Ap-index of geomagnetic activity gives an example of additional tools allowing to reveal the influence of space weather factors on the economic behavior of people on a global scale.
Blockchains have sparked global interest in recent years, gaining importance as they increasingly influence technology and finance. This thesis investigates the robustness of blockchain protocols, specifically focusing on Ethereum Proof-of-Stake. We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have permanent conflicting blocks, and Liveness, which guarantees the continuous addition of new reliable blocks. Our research addresses the gap between traditional distributed systems approaches, which classify agents as either honest or Byzantine (i.e., malicious or faulty), and game-theoretic models that consider rational agents driven by incentives. We explore how incentives impact the robustness with both approaches. The thesis comprises three distinct analyses. First, we formalize the Ethereum PoS protocol, defining its properties and examining potential vulnerabilities through a distributed systems perspective. We identify that certain attacks can undermine the system's robustness. Second, we analyze the inactivity leak mechanism, a critical feature of Ethereum PoS, highlighting its role in maintaining system liveness during network disruptions but at the cost of safety. Finally, we employ game-theoretic models to study the strategies of rational validators within Ethereum PoS, identifying conditions under which these agents might deviate from the prescribed protocol to maximize their rewards. Our findings contribute to a deeper understanding of the importance of incentive mechanisms for blockchain robustness and provide insights into designing more resilient blockchain protocols.
Khaladdin Rzayev, ĪĪøĪ±Ī½Ī¬ĻĪ¹ĪæĻ Ī£Ī¬ĪŗĪŗĪ±Ļ, Andrew Urquhart
The network effect, measured by usersā adoption, is considered an important driver of cryptocurrency market dynamics. This study examines the role of adoption timing in cryptocurrency markets by decomposing total adoption into two components: innovators (early adopters) and imitators (late adopters). We find that the innovatorsā component is the primary driver of the association between user adoption and cryptocurrency returns, both in-sample and out-of-sample. Next, we show that innovatorsā adoption improves price efficiency, while imitatorsā adoption contributes to noisier prices. Furthermore, we demonstrate that the adoption model captures significant cryptocurrency market phenomena, such as herding behaviour, more effectively, making it better suited for forecasting models in cryptocurrency pricing. These results suggest that our methodology for linking early and late adopters to market dynamics can be applied to various domains, offering a framework for future research at the intersection of operational research and financial markets.
Cryptocurrencies are known for their volatility and instability, making them an attractive but risky investment for traders, analysts, and researchers. As the allure of Bitcoin (BTC) and other cryptocurrencies continues to grow, so does the interest in predicting their prices. To forecast the market rate and sustainability of cryptocurrencies, this study uses machine learning-based time series analysis. The study employs forecast periods ranging from 1 to 10-minutes to categorize the consistency of the market. High-frequency pricing of cryptocurrencies is anticipated with a timestep of up to 10 seconds using various deep learning (DL) models. A hybrid model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is created and compared with standard LSTM and GRU models. Mean squared error (MSE) is the benchmark for estimating the models' performance. The study achieves better results than benchmark models, with MSE values for BTC, Cardano (ADA), and Cosmos (ATOM) in a 5-minute window size being 0.000192, 0.000414, and 0.000451, respectively, and for a 10-minute window size being 0.000212, 0.000197, and 0.000746. Compared to existing models, the suggested model offers a high price predicting accuracy. This study on crypto price prediction using machine learning applications is a preliminary investigation into the topic.
Venator Santiago, Michel Charifzadeh, Tim Alexander Herberger
Purpose This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin, Ether, a Decentralized Finance index and U.S.-sourced conventional assets stocks, bonds, oil, gold and the dollar index. The primary research question addresses whether correlations increased between digital and conventional assets during the collapse. Design/methodology/approach A dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model was used to examine changes in volatility correlations during the market crash. Specifically, a data set of 1,442 close prices from 30-minute interval candles of digital and conventional asset prices are considered to provide a granular view of market dynamics during the sample period from January 3rd, 2022, to May 31st, 2022, including the crash event. Findings While the dynamic conditional correlation plots of the model indicate increased volatility, the results do not offer sufficient evidence to confirm an increase in correlations between digital and conventional assets during the Terra-Luna downfall. Furthermore, the authors confirm Bitcoinās role as a diversifier with oil and observe the dollar index maintaining a negative correlation with Bitcoin during the crash, supporting Bitcoinās function as a hedge against the U.S. dollar. However, the findings during the crash diverge from previous studies, reflecting shifts in correlation patterns in broader market downturns. Specifically, the authors identify the need for adaptive capital allocation strategies, as goldās oscillation during the period suggests it may not serve as an effective hedge during black swan events. Practical implications The findings provide insights for investors, financial institutions and regulators to improve risk management, portfolio diversification, trading strategies and the formulation of consumer protection regulations. In addition, the results underscore the challenges of mitigating risks beyond regulatory measures and emphasize the importance of exercising caution for investors. Originality/value This study addresses the research gap in changes between conventional and digital asset volatility correlations during collapses in the digital asset space.
ā¢ A recently developed advanced stochastic volatility modeling is utilized for cryptocurrency volatility analysis. ā¢ The suggested Bayesian Markov Chain Monte Carlo (MCMC) sampling approach proves to be effective. ā¢ The modeling accurately captures the dynamics of stochastic volatility. ā¢ 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 Studentās 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 modelās 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).
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 (Ī² = 0.342, p &amp;lt; 0.001) and earning desires (Ī² = 0.378, p &amp;lt; 0.001) having the strongest impact on Indian Cryptocurrency market arbitrage. The regression model explained 53% of the variance in trading intentions (RĀ² = 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 tradersā 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 &amp;amp; Publications at the Office of Research &amp;amp; Development, Periyar Maniammai Institute of Science &amp;amp; Technology (Deemed to be University), India for his suggestions to improve the language of the manuscript.