Search Results (612)

Bitcoin’s extreme price volatility presents significant challenges for investors and traders, necessitating accurate predictive models to guide decision-making in cryptocurrency markets. This study compares the performance of machine learning approaches for Bitcoin price prediction, specifically examining XGBoost gradient boosting, Long Short-Term Memory (LSTM), and GARCH-DL neural networks using comprehensive market data spanning December 2013 to May 2025. We employed extensive feature engineering incorporating technical indicators, applied multiple machine and deep learning models configurations including standalone and ensemble approaches, and utilized cross-validation techniques to assess model robustness. Based on the empirical results, the most significant practical implication is that traders and financial institutions should adopt a dual-model approach, deploying XGBoost for directional trading strategies and utilizing LSTM models for applications requiring precise magnitude predictions, due to their superior continuous forecasting performance. This research demonstrates that traditional technical indicators, particularly market capitalization and price extremes, remain highly predictive in algorithmic trading contexts, validating their continued integration into modern cryptocurrency prediction systems. For risk management applications, the attention-based LSTM’s superior risk-adjusted returns, combined with enhanced interpretability, make it particularly valuable for institutional portfolio optimization and regulatory compliance requirements. The findings suggest that ensemble methods offer balanced performance across multiple evaluation criteria, providing a robust foundation for production trading systems where consistent performance is more valuable than optimization for single metrics. These results enable practitioners to make evidence-based decisions about model selection based on their specific trading objectives, whether focused on directional accuracy for signal generation or precision of magnitude for risk assessment and portfolio management. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

This study investigates the multifractal characteristics and nonlinear cross-correlations between two major carbon credit indices—S&P Global Carbon Index and EEX Global Carbon Index—and key global financial assets: the Euro/US Dollar exchange rate, Dow Jones Industrial Average, gold, Western Texas Intermediate, and Bitcoin. Using daily data from August 2020 to June 2025, we apply the Asymmetric Multifractal Detrended Cross-Correlation Analysis framework to examine the strength, asymmetry, and persistence of interdependencies across varying fluctuation magnitudes. Our findings reveal consistent multifractality in all asset pairs, with stronger multifractal spectra observed in those linked to Bitcoin and Western Texas Intermediate Crude Oil price. The analysis of generalized Hurst exponents indicates higher persistence for small fluctuations and antipersistent behavior for large fluctuations, particularly in pairs involving the S&P Global Carbon Index. We also detect significant asymmetry in the cross-correlations, especially under bearish trends in Bitcoin and Western Texas Intermediate. Surrogate data tests confirm that multifractality largely stems from fat-tailed distributions and temporal correlations, with genuine multifractality identified in the S&P Global Carbon Index–Dow Jones Industrial average pair. These results highlight the complex and nonlinear dynamics governing carbon markets, offering critical insights for investors, policymakers, and regulators navigating the intersection of environmental and financial systems. Full article

This paper analyzes high-frequency intraday Bitcoin data from 2019 to 2022. The Bitcoin market index exhibits two distinct periods, characterized by abrupt volatility shifts. Bitcoin returns can be described by anomalous diffusion processes, transitioning from subdiffusion for short intervals to weak superdiffusion at longer intervals. Heavy tails are captured well by q-Gaussian distributions, and the autocorrelation of absolute returns shows power law behavior. Both periods display multifractality, with Hurst exponents shifting toward 0.5 over time, indicating increased market efficiency. The time evolution of the empirical PDF of price return allows us to connect these stylized facts to the mathematical framework of multifractals and locally fractional porous medium equations. Full article

In this study, we aim to provide a comprehensive analysis of the risk management potential of sectoral Credit Default Swaps (CDSs) within financial portfolios. Our objectives are threefold: (i) to investigate the safe haven properties of sectoral CDSs; (ii) to assess their hedging effectiveness and evaluate the diversification benefits of incorporating sectoral CDSs into both conventional and Islamic stock market portfolios; and (iii) to compare these findings with those obtained from alternative assets such as the VSTOXX, gold, and Bitcoin indices. To achieve this, we estimate time-varying hedge ratios using a range of multivariate GARCH (MGARCH) models and subsequently compute hedging effectiveness metrics. Conditional correlations derived from the Asymmetric Dynamic Conditional Correlation (ADCC) model are employed in linear regression analyses to assess safe haven characteristics. This methodology is applied across different subperiods to capture the impact of the crypto currency bubble and the COVID-19 pandemic on hedging performance. Full article

The Generalized Tempered Stable (GTS) distribution extends classical stable laws through exponential tempering, preserving the power-law behavior while ensuring finite moments. This makes it especially suitable for modeling heavy-tailed financial data. However, the lack of closed-form densities poses significant challenges for simulation. This study provides a comprehensive and systematic comparison of GTS simulation methods, including rejection-based algorithms, series representations, and an enhanced Fast Fractional Fourier Transform (FRFT)-based inversion method. Through extensive numerical experiments on major financial assets (Bitcoin, Ethereum, the S&P 500, and the SPY ETF), this study demonstrates that the FRFT method outperforms others in terms of accuracy and ability to capture tail behavior, as validated by goodness-of-fit tests. Our results provide practitioners with robust and efficient simulation tools for applications in risk management, derivative pricing, and statistical modeling. Full article

This study examines links between global financial stress and cryptocurrency returns from 1 January 2017 to 31 January 2025, while explicitly accounting for commodity markets. We use an econometric toolkit: unit-root and cointegration testing, ARDL bounds, Toda–Yamamoto causality, and a two-state Markov Switching model to trace long-run equilibrium and transmission mechanisms across cryptocurrencies (BGCI), systemic stress (OFR-FSI), volatility measures (VIX, VVIX, VSTOXX, VVSTOXX, MOVE), major equities and bonds, and three commodities (gold, oil, copper). Results show robust long-run cointegration between BGCI and several financial variables, including S&P/ASX 200 and the Bloomberg Barclays Bond Index; models that include commodities continue to support these long-term links. Toda–Yamamoto tests reveal that stress and volatility indices unidirectionally transmit shocks to cryptocurrencies and commodities, while gold displays a bidirectional relationship with BGCI, indicating a conditional safe haven interaction. Markov Switching estimates show amplified co-movement among BGCI, gold and bonds in stress regimes, with the model predominantly remaining in a normal state. Overall, cryptocurrencies are embedded within the broader financial system; commodities, especially gold, are used to moderate the stress crypto transmission and offer conditional diversification value during turmoil. Full article

The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions. Full article

This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture long memory, paired with both constant-volatility (CONST) and stochastic-volatility specifications via the Cox–Ingersoll–Ross (CIR) process. The novel family of models is based on Generalized Ornstein–Uhlenbeck processes with a fluctuating exponential trend (GOU-FE), which are modified to account for multiplicative fBm noise. Traditional Geometric Brownian Motion processes (GFBM) with either constant or stochastic volatilities are employed as benchmarks for comparative analysis, bringing the total number of evaluated models to four: GFBM-CONST, GFBM-CIR, GOUFE-CONST, and GOUFE-CIR models. Estimation by numerical optimization and evaluation through error metrics, information criteria (AIC, BIC, and EDC), and 95% Expected Shortfall (ES₉₅) indicated better fit for the stochastic-volatility models (GOUFE-CIR and GFBM-CIR) and the lowest tail-risk for GOUFE-CIR, although residual analysis revealed heteroscedasticity and non-normality. For intraday data, Exponential, Weibull, and Generalized Gamma Autoregressive Conditional Duration (ACD) models, with adjustments for intraday patterns, were applied to model the time between transactions. Results showed that the ACD models effectively capture duration clustering, with the Generalized Gamma version exhibiting superior fit according to the Cox–Snell residual-based analysis and other metrics (AIC, BIC, and mean-squared error). Overall, this work advances the modeling of Bitcoin prices by rigorously applying and comparing stochastic frameworks across temporal scales, highlighting the critical roles of long memory, stochastic volatility, and intraday dynamics in understanding the behavior of this digital asset. Full article

This study explores the new insights into the integration and dynamic asymmetric volatility risk spillovers between Bitcoin, currency pairs (USD/ZAR, GBP/ZAR and EUR/ZAR), and traditional financial assets (ALSI, Bond, and Gold) in South Africa using daily data spanning the period from 2010 to 2024 and employing Time-Varying Parameter Vector Autoregression (TVP-VAR) and wavelet coherence. The findings revealed strengthened integration between traditional financial assets and currency pairs, as well as weak integration with BTC/ZAR. Furthermore, BTC/ZAR and traditional financial assets were receivers of shocks, while the currency pairs were transmitters of spillovers. Gold emerged as an attractive investment during periods of inflation or currency devaluation. However, the assets have a total connectedness index of 28.37%, offering a reduced systemic risk. Distinct patterns were observed in the short, medium, and long term in time scales and frequency. There is a diversification benefit and potential hedging strategies due to gold’s negative influence on BTC/ZAR. Bitcoin’s high volatility and lack of regulatory oversight continue to be deterrents for institutional investors. This study lays a solid foundation for understanding the financial dynamics in South Africa, offering valuable insights for investors and policymakers interested in the intricate linkages between BTC/ZAR, currency pairs, and traditional financial assets, allowing for more targeted policy measures. Full article

Forecasting Bitcoin prices remains a complex task due to the asset’s inherent and significant volatility. Traditional reinforcement learning (RL) models often rely on a single observation from the time series, potentially missing out on short-term patterns that could enhance prediction performance. This study presents a Deep Q-Network (DQN) model that utilizes a multi-step state representation, incorporating consecutive historical timesteps to reflect recent market behavior more accurately. By doing so, the model can more effectively identify short-term trends under volatile conditions. Additionally, we propose a novel reward mechanism that adjusts for volatility by penalizing large prediction errors more heavily during periods of high market volatility, thereby encouraging more risk-aware forecasting behavior. We validate the effectiveness of our approach through extensive experiments on Bitcoin data across minutely, hourly, and daily timeframes. The proposed model achieves notable results, including a Mean Absolute Percentage Error (MAPE) of 10.12%, Root Mean Squared Error (RMSE) of 815.33, and Value-at-Risk (VaR) of 0.04. These outcomes demonstrate the advantages of integrating short-term temporal features and volatility sensitivity into RL frameworks for more reliable cryptocurrency price prediction. Full article

The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and external connectedness across asset groups using a time-varying parameter VAR model. The results show that post-FTX, Bitcoin and Ethereum intensified their roles as core shock transmitters, while Tether consistently acted as a volatility absorber. DeFi tokens exhibited heightened intra-group spillovers and occasional external influence, reflecting structural fragility. Tech stocks remained largely insulated, with reduced cross-market linkages. Network visualizations confirm a post-crisis fragmentation, characterized by denser internal crypto-DeFi ties and weaker inter-group contagion. These findings have important policy implications for regulators, investors, and system designers, indicating the need for targeted risk monitoring and governance within decentralized finance. Full article

In a stage of more and more complex and high-frequency financial markets, the volatility analysis is a cornerstone of modern financial econometrics with practical applications in portfolio optimization, derivative pricing, and systematic risk assessment. This paper introduces a novel Bayesian Time-varying Generalized Autoregressive Conditional Heteroskedasticity (BtvGARCH-Itô) model designed to improve the precision and flexibility of volatility modeling in financial markets. Original GARCH-Itô models, while effective in capturing realized volatility and intraday patterns, rely on fixed or constant parameters; thus, it is limited to studying structural changes. Our proposed model addresses this restraint by integrating the continuous-time Ito process with a time-varying Bayesian inference to allow parameters to vary over time based on prior beliefs to quantify uncertainty and minimize overfitting, especially in small-sample or high-dimensional settings. Through simulation studies, using sample sizes of N = 100 and N = 200, we find that BtvGARCH-Itô outperformed original GARCH-Itô in-sample fit and out-of-sample forecast accuracy based on posterior estimates comparison with true parameter values and forecasting error metrics. For the empirical validation, this model is applied to analyze the volatility of S&P 500 and Bitcoin (BTC) using one-minute length data for S&P 500 (from 3 January 2023 to 31 December 2024) and BTC (from 1 January 2023 to 1 January 2025). This model has potential as a robust tool and a new direction in volatility modeling for financial risk management. Full article

In this literature review, we critically examine the evolving landscape of privacy in blockchain systems, with a particular focus on the differentiation of privacy attacks and protective measures across three distinct layers: the on-chain layer; the off-chain layer; and on the infrastructure, i.e., peer-to-peer network layer. In this review, we categorize prevalent privacy attacks, such as transaction tracing, data leakage, and network surveillance, highlighting their implications at each layer. In addition, we evaluate a range of protective techniques, including cryptographic methods, zero-knowledge proofs, and other privacy-preserving protocols. We explore the compatibility of these privacy techniques with existing blockchain systems. By synthesizing current research and practical implementations, our aims are to provide a comprehensive understanding of privacy challenges and solutions in blockchain environments, identify gaps, and guide future developments in privacy-enhancing technologies within the blockchain ecosystem. Full article

The Bitcoin network is a sophisticated accounting system that facilitates consensus and verification of transactions through cryptographic proof, eliminating the need for a central authority. Given its success, the underlying technology, generally referred to as blockchain, has been proposed as a means to improve legacy accounting and reporting systems. However, integrating real-world data into a blockchain requires the use of oracles: third-party systems that, if poorly selected, may be less decentralized and transparent, potentially undermining the expected benefits. Through a systematic review of the existing literature, this study investigates whether research articles on the integration of blockchain technology in accounting and reporting have addressed the limitations posed by oracles, under the rationale that the omission of oracles constitutes a theoretical bias. Furthermore, this study examines oracle-based solutions proposed for reporting applications and classifies them based on their intended purpose. While the overall consideration of oracles remains limited, the findings indicate a steadily increasing interest in their role and implications within accounting, auditing, and ESG-related blockchain implementations. This growing attention is particularly evident in ESG reporting, where permissioned blockchains and attestation mechanisms are increasingly being examined as practical responses to data verification challenges. Full article