Ala Alrawajfi, Mohd Tahir Ismail, Sadam Al Wadi, Saleh Atiewi
Ethereum and other cryptocurrencies are volatile, making Ethereum-USD rate evaluation difficult.Due to unsuccessful data collection and exchange downtimes, financial time series data are incomplete and lacking critical values.Thus, assessments may be incomplete, and trends may be miscalculated.This research builds and tests an ARIMA-random forest data imputation method to overcome these concerns.This innovative strategy uses AutoRegressive Integrated Moving Average (ARIMA) to describe the linear chronologic sequence relationship and random forest to solve nonlinearity.The suggested method uses ARIMA to handle the linear time-dependent data feature and random forest to reduce estimation errors to improve Ethereum-USD closing price estimates.The mean absolute error (MAE) and mean absolute percentage error (MAPE) results demonstrate that the proposed hybrid model significantly outperforms conventional imputation approaches across all missing data levels (10%-50%).For example, at 30% missing data, the hybrid model achieved an MAE of 0.91 and a MAPE of 0.00074, compared to ARIMA's MAE of 2.21 (MAPE 0.00185) and Random Forest's MAE of 2.34 (MAPE 0.00186).Across all scenarios, the hybrid model reduced MAE by up to 60% and MAPE by over 55% relative to the best single-method baseline, indicating superior robustness and accuracy in handling incomplete Ethereum-USD datasets.By providing precise market and result knowledge, these insights help financial analysts, traders, and researchers make accurate, efficient decisions.
This research investigates the dynamics of connectedness among cryptocurrency and various risk factors, including oil price demand and supply shocks, EPU, GPR, and the ADS business conditions index using the quantile time-frequency connectedness approach. The findings reveal that cryptocurrency behaves as a net receiver of shocks in the short term but transitions to a net transmitter over the long term. Critical sources of both short- and long-term shocks are attributed to oil price demand, supply fluctuations, and GPR. However, during extreme events like the COVIDâ19 pandemic and the Russia-Ukraine war, cryptocurrency, oil shocks, and other indices alternately become net transmitters and receivers of shocks depending on time frames and quantile ranges. During periods of heightened market uncertainty, monitoring the interconnected behavior of these variables is critical for investors and policymakers aiming to predict market shifts and manage risks effectively.
This study examines the intricate relationships between cryptocurrency and various uncertainties related to economic policy and global risk factors. It explores the interactions between cryptocurrency and global risk factors, comparing these with their relationships to different measures of economic policy uncertainty (EPU). We find that cryptocurrency returns are more sensitive to global risk factors than to the country-level EPU. Notably, gold exhibits bidirectional causality with cryptocurrency in returns and volatility. The research sheds light on the dynamic interactions within cryptocurrency markets, underscoring the importance of continuous monitoring and adaptive strategies to navigate the evolving financial landscape of the digital ecosystem.
This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables.
Advanced blockchain technologies and growing environmental and economic uncertainties have Motivated us to investigate the impact of climate policy uncertainty (CPU) and global economic policy uncertainty (GEPU) on five green cryptocurrenciesâADA, EOS, IOTA, XLM, XTZâselected based on energy efficiency and mining processes. We examined the short- and long-run impacts of alternative assets on these cryptocurrencies using a nonlinear autoregressive distributed lag model. In the long run, these cryptocurrencies are negatively affected by CPU and GEPU, questioning their safe-haven potential. In the short run, ADA, EOS, and XLM share a positive asymmetric relationship with CPU, whereas all cryptocurrencies have a negative asymmetric relationship with GEPU. Therefore, they can be considered a safe haven. In the short and long term, green bonds exert a positive impact, whereas interest rates, the S&P 500, and the gold index negatively impact these cryptocurrencies. In the short run, Bitcoin shows a negative relationship with EOS, IOTA, and XTZ and a positive relationship with ADA and XLM. Over the long term, Bitcoin exhibits a positive correlation with all cryptocurrencies. USD exhibits a positive relationship in the short run and a negative relationship in the long run with all cryptocurrencies. The findings offer practical implications for portfolio construction and investors dealing in the green cryptocurrency market.
Ăder Johnson de Area LeĂŁo Pereira, Thanmillys Nadhynne de Lima da Conceição, Emanuel Cruz Lima
The urgent need to mitigate climate change has elevated green hydrogen as a sustainable alternative to fossil fuels, while green cryptocurrencies have emerged to address the environmental concerns of traditional cryptocurrency mining. This study investigates the dynamic correlation between the green hydrogen market and selected green cryptocurrencies (Cardano, Stellar, Hedera, Algorand, and Chia) from July 2021 to April 2024, utilizing the Dynamic Conditional Correlation GARCH (DCC-GARCH) model with robustness checks using EGARCH and GJR-GARCH specifications. Our findings reveal significant correlations, with peaks reaching up to 50% in 2022, a period likely influenced by the Russia-Ukraine conflict. Subsequently, a decline in these correlations was observed in 2023. These results underscore the interconnectedness of sustainability-driven markets, suggesting potential contagion effects during periods of global instability. The high persistence of correlation shocks (ι + β values approaching unity) indicates that correlation regimes tend to be long- lasting, with important implications for portfolio diversification and risk management strategies. Robustness checks using EGARCH and GJR-GARCH specifications confirmed qualitatively similar patterns, reinforcing the validity of our findings into the evolving landscape of green finance and energy.
This paper investigates the relationship between cryptocurrencies and other financial assets, with a particular focus on the dynamics of information flow between developed and emerging markets. To achieve this objective, the study applies a combined methodology of spillover index analysis and network topology based on graph theory. The analysis covers key cryptocurrencies (Bitcoin and Ethereum), stocks, and conventional currencies over the period November 2017 to September 2022, and distinguishes between short-term and long-run dynamics. The empirical findings show that in the short run, Bitcoin and Ethereum predominantly act as net shock transmitters, whereas in the long run, stocks and conventional currencies, together with Bitcoin and Ethereum, become the principal conveyors of spillover shocks. The network topology analysis corroborates these results by revealing the centrality of these assets in the spillover structure. By integrating spillover and network approaches across different markets and time horizons, this study contributes to the literature by providing a more nuanced understanding of how cryptocurrencies interact with traditional financial assets under varying market conditions.
This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalizationâBitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAIâagainst a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.
Type of the article: Research ArticleAbstractThe rise of decentralized finance (DeFi) presents new opportunities for accessing modern financial services. Despite their transformative architecture, most DeFi applications are currently unregulated, which exposes market participants to unforeseen risks. Therefore, understanding the level of connectedness between DeFi and traditional finance (TradFi) is crucial, particularly in emerging Asian markets where the level of cryptocurrency acceptance is high. Applying the time-varying parameter vector autoregressive model, this study examines the return connectedness between leading DeFi assets and traditional financial sectors in Indonesia, India, and Vietnam â the top three countries in Asia for cryptocurrency adoption. By analyzing TradFi at the industry level, this study captures sector-specific spillover dynamics that are essential to the monitoring of systemwide risk. The empirical results reveal low, time-varying return spillovers between DeFi and traditional financial sectors in the selected emerging Asian markets. The emerging financial sectors exhibit stronger linkages with broader traditional market indicators than with DeFi, in which assets interact primarily with each other. Emerging financial sectors and gold are the recipients of return spillovers, and DeFi assets act as the return transmitters. The current low degree of integration between DeFi and TradFi offers policymakers a window of opportunity to develop a robust financial regulatory framework that addresses issues of market stability and consumer protection while promoting the advancement of financial innovation.AcknowledgmentsWe thank the editors and anonymous reviewers for their valuable and constructive feedback, which has contributed significantly to improving the quality of this manuscript.
Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios.
This paper analyzes the time-varying herding behavior in the non-fungible token (NFTs) and cryptocurrency markets and investigates their interrelationship. Using the daily market data from January 1st, 2020 to April 30th, 2023, our study covers the period characterized by Covid and post-Covid-19 induced global financial market volatility, capturing the dynamics in the global macroeconomic system and the Federal Reserveâs interest rate policy. Based on the rolling window method, our findings show the presence of herding behavior in both markets, where herding behavior in these markets may be influenced by the major events announcements particularly those related to the Federal Reserve's interest rate policy. Vector error correction model (VECM) indicates that the NFT market impacts the price of Ethereum, thereby influencing the broader cryptocurrency market. Such finding contributes to a deeper understanding of the market dynamics. By examining herding behavior, our findings indicate that the NFT market demonstrates relative independence from the volatile prices of the cryptocurrency market, suggesting the potential diversification benefits of incorporating NFTs for investorsâ portfolio construction and risk management.
⢠Wavelet coherence reveals Bitcoinâs persistent link with climate policy uncertainty ⢠Green cryptos show context-dependent coherence with climate policy uncertainty ⢠Partial decoupling of green cryptos from Bitcoin emerges at medium-term scales ⢠Twofold framework uncovers time-scale responses of crypto to policy uncertainty ⢠Emphasizes need for stable climate regulations to curb crypto market volatility As global climate policy uncertainty (CPU) intensifies, understanding its intersection with emerging financial technologies becomes increasingly urgent. This study, therefore, investigates the dynamic relationship between CPU and the cryptocurrency market, focusing on Bitcoin and eight leading green cryptocurrencies (Algorand, Cardano, EOS, Hedera, IOTA, Nano, Stellar, and Tezos) using a wavelet coherence analysis. Specifically, the study employs a twofold framework: first, assessing the responsiveness of Bitcoin and green cryptocurrencies to climate policy uncertainty across time scales; second, examining Bitcoin's interaction with green cryptocurrencies to determine their potential stabilizing or decoupling effects amid regulatory uncertainty. The analysis spans from November 2017 to March 2025, capturing multiple phases of regulatory evolution and market transformation. The findings reveal that Bitcoin exhibits a structurally embedded and persistent coherence with CPU, especially over longer investment horizons. This persistent linkage highlights Bitcoinâs role in exacerbating regulatory volatility due to its significant environmental footprint. Conversely, green cryptocurrencies demonstrate more sporadic and context-dependent coherence, often aligning with major climate policy announcements or periods of regulatory scrutiny. While positioned as sustainable alternatives, these assets remain influenced by Bitcoinâs dominance and broader market sentiment, particularly at medium-term investment scales. The partial synchronization observed across key periods suggests an incomplete decoupling from both CPU and Bitcoin. These results highlight the importance of clear and stable climate regulations to reduce market uncertainty and support innovation in sustainable blockchain technologies.
This study aims to understand the relationship among cryptocurrency, stock, and gold markets. Cointegration, structured VAR, and causality tests were used with daily datasets from 11/09/2017 to 11/17/2023. A cryptocurrency basket is accepted as the cryptocurrency market for this study. The stock markets have a one-way relationship both with the gold and cryptocurrency markets in the short-run. All markets have effects on other marketsâ price variances, as well. The price shocks of the markets to each other are not so essential for the prices. However, their own price shocks impact their prices for a few days. The stock market has asymmetric relationships with the gold and cryptocurrency markets. A 1.00 % rise in stock price causes declines in the gold and cryptocurrency prices by 2.35% and 2.42%, respectively. If the gold market or stock market is ignored, a 1.00% rise in gold prices causes a 0.69% rise in cryptocurrency prices, or a 1.00% rise in stock prices raises the cryptocurrency prices by 4.03%.
Cryptocurrency investment is a rapidly growing financial sector, marked by high volatility, decentralized technologies, and significant profit potential. Investors use strategies like long-term holding (âHODLingâ), portfolio diversification, and short-term trading. âHODLingâ relies on long-term value appreciation but requires resilience to price fluctuations. Diversifying with assets like Bitcoin and Ethereum reduces risk due to their low correlation with traditional investments. The crypto market is highly sensitive to geopolitical, economic, and technological factors, attracting investors during economic instability. Advanced models like LASSO and AutoEncoder aid in price prediction and strategy optimization. Despite high return potential, careful risk management is essential due to volatility and regulatory uncertainty. This study experimentally applies identical cryptocurrency portfolios to different investment strategies, identifying the most profitable approach.
Ifran Khan, Huangbao Gui, Chin Man Chui, Mrs Faryal ¡ 6 authors
This study investigates the dynamic volatility transmission between leading cryptocurrencies (Bitcoin, Ethereum, and Binance Coin) and major Chinese firms in the technology (Tencent and Alibaba), green energy (CATL, BYD, and LONGi), and traditional energy (PetroChina) sectors, including the CSI 300 index. Employing the frameworks of Diebold and Yilmaz (2012) and BarunĂk and KĹehlĂk (2018) on daily data from July 2018 to May 2025, we demonstrate significant cross-market risk transmission. The total connectedness index averages 34.77%, soaring to over 50% during the COVID-19 crisis, underscoring heightened systemic vulnerability. Our key finding identifies the CSI 300 index and cryptocurrencies (BTC, ETH) as the primary net transmitters of volatility shocks, whereas Chinese tech and energy firms (Tencent, CATL, and PetroChina) act as the main net receivers. A critical insight from the frequency decomposition is the absolute dominance of short-term spillovers (1â4 days), which constitute 34.85% of total connectedness, vastly outweighing the minimal effects in the medium- (4â10 days: 0.78%) and long-term (beyond 10 days: 0.52%). Investor sentiment, speculation, and news shocks drive short-term volatility spillovers from cryptocurrencies to stocks, particularly evident in their strong correlation with Chinese tech and energy equities. We attribute these spillovers to shared investor bases, sectoral links like crypto mining's energy demand, and regulatory interdependencies. Our evidence confirms that cryptocurrency markets are now integral to global financial stress, transmitting significant volatility to real-economy sectors. This study offers critical insights for investors and policymakers managing risk in an increasingly interconnected financial landscape.
This paper examines the market reaction to the approval of spot Bitcoin and Ethereum exchange-traded funds (ETFs), focusing on the return dynamics of a functionally diverse types of leading cryptocurrencies, including coins (BTC, BCH, LTC, XRP), smart contract platforms (ETH, ADA, AVAX), and utility tokens (LINK, MATIC). Using high-frequency intraday data, we perform an event study to assess the abnormal returns around the ETF approval dates. This study makes a significant contribution to the literature on event studies by being the first to examine investorsâ reactions to information arrival in a âprimary market.â Both the market model and the capital asset pricing model (CAPM) are applied to evaluate the effects of ETF approval on individual asset returns. Our results reveal that spot Bitcoin ETF approval by the US Securities and Exchange Commission leads to significant positive abnormal returns, along with heightened market volatility. In contrast, spot Ethereum ETF approval has had more modest effects. Moreover, we observe considerable shifts in the volatility spillovers among Bitcoin, Ethereum, and other major cryptocurrencies after the ETF approval, reflecting a change in market sentiment and interconnectedness. This analysis enhances understanding of how institutional products, such as ETFs, shape cryptocurrency market behavior, offering valuable insights for regulatory frameworks and investor strategies.
This study investigates the safe-haven and diversification roles of gold and Bitcoin in financial markets from 2015 to 2025. With rising economic uncertainties, the need for reliable safe-haven assets has become critical. Gold has historically provided stability during crises, whereas Bitcoin's volatile nature raises questions about its reliability as a safe haven. Through empirical analysis, including GARCH models and quantile regression, the research evaluates the performance of both assets during market stress. Findings indicate that gold consistently outperforms Bitcoin in terms of downside protection, confirming its status as a traditional safe-haven asset. Conversely, Bitcoin offers potential diversification benefits, enhancing portfolio performance when combined with gold. The results support the notion that integrating both assets can lead to improved risk-adjusted returns, making a dual-asset investment strategy a practical approach for investors navigating market uncertainties.
This study analyzes the time-varying interactions among assets in the digital financial asset market. Within the scope of the study, 1,820 daily observations from the 2020â2025 period for Ethereum, Ripple, Binance Coin, Cardano, Stellar, IOTA, Stacks, and Chainlink are examined using the Generalized R² method proposed by Balli et al. (2023). This approach reveals both contemporaneous and lagged interconnectedness between assets, thereby enabling an understanding of how dynamic relationships evolve over time. The results indicate that market interconnectedness is not stable over time and that the transmission of shocks tends to intensify particularly during periods of uncertainty. The findings show that Ethereum maintained a central role throughout the analysis period, while Cardano, STX, LINK, and IOTA were more exposed to shocks. These results underscore the necessity of policy frameworks that address not only individual asset risks but also contagion risks to promote market stability. From an investorâs perspective, it is recommended that portfolio compositions consider both contemporaneous and lagged effects.
This study provides an in-depth analysis of the dynamic connectedness between G7 stock market indices, traditional cryptocurrencies (Bitcoin, Ethereum), gold, digital gold (PAXG, XAUT), and companies specializing in artificial intelligence (AI). Covering the period from 2020 to 2024, the analysis focuses on four distinct periods: the COVID-19 pandemic, the Russia-Ukraine conflict, the banking crisis triggered by the collapse of Silicon Valley Bank in March 2023 and the speculative rise in the gold markets in 2024. The methodology employs a Quantile Vector Autoregressive (QVAR) connectivity approach, starting with the median quantile and systematically extending to various quantiles to capture the entire distribution of connectedness under different market conditions. Our results reveal significant fluctuations in the Total Connectivity Index (TCI) during the studied crises and demonstrate how the roles of key assetsâBitcoin, Ethereum, gold, PAXG, XAUT, and AI firmsâshift between being net emitters and receivers of shocks. These shifts underscore the importance of asset selection in crafting effective hedging strategies. Specifically, we observe that G7 investors adopt varying diversification strategies depending on their domestic market conditions and the specific crisis period. The study highlights that assets for diversification and risk reduction vary by country and crisis. Traditional cryptocurrencies and AI companies in general emerge as promising diversification tools, especially in times of technological disruption and economic uncertainty. Several financial implications for investors and policymakers are proposed, providing insights for optimizing portfolio resilience in the face of global market volatility.
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, TodaâYamamoto 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. TodaâYamamoto 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.
Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the meanâvariance model introduced by Markowitz. However, this framework relies on restrictive assumptionsâsuch as normally distributed returns and symmetric risk preferencesâthat often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the MeanâDeviationâEntropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are consideredâShannon entropy, Tsallis entropy, and cumulative residual SharmaâTanejaâMittal entropy (CR-STME)âto explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical meanâvariance and meanâMAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.
Yuankui Wang, Mohd Fahmi Ghazali, Ruzanna Ab Razak, Mohd Azlan Shah Zaidi
This study applies Phase Space Reconstruction and Phase Space LSTM to analyze Bitcoinâs interactions with Gold, S&P 500, U.S. Bonds, EUR/USD, and Crude Oil, revealing hidden dependencies and chaotic structures in financial markets. Study implement a multi-method validation framework combining the Rosenstein algorithm for Lyapunov exponent estimation, 0 â 1 test for chaos and BDS test to provide robust evidence for deterministic chaos. Results indicate that most assets exhibit deterministic chaos, with price evolution highly sensitive to liquidity conditions and macroeconomic forces. Phase space analysis conducted in optimal four-dimensional embeddings uncovers stronger predictive linkages between Bitcoin and U.S. Bonds, reinforcing its growing dependence on global financial conditions. The application of PS-LSTM significantly enhances forecasting accuracy, demonstrated through rigorous validation including statistical significance testing and economic significance evaluation using risk-adjusted performance metrics. These findings suggest that cryptocurrencies are not isolated assets but deeply entangled with systemic financial fluctuations, necessitating a reassessment of market stability and risk propagation through the lens of statistical mechanics and econophysics. ⢠PSR reveals hidden dependencies across Bitcoin, gold, stocks, bonds, exchange rate and commodities. ⢠Phase space analysis reveals that Bitcoin-bond linkages indicate macroeconomic integration. ⢠Phase Space LSTM (PS-LSTM) enhances forecasting accuracy, reducing overfitting and improving predictive stability across all assets. ⢠PS-LSTM reduces overfitting and improves forecasting across all asset classes. ⢠Chaos detection confirms the presence of nonlinear dynamics in cryptocurrency and commodity markets. ⢠Higher-dimensional embeddings enhance the detection of causality between financial assets.
Taxation of Non-Fungible Tokens (NFTs) is a topical challenge in the emerging global digital economy. NFTs, being individual blockchain assets symbolizing ownership of art, music, gaming collectibles, or virtual property, are difficult to fit into traditional categories of property, securities, or commodity law. India's Finance Act 2022 brought a framework for taxing Virtual Digital Assets (VDAs) including NFTs, by levying a flat 30 per cent tax on income arising on their transfer and a 1 per cent Tax Deducted at Source (TDS). This legislative move, making NFTs a part of the official economy, has at the same time created ambiguity around valuation, fairness, and the larger digital innovation implications. The present paper embarks on an in-depth analysis of NFT taxation in India by putting the Finance Act 2022 within the overall policy context of economic formalisation. It analyzes the complexities of valuation, wherein subjective determination of price and unstable market conditions restrict even-handed assessment; risks of double taxation, especially where royalties, resale profits, and GST overlap; complexities of cross-border enforcement in decentralized blockchain transactions; and compliance burden on investors, creators, and exchanges through compulsory TDS deductions. These bring out the inflexibility and shortfalls in the current framework. As a backdrop to understand India's strategy, the paper contrasts global practices. The United States has released Internal Revenue Service (IRS) guidance that indicates NFTs can be considered collectibles and taxed with a premium capital gains rate. The United Kingdom uses principles of capital gains tax to the transfer of NFTs, with the aim of equity with other assets. Singapore has exempted some NFT transactions from Goods and Services Tax (GST), which is a forward-thinking move, while the European Union is still considering a harmonized digital tax code under its proposed reforms of the digital economy. These cross-country views expose that India's flat-rate policy is an extreme outlier in its harshness, threatening to drive creators and investors to more innovation-friendly places. The paper also addresses wider policy issues, such as whether NFTs are subject to capital gains rules or a general rate of tax, whether authentic creators should be distinguished from speculative traders, and how international cooperation, especially under the OECD's digital tax efforts, could reduce cross-border revenue losses. Pursuant to these analyses, the report provides in-depth recommendations: the introduction of transparent and standardized valuation guidelines for NFTs; differentiated treatment for creators to foster innovation; relief from compliance burdens by amending the 1 per cent TDS; consideration of bilateral tax treaties to cover cross-border NFT transactions; and a phase-wise movement towards a capital gains tax model. By plugging a major research lacuna in Indian juristic literature, this article contends that though India's taxation of NFTs is an important starting point, the existing architecture is incomplete and unbending. What is needed is a remodeled approach one that scales the state's interests with the imperative to promote a dynamic digital economy.