In March 2025, the United States established a Strategic Bitcoin Reserve by executive order. Bhutan had been quietly mining Bitcoin with hydroelectric power, accumulating over $1 billion without public announcement. By February 2026, more than 145 publicly traded companies held Bitcoin on their balance sheets, collectively exceeding one million BTC. No established academic framework predicted these developments. In his 2023 MIT thesis Softwar, Major Jason Lowery proposed that Bitcoin is best understood not as money or a hedge but as a power projection technology rooted in thermodynamic proof of work. This paper presents the first empirical evaluation of that framework. Nine falsifiable predictions are tested against observed data; five have been confirmed and one partially realized within three years of publication. The developments that monetary, financial, environmental, and security models fail to explain (strategic reserves, geopolitical competition for hash rate, sovereign mining operations) are precisely those the power projection framework predicts.
This paper investigates systemic risk transmission across stablecoin markets using Quantile Vector Autoregression (QVAR). Analyzing eight major stablecoins with day data coverage from 2021 to 2025, supplemented by minute-level event studies on three additional coins experiencing major depegs until 2025, we document three findings. First, stabilization mechanism dictates tail-risk behavior: fiat-backed stablecoins function as "stability anchors" with near-zero net spillovers across quantiles, while algorithmic and crypto-collateralized designs become risk amplifiers specifically under extreme market conditions. Second, the theoretical risk isolation between fiat and crypto markets breaks down during stress: direct volatility channels emerge between the US Dollar Index and Bitcoin that bypass stablecoin intermediation. Third, Forbes-Rigobon contagion tests across four depeg events show heterogeneous transmission: after adjusting for volatility, algorithmic stablecoins exhibit significant residual contagion while fiat-backed coins show flight-to-quality effects. These findings imply that uniform stablecoin regulation is inappropriate; regulatory capital buffers for extreme losses should be 2--3x higher for non-fiat-backed stablecoins than median-based measures indicate.
The sudden growth of cryptocurrencies has created a set of intricate regulatory and legal issues for the financial and governance system of India. The decentralized nature of digital currencies like Bitcoin and Ethereum challenges the conventional monetary system, giving rise to concerns about their legal status, protection of investors, taxation, and overall financial stability. This paper critically analyzes the regulatory environment in India, especially in the wake of the 2018 circular issued by the Reserve Bank of India and its subsequent strike-down in the case of Internet and Mobile Association of India v. Reserve Bank of India. It also discusses challenges with respect to money laundering under the Prevention of Money Laundering Act, 2002, taxation of virtual digital assets, and the lack of a comprehensive statutory regulatory framework for cryptocurrency exchanges. The paper contends that the current stance of India is one of regulatory ambivalence, vacillating between control and tolerance.
Abstract This paper investigates the time-varying dynamics of the Bitcoin price by examining its relationship with key global factors, including the VIX, the interest rate, the US dollar index, the oil price, and the gold price. The empirical analysis employs a state-space model, the Kalman filter method, and a TVP-VAR-SV. The findings from the state-space model indicate a significant negative association between the Bitcoin price and the VIX, while identifying a positive relationship with the gold price. Further analysis using instantaneous time-varying impulse response functions reveals that the negative response of Bitcoin to the VIX intensified significantly during the pandemic period. A similar negative impact was observed regarding the US dollar index and the oil price. In contrast, the interest rate exhibited a positive connection with the Bitcoin price. Notably, the relationship between Bitcoin and gold, which was negative prior to the pandemic, became statistically insignificant as the crisis escalated. This underscores that Bitcoin’s hedging capabilities and safe haven characteristics are not intrinsic fundamental qualities, but rather conditional behaviors that evolve with shifting global economic landscapes. The evidence suggests that Bitcoin’s defensive properties are structural rather than fundamental, emerging primarily during specific volatility regimes. Additionally, the inverse relationship between the oil price and Bitcoin suggests that rising energy costs may dampen the cryptocurrency’s appeal due to its substantial energy consumption. These results offer significant implications for scholars, investors, and portfolio managers regarding the management of digital assets during periods of systemic instability.
Time-series forecasting is a critical task across many domains, from engineering to economics, where accurate predictions drive strategic decisions. However, applying advanced deep learning models in challenging, volatile domains like finance is difficult due to the inherent limitation and dynamic nature of financial time series data. This scarcity often results in sub-optimal model training and poor generalization. The fundamental challenge lies in determining how to reliably augment scarce financial time series data to enhance the predictive accuracy of deep learning forecasting models. Our main contribution is a demonstration of how Generative Adversarial Networks (GANs) can effectively serve as a data augmentation tool to overcome data scarcity in the financial domain. Specifically, we show that training a Long Short-Term Memory (LSTM) forecasting model on a dataset augmented with synthetic data generated by a transformer-based GAN (TTS-GAN) significantly improves the forecasting accuracy compared to using real data alone. We confirm these results across different financial time series (Bitcoin and S\&P500 price data) and various forecasting horizons. Furthermore, we propose a novel, time series specific quality metric that combines Dynamic Time Warping (DTW) and a modified Deep Dataset Dissimilarity Measure (DeD-iMs) to reliably monitor the training progress and evaluate the quality of the generated data. These findings provide compelling evidence for the benefits of GAN-based data augmentation in enhancing financial predictive capabilities.
In this paper, we analyse the impacts of exogenous and endogenous factors on wealth distribution in the Bitcoin token economy, where wealth distribution refers to the distribution of BTC between economic participants or groups of economic participants. The objective of the paper is to analyse the impact of economic policies on wealth distribution in the Bitcoin ecosystem. Different macroeconomic and microeconomic time series are used to eliminate noise in the wealth distribution time series, and the causality analysis is performed between Bitcoin Improvement Proposals (i.e., BIPs) and the cleaned wealth distribution data to reveal possible patterns in the impacts that the endogenous policies have on wealth distribution in token economies. Lastly, a structure for economic policy taxonomy in token economies is proposed where different the policy implementations are illustrated by existing BIPs. This approach highlights the actions available to the policy makers, as well as providing a technique for analysis of policy impacts in token economies and their categorization.
The growing popularity, the exponentially expanding market size, and the volatility of Cryptocurrency are gaining the attention of all, whether it is investors, policymakers, miners, or academicians. So, this paper has used Bibliometric analysis to explore the existing works of literature in the area of Business, Finance, and Economics. We have reviewed and analysed 1344 articles extracted from the Web of Science core collection, Clarivate Analytics of the period from 2011 to mid-2022 using VOSviewer and Biblioshiny (Biblimetrix: R package) analytical tools. This paper has presented citations, publications, and the impact of sources, documents, authors, organizations, countries, etc., along with their relationships with the help of tables, charts, and network diagrams. The analysis shows exponential growth in the last 4-5 years. Bitcoin and Cryptocurrency (or Cryptocurrencies) are the most frequent keywords. With many ups and downs, cryptocurrency is maintaining its pace with a gradual increase in its acceptability worldwide.
The Bitcoin protocol [Nakamoto, 2008] represents a landmark achievement in distributed systems and cryptographic engineering. However, its fixed-supply design embeds a critical long-term vulnerability: the mathematical inevitability of permanent supply contraction driven by generational private-key inheritance failure. This paper formalises the generational loss model through discrete probability theory and recurrence relations, demonstrating that conservative estimates predict 51% of total supply becoming permanently inaccessible within 264 years, while realistic models project 64% loss. We further establish that the cessation of block rewards at approximately block height 6,930,000 ($\approx$2140 CE) eliminates the mining security budget, exposing the network to sustained 51% attack risk. We propose Bitcoin Infinity — the Perpetual Continuity Protocol — a minimal, mathematically grounded modification to Bitcoin Core's GetBlockSubsidy() function. The modification replaces a single hard-stop conditional with a modulo operation, restarting the original 50 BTC/block halving curve every 33 halvings ($\approx$132 years) in perpetuity. We prove that under this scheme, circulating supply converges to a stable equilibrium $C^* = S_0r/(1 - r)$ (approximately 49 M BTC at 30% generational loss), mining incentives are preserved indefinitely, and all previously issued bitcoins remain fully valid. The implementation is verified against 113 boundary tests with zero failures, exhibits no undefined behaviour under C++17, and maintains complete backward compatibility with the existing network until the activation block. Link: https://revistazen10.github.io/bitcoin-infinity/
Bitcoin's design promises resilience through decentralization, yet the physical infrastructure supporting the network creates hidden dependencies. We present the first longitudinal study of Bitcoin's resilience to submarine cable failures, using 11 years of P2P network data (2014--2025) and 68 verified cable fault events. Applying a Buldyrev-style cascade model at country level, we find that Bitcoin's clearnet (non-TOR) critical failure threshold $p_c \approx 0.72$--$0.92$ for random failures, meaning the vast majority of inter-country cables must fail before significant node disconnection. Targeted attacks are an order of magnitude more effective ($p_c = 0.05$--$0.20$). To address the majority of nodes now using TOR with unobservable locations, we develop a 4-layer multiplex model incorporating TOR relay infrastructure. Because relay bandwidth concentrates in well-connected European countries, TOR adoption increases resilience under current relay geography ($Δp_c \approx +0.02$--$+0.10$) rather than introducing hidden fragility. Empirical validation confirms weak physical-layer coupling: 87% of historical cable faults caused less than 5% node impact. We contribute: (1) a multiplex percolation framework for overlay-underlay coupling, including a 4-layer TOR relay model; (2) the first empirical measurement of Bitcoin's physical-layer resilience over a decade; and (3) evidence that TOR adoption amplifies resilience, with distributional bounds quantifying uncertainty under partial observability.
The dissertation examines statistical arbitrage methods in the cryptocurrency markets using cointegration analysis on Bitcoin, ethereum, Litecoin, Ripple using daily price data of the cryptocurrencies between January 2022 and October 2024. The research deploys strict econometric procedures, such as the Engle-Granger two-step process and Johansen test, to uncover and take advantage of the mean-reverting relationships between the key cryptocurrencies. Findings indicate that there are strong relationships of cointegration especially between Bitcoin-Ether and Ethereum-Litecoin with the relationship between Bitcoin-Ether and Ethereum being very stable in many market regimes. The statistically arbitrage strategies depending on such cointegrated pairs led to large risk-adjusted returns whose Sharpe ratios of 1.58 to 2.45 were markedly higher than buy-and-hold standards. The Bitcoin-Etherer pairs trading strategy had an annualized return of 16.34 evidenced by a volatility of just 8.45 against the volatility of Bitcoin on buy and hold at 54.67. These strategies had low beta (0.09-0.18), which was an affirmative of their market-neutral qualities and their positive alpha generation of between 11-15% per annum.
The rapid growth of cryptocurrency markets has created new challenges in understanding and predicting the structural dynamics of digital asset prices. Bitcoin, as the most traded blockchain-based currency, exhibits extreme volatility, nonlinear patterns, and complex regime shifts that traditional financial models cannot adequately capture. This study proposes a hybrid analytical framework that integrates K Means clustering with the Hidden Markov Model to identify and model multiple market regimes in Bitcoin time series data. The Bitcoin dataset used in this research contains minute-level records that were preprocessed to extract key indicators, namely logarithmic returns and rolling volatility, which represent the short-term dynamics of market behavior. The K Means algorithm was first employed to segment the data into three distinct clusters that correspond to bullish, bearish, and sideways regimes, followed by the application of the Hidden Markov Model to estimate probabilistic transitions between these regimes over time. The results reveal that the hybrid K Means and Hidden Markov Model approach achieves superior performance compared to a standalone model, as indicated by a higher log likelihood and a lower Bayesian Information Criterion value. The transition probability matrix shows that bullish and bearish regimes are highly persistent, while the sideways regime acts as a transitional buffer that connects both market extremes. The empirical findings confirm that Bitcoin prices evolve through persistent and probabilistically determined regimes rather than random fluctuations. The proposed framework provides a more comprehensive understanding of cryptocurrency market dynamics and offers practical value for investors, risk analysts, and policymakers in designing adaptive trading and risk management strategies within blockchain-based financial ecosystems.
Ahmad Khalifah Zamrud, Usman Jafar, Abdul Wahid Haddade
IntroductionThe rapid expansion of cryptocurrency has generated significant debate within Islamic economic discourse. Bitcoin, as the first decentralized digital currency, offers technological advantages such as transparency, efficiency, and global accessibility. However, it also raises concerns regarding price volatility, speculative trading behavior, and the absence of intrinsic value. These issues have prompted Islamic scholars and regulatory institutions to evaluate cryptocurrency from the perspective of Islamic law and financial ethics. In Indonesia, the Indonesian Ulema Council issued a religious ruling declaring Bitcoin impermissible due to elements of uncertainty, speculation, and potential economic harm. This ruling has stimulated ongoing discussion about the compatibility of cryptocurrency innovation with Islamic economic principles.ObjectivesThis study aims to critically analyze the religious ruling on Bitcoin issued by the Indonesian Ulema Council by examining its legal reasoning, its relationship with Islamic economic principles, and its implications for the governance of digital financial innovation. The research also seeks to explore whether cryptocurrency can be accommodated within an Islamic economic framework under certain regulatory and ethical conditions.MethodThe study employs a qualitative research design using a transdisciplinary analytical approach that integrates perspectives from Islamic jurisprudence, Islamic economics, financial regulation, and digital financial technology. Data were collected through documentation of religious rulings, regulatory policies, and scholarly literature related to cryptocurrency and Islamic finance. The data were analyzed through thematic and comparative analysis to identify the legal reasoning underlying the prohibition of Bitcoin and to evaluate alternative scholarly interpretations regarding the status of digital assets in Islamic economics.ResultsThe findings indicate that the prohibition of Bitcoin is primarily based on concerns about excessive uncertainty, speculative trading behavior, and potential economic harm associated with cryptocurrency markets. Nevertheless, the analysis also reveals that cryptocurrency may be considered permissible when these elements are mitigated through transparent governance, regulatory oversight, and the development of asset-backed digital financial instruments.ImplicationsThe study highlights the importance of developing regulatory and institutional frameworks that reconcile financial innovation with Islamic ethical principles. Such frameworks can provide clearer guidance for Muslim investors while supporting responsible digital financial development.Originality or NoveltyThis research contributes to the growing literature on cryptocurrency in Islamic economics by offering a critical analysis of religious rulings within the broader context of digital financial transformation and regulatory governance.
Amro Saleem Alamaren, Korhan K. Gökmenoğlu, Nigar Taşpınar
Abstract This study investigates the volatility spillover and connectedness networks among renewable energy sources (Biofuel, Fuel cell, Geothermal, Solar), green bonds, and cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin) in the U.S. market. To accomplish this objective, we analyzed data from November 15, 2017, to May 31, 2024, via the methods introduced by Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) and Baruník and Křehlík (J Financ Econometr 16:271 296, 2018). Our findings reveal that major global disruptions—including the COVID-19 pandemic, the Russia–Ukraine war, the collapse of Silicon Valley Bank, and the Credit Suisse crisis—have intensified volatility spillovers and financial contagion across markets, exacerbating their outcomes. The findings suggest that the effectiveness of green finance depends on its allocation across these sectors, highlighting the importance of examining each sector to understand the success of these financial initiatives. The influence of COVID-19 on the U.S. economy has increased transmission risk across markets. Renewable energy is less volatile than green bonds and cryptocurrencies are, with these indices reacting more quickly to short-term shocks. Investors should focus on short-term impacts to manage market risk effectively. By providing insights into how financial shocks propagate across sectors, emphasizing the need for a sector-specific approach to assessing financial sustainability, and underscoring the importance of short-term risk management strategies, this research offers valuable contributions to decision-makers and investors.
We test price efficiency, which shows the fairness of trading for retail investors using the runs tests and variance ratio tests. We reject the hypothesis that Bitcoin prices are price efficient on most markets, but efficient on the Bitstamp BTC/USD. Coinbase departs from efficiency, indicating that fraud, later found by regulators, has significantly harmed retail investors. We also document barriers to trading of Bitcoin, which result in difficulties in arbitrage despite global price differences. My results predict the hack of the Bitfinex exchange, which caused it to close and harmed many people.
This study explores the key determinants influencing cryptocurrency in Indonesia, focusing on macroeconomic variables including inflation, money supply, gold prices, and crude oil prices over the period from 2013 to 2023. It investigates the dynamic relationships between these variables and Bitcoin, the most widely recognized cryptocurrency globally. The research offers a novel contribution by integrating both domestic economic indicators and external commodity prices into a comprehensive framework for cryptocurrency pricing tailored specifically to the Indonesian market context. This innovative and comprehensive approach significantly enhances the understanding of how macroeconomic factors interact with cryptocurrency behavior, which is crucial for various stakeholders and policymakers alike. The findings aim to provide valuable insights to support the formulation of effective monetary policies in an evolving, increasingly complex financial landscape. Future studies are encouraged to build upon this framework by examining the connections between cryptocurrency and other components of the broader financial system.
Accurate forecasting of Bitcoin (BTC) has always been a challenge because decentralized markets are non-linear, highly volatile, and have temporal irregularities. Existing deep learning models often struggle with interpretability and generalization across diverse market conditions. This research presents a hybrid stacked-generalization framework, TFT-ACB-XML, for BTC closing price prediction. The framework integrates two parallel base learners: a customized Temporal Fusion Transformer (TFT) and an Attention-Customized Bidirectional Long Short-Term Memory network (ACB), followed by an XGBoost regressor as the meta-learner. The customized TFT model handles long-range dependencies and global temporal dynamics via variable selection networks and interpretable single-head attention. The ACB module uses a new attention mechanism alongside the customized BiLSTM to capture short-term sequential dependencies. Predictions from both customized TFT and ACB are weighted through an error-reciprocal weighting strategy. These weights are derived from validation performance, where a model showing lower prediction error receives a higher weight. Finally, the framework concatenates these weighted outputs into a feature vector and feeds the vector to an XGBoost regressor, which captures non-linear residuals and produces the final BTC closing price prediction. Empirical validation using BTC data from October 1, 2014, to January 5, 2026, shows improved performance of the proposed framework compared to recent Deep Learning and Transformer baseline models. The results show a MAPE of 0.65%, an MAE of 198.15, and an RMSE of 258.30 for one-step-ahead out-of-sample under a walk-forward evaluation on the test block. The evaluation period spans the 2024 BTC halving and the spot ETFs (exchange-traded funds) period, which coincide with major liquidity and volatility shifts.
Cryptocurrencies have started gaining ground as investment vehicles. Cryptocurrencies exhibit characteristics that differentiate them from traditional financial assets. In 2009, Bitcoin (BTC), the first digital currency, was launched. In 2021, the Securities and Exchange Commission (SEC) approved ProShares Bitcoin Strategy (BITO), the first U.S. Bitcoin futures exchange-traded fund (ETF). In 2024, SEC gave final approval for spot Ether (ETH) ETFs to start trading, further legitimizing the asset class. Although cryptocurrencies share many features of alternative assets, they are hindered by high volatility and regulatory uncertainties. Extant literature studies cryptocurrencies as alternative investments from various perspectives. Using market data, this empirical paper aims to contribute to the literature by studying the extent to which cryptocurrencies improve the risk-return profile of a diversified portfolio. Specifically, we do so by examining the economic impact of including Bitcoin for a passive investor investing in the U.S. Stock market index (S&P 500 index).
This paper proposes an AI-based trading framework that integrates supervised price forecasting with reinforcement learning (RL)-based decision-making. The objective is to enhance both profitability and risk management in cryptocurrency trading by equipping RL agents with forward-looking market information and risk-aware incentives. The proposed methodology follows a two-stage design. First, a univariate long short-term memory (LSTM) model generates 72 bitcoin price forecasts. These predictions are used to compute future technical indicators, which are combined with current market indicators to construct an enriched, forward-looking state representation. Second, an RL agent is trained in this environment using a novel long-term reward function that incorporates transaction costs, drawdown penalties, volatility penalties, and delayed rewards to promote stable and sustainable trading behavior. Four state-of-the-art RL algorithms (PPO, SAC, TD3, and A2C) are systematically evaluated over randomized 180-day episodes using hourly bitcoin data. The results demonstrate that the proposed agent consistently outperforms conventional buy-and-hold and moving average crossover strategies, achieving an average profit ratio of 32% and a Sharpe ratio of 1.34. These findings highlight the novelty and effectiveness of combining mid-term price forecasts, enriched technical states, and risk-aware RL training for robust cryptocurrency trading.
Manaf Ahmed, Mohammed Adnan, Ali Matar, Faez Hlail Srayyih · 7 authors
Predicting cryptocurrency price is challenging owing to high volatility, less historical data, and the impact of external parameters like news, public sentiment, and regulatory announcements. This challenge is tackled in this research by employing models of deep learning like Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU)—to predict Bitcoin's OHLC prices daily. Based on historical time-series data of Coin Codex, the research uses an autoencoder-based feature extraction method with five-day sliding window method for sequence generation. Hyperband optimization is used to tune hyperparameter of each model. The result shows that BiLSTM performs better than all the other models with minimum Mean Squared Error (MSE = 0.001183), Mean Absolute Error (MAE = 0.026090), and maximum R² score (0.980596) after optimization. The results emphasize the significance of deep learning in capturing nonlinear dynamics in time series of financial applications and bear testimony to the effectiveness of hyperparameter tuning in enhancing model accuracy. The study enhances the development of prediction tools for digital asset markets and enables more informed investment decisions.
Nourhaine Nefzi, A. Melki, Sahar Loukil, Ahmed Jeribi
Abstract This study investigates the dynamic connectedness within the cryptocurrency market by analyzing four distinct cryptomarket blocks: Bitcoin and Ethereum (conventional cryptocurrencies); PAXG, DGX, and GLC (gold-backed cryptocurrencies); LINK and MNK (decentralized finance); and THETA and MANA (nonfungible tokens). Using the time-varying parameter quantile vector autoregressive (TVP-Quantile VAR) model for the period 2019–2023, our analysis reveals significant insights into the risk transmission dynamics among cryptocurrencies. Both conventional cryptocurrencies exhibit a consistent net transmitter effect in extreme periods, whereas decentralized finance (DeFi) and nonfungible tokens (NFTs) shift between a net shock transmitter and a net shock receiver over time and quantiles. Moreover, our results shed light on the hedging and safe haven properties of these assets. By linking the dynamic connectedness findings with established literature on hedging and safe haven functions, we elucidate how these cryptocurrencies perform under varying market conditions. Specifically, we report that the role of LINK, MNK, THETA, and MANA as reliable safe-haven assets is contingent upon the observed period. We also observe the hedge and safe haven properties of selected gold-backed cryptocurrencies within the network. Overall, our findings suggest that, despite the dynamic connectedness of the cryptocurrency market, investors have the flexibility to diversify across these digital assets.
We present BAZINGA, a novel distributed system that achieves unification of artificial intelligence and blockchain through a new consensus mechanism called Proof-of-Boundary (PoB). Unlike traditional approaches that treat AI and blockchain as separate layers ("AI on blockchain"), BAZINGA demonstrates that AI and blockchain are Subject and Object of a single system, with consensus emerging from the boundary between them. The key discovery is that blockchain consensus can be achieved through understanding rather than computational work or financial stake. Nodes validate blocks by demonstrating comprehension via a mathematical boundary condition: the ratio of Physical to Geometric measures must equal φ⁴ ≈ 6.854 (where φ is the golden ratio). Key results: • 70 billion times more energy-efficient than Bitcoin • Sybil-resistant without financial stake • Unified with federated learning for distributed AI training • Validated through mathematical understanding rather than arbitrary computation The system includes four integration layers (Trust Oracle, Knowledge Ledger, Gradient Validator, Inference Market) that bind AI intelligence with blockchain validation. Fully implemented as open-source software (MIT License). Software: https://pypi.org/project/bazinga-indeed/ Source: https://github.com/0x-auth/bazinga-indeed Demo: https://huggingface.co/spaces/bitsabhi/bazinga
Lihki Rubio, Keyla Alba, Carlos E Velásquez, Filipe R. Ramos
Accurately forecasting Bitcoin’s conditional variance is essential for reliable Value-at-Risk (VaR) estimation yet remains challenging due to nonlinear dynamics, volatility clustering, and heavy-tailed return distributions. This study developed a novel stacking ensemble that integrates econometric and machine-learning models through XGBoost meta-learning to produce improved variance forecasts. Hybrid ML–GARCH specifications are incorporated separately to enrich the comparative analysis. All estimators are trained with time-aware cross-validation to ensure temporal coherence and prevent look-ahead bias. Using Bitcoin data from 2014 to 2020, the empirical results show that the stacking ensemble consistently outperforms both standalone and hybrid alternatives in conditional variance forecasting and VaR accuracy, including during periods of severe market stress such as the COVID-19 episode. Residual diagnostics confirm that the ensemble effectively captures persistent temporal dependencies in volatility dynamics. Overall, the proposed methodology offers an innovative and interpretable risk-management tool for financial institutions, combining statistical rigor with the adaptability of machine-learning techniques in digital asset markets.
Unlike Ethereum, which was conceived as a general-purpose smart-contract platform, Bitcoin was designed primarily as a transaction ledger for its native currency, which limits programmability for conditional applications. This constraint is particularly evident when considering oracles, mechanisms that enable Bitcoin contracts to depend on exogenous events. This paper investigates whether new oracle designs have emerged for Bitcoin Layer 1 since the 2015 transition to the Ethereum smart contracts era and whether subsequent Bitcoin improvement proposals have expanded oracles' implementability. Using Scopus and Web of Science searches, complemented by Google Scholar to capture protocol proposals, we observe that the indexed academic coverage remains limited, and many contributions circulate outside journal venues. Within the retrieved corpus, the main post-2015 shift is from multisig-style, which envisioned oracles as co-signers, toward attestation-based designs, mainly represented by Discreet Log Contracts (DLCs), which show stronger Bitcoin community compliance, tool support, and evidence of practical implementations in real-world scenarios such as betting and prediction-market mechanisms.