Benjamin Mudiangombe Mudiangombe, John Weirstrass Muteba Mwamba
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 goldâs negative influence on BTC/ZAR. Bitcoinâs 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.
We quantify the economic consequences of Ethereumâs transition from Proof-of-Work to Proof-of-Stake. We document a structural break in inflation dynamics, shifting to an ARIMA(2,1,1) process with deflationary tendencies. The relationship between inflation and staking returns weakens post-Merge, challenging assumptions about incentive structures in Proof-of-Stake systems. Analysis reveals significant changes in market microstructure, including reduced spot trading volume and altered futures market behavior. We identify complex feedback loops between on-chain metrics and market variables, defying traditional equilibrium models. Our results suggest the need for new economic models to understand Proof-of-Stake systems and their market implications.
This chapter explores the intersection of cryptocurrency crime and artificial intelligence (AI), highlighting both the threats posed by AI-driven cybercriminal activities and the potential of AI-based countermeasures. Cybercriminals increasingly leverage AI for money laundering, fraud, market manipulation, ransomware attacks, and identity theft, exploiting vulnerabilities in smart contracts and decentralized finance (DeFi) platforms. Conversely, AI is a powerful tool for combating these threats, aiding in blockchain analysis, anomaly detection, and Anti-Money Laundering (AML) enforcement. Advanced machine learning models enhance Know Your Customer (KYC) protocols and enable predictive crime prevention by analyzing transactional patterns. Additionally, this chapter examines challenges such as regulatory loopholes, adversarial AI, and jurisdictional complexities. The future implications of AI in financial crime prevention, including the role of quantum computing and emerging financial technologies, are also discussed.
This study examines how Bitcoinâs relationships with major financial assets evolved following the January 2024 spot ETF approval. Using DCC-GARCH analysis, we find Bitcoinâs correlation with stocks exhibits a complete trend reversal from declining to increasing trends, while other assets show more limited changes. TVP-VAR spillover analysis reveals declining Bitcoin self-spillover and strengthened bidirectional risk transmission with equity markets. These findings indicate the ETF approval altered Bitcoinâs market role, transitioning from decoupling to integration with financial assets and reducing its diversification benefits in equity-focused portfolios.
The goal of this research is to analyze political uncertainty's short- and long-term impact on the volatility of Bitcoin throughout the US presidential election period (2023-2024), and test its value as a hedge asset in the face of rising political tensions. The GARCH-MIDAS model used here selects high-frequency daily returns on Bitcoin and low-frequency macroeconomic and political data, such as the Economic Policy Uncertainty Index (EPU), the Volatility Implied Index (VIX), and an irregular dummy variable for political events (POL_EVT). The empirical evidence depicts how Bitcoin is highly sensitive to political shocks, both sudden (short-run) and institutional (long-run), with its volatility speeding up as uncertainty increases. In contrast to traditional safe-haven securities such as gold or government bonds, Bitcoin does not exhibit hedging behavior during times of political turmoil. Instead, it is a high-risk speculation asset, responding in real-time but destabilizing to evolving political events. Moreover, the GARCH-MIDAS model proved to be outstanding in capturing the time and non-linear impacts of uncertainty compared to standard models, buttressing the importance of including political factors when studying the volatility dynamics of cryptocurrencies.
ChiâWei Su, YuâMei Ding, KaiâHua Wang, Xiaoqing Wang
ABSTRACT In this paper, the safeâhaven attributes of green bonds, gold, and bitcoin are compared to those of traditional bonds under various time periods and quantiles by using the WQC methodology. The results indicate that green bonds exhibited a stable safeâhaven function at longer time horizons during the full sample period, whereas other assets did not have safeâhaven features. During the COVIDâ19 pandemic, bitcoin exhibited safeâhaven attributes at all time horizons, whereas gold demonstrated these characteristics over the short and medium terms. In the sample period during the RussiaâUkraine war, green bonds had strong safeâhaven properties at shorter and middle time horizons, whereas bitcoin had these properties at longer time spans. In this paper, a multivariate network framework that includes green bonds, gold, and bitcoin is constructed, and the theoretical foundations that influence the safeâhaven attributes of assets are detailed. In addition, this study clearly presents the safeâhaven effects of assets under various sample periods, time horizons, and quantiles, thereby bridging the gap of existing studies that ignore time frequency. Thus, this paper provides advice for investors, regulators, and policyâmakers, such as choosing portfolios on the basis of asset characteristics, monitoring asset disclosure, and encouraging the trading of safeâhaven assets.
Abstract This paper presents a systematic literature review of 137 peer-reviewed publications from 41 journals, examining the interconnectedness between cryptocurrencies and traditional financial markets. Using a rigorous three-stage methodology for study selection, we identify key research themes including spillover effects, volatility transmission, interdependence, hedge effectiveness, and safe-haven properties of cryptocurrencies. Our analysis reveals that GARCH-based models dominate early work on volatility and contagion, while more recent studies adopt advanced approaches, such as cross-quantilogram, wavelet coherence, and multifractal detrended cross-correlation, to capture non-linear, time-varying relationships without assuming stationarity. Our review offers three major contributions. First, we provide a comprehensive classification of the interconnectedness between different types of cryptocurrencies and financial markets, highlighting their evolving roles as hedges, safe havens, or diversifiers. Second, we synthesize empirical findings to show how spillovers, time-varying correlations, tail dependencies, and contagion risks intensify under major events, such as COVID-19, regulatory shifts, and geopolitical conflicts. Third, we draw attention to overlooked areas, including emerging market dynamics and macroeconomic determinants. We recommend that policymakers implement early warning systems and proactively monitor volatility and connectedness in crypto markets to reduce contagion risks and maintain financial stability. Policy frameworks should consider the unique features of crypto markets and the time-varying interlinkages between cryptos, commodities, fiat currencies, and equities. Investors, in turn, should track cryptocurrency price movements closely, as they provide valuable signals for forecasting broader market trends and improving portfolio risk management. These insights have practical implications for risk mitigation and decision-making in increasingly integrated financial systems.
Purpose The purpose of this study is to examine the response of various cryptocurrency market classes to the Federal Reserveâs quantitative easing (QE) announcements. Design/methodology/approach We used the time-varying parameter vector autoregressive model to analyze the price spillover and interconnectedness between the US market assets/indices, and cryptocurrencies, explicitly focusing on Layer 1 tokens, DeFi tokens, Exchange-Based Tokens, Smart Contracts and Stablecoins. Findings The findings reveal that most cryptocurrency classes exhibit notable price spillovers from US assets/indices. However, the analysis suggests that only high-return tokens show significant responses during the Federal Reserve QE announcements and receive price spillover. In contrast, leading tokens remain unaffected by such spillovers, which suggests that during QE periods investors tend to seek higher returns and are more likely to invest in high return assets. It reflects a preference for assets that could offer greater returns when monetary policy is easing while more stable cryptocurrencies are less impacted by policy changes, implying that investors may adjust their strategies by shifting toward high return cryptocurrencies during periods of QE to capitalize on these market movements. Research limitations/implications This study focuses on a limited selection of cryptocurrency classes and specific QE events, which may not fully capture all market dynamics or incorporate newer cryptocurrency assets due to insufficient historical data. Another key limitation of this study is the inclusion of the COVID-19 pandemic period, which represents an extraordinary macroeconomic environment that may not be representative of normal market conditions. Future research could explore a broader range of crypto assets over an extended timeframe and sub-period analysis excluding the pandemic years or incorporate regime-switching models to account for structural breaks. Practical implications Understanding the response of different cryptocurrency assets to QE announcements can significantly assist investors in making informed decisions regarding asset allocation during these periods, guiding them in identifying the most profitable cryptocurrencies as alternatives to traditional assets. Originality/value In contrast to prior research, which primarily concentrates on the impact of QE on financial markets or confines its analysis to major and prominent crypto assets, this study provides a comprehensive examination of how specific cryptocurrency classes respond to macroeconomic policy changes.
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.
ABSTRACT In the context of ChinaâUS trade friction, we use the TVPâVAR, SHAP, DECO, and CDB model to test the spillover volatility, hedging, safe haven, and portfolio returns of cryptocurrencies based on daily data of Bitcoin, Ethereum, Litecoin, Ripple, CSI300 Index, Shanghai Composite Index, S&P500 Index, and Nasdaq Index. The results show a significant shortâterm timeâvarying asymmetric volatility spillover effect between cryptocurrencies and the US and Chinese stock markets. Cryptocurrencies can be used as shortâterm hedging assets for the Chinese stock market. The evidence also shows no longâterm correlation between cryptocurrencies and the stock market. Therefore, in periods of volatility caused by trade friction between the two countries, such as the imposition of high tariffs, investors can regard cryptocurrencies as shortâterm hedging assets and longâterm safe haven assets to mitigate losses caused by stock market fluctuations. In addition, adding cryptocurrencies to stock index portfolios can significantly diversify risks and increase returns.
The rapid rise of Bitcoin ignited a global frenzy over cryptocurrencies, driving a surge in their issuance and investment. Unprecedented returns on these assets have led to comparisons with irrational exuberance. Against this backdrop, and adopting a behavioural finance perspective, this thesis thoroughly investigates the role of investor sentiment in cryptocurrency prices and volatility across three empirical studies. Sentiment is proxied by the Fear and Greed Index (FGI), published by Alternative.me, and the Economic News Sentiment Index (NSI), developed by the Federal Reserve Bank of San Francisco. The first study examines volatility connectedness among six Bitcoin (BTC) currency pairs and identifies its determinants. The results indicate that BTC/USD and BTC/GBP are major volatility transmitters, while BTC/USDT remains relatively isolated. Key determinants include trading volume, sentiment, economic policy uncertainty, and gold volatility. There is an asymmetric effect of sentiment derived from the FGI: optimistic sentiment intensifies volatility connectedness, whereas pessimistic sentiment dampens it. Policy uncertainty and gold volatility are positively associated with spillover intensity, with cryptocurrency-related events further shaping the degree of connectedness. The second study focuses on intraday cross-exchange (i.e., Bitfinex and Kraken) price discovery for Bitcoin and Ethereum, along with its potential drivers. The results show that Bitfinex dominates price discovery during most periods, but the pandemic alters this process. We identify a shift in drivers, with market quality factors losing significance and news sentiment emerging as a more prominent influence in the post-pandemic period. During episodes of heightened news sentiment, Bitfinex consolidates its leading position. Additionally, Ethereumâs price discovery is significantly associated with intraday volatility. The third study assesses whether the inclusion of the FGI in GARCH(1,1), EGARCH(1,1), and HAR(1,7,30) models improves the accuracy of volatility forecasts for twelve cryptocurrencies. The findings suggest that incorporating the FGI enhances predictive performance, with variation across assets. Bitcoin, Ethereum, and cryptocurrencies technologically linked to them (e.g., Litecoin, DASH and Ethereum Classic) benefit from the integration of sentiment in most cases. EGARCH+FGI and HAR+FGI consistently outperform competing models, particularly at the weekly forecast horizon. This thesis provides behavioural finance insights for cryptocurrency investors and policymakers. Investors may leverage the FGI and the NSI to refine trading strategies and inform venue selection, while policymakers may incorporate the FGI into monitoring frameworks to better identify systemic risks and anticipate excessive cross-market spillovers.
Amid growing global urgency for climate action, innovative financial mechanisms are critical for advancing renewable energy transitions in developing economies. This study investigates the role of financial technology (fintech), with a focus on foreign portfolio investment (FPI), in influencing renewable energy investment (REINV) across 54 developing countries in Africa, Asia, and Latin America from 2010 to 2023. Employing a multi-method empirical approach, comprising Spatial Durbin Models (SDM), Quantile Regression (QR), Stochastic Frontier Analysis (SFA), and Spatial Quantile Regression (SQR), the research captures spatial dependencies, distributional heterogeneity, and efficiency dynamics. The SDM results indicate that FPI significantly increases REINV both directly (1.112) and indirectly through spillover effects (0.445), supported by significant spatial autocorrelation (0.334). Economic development and institutional quality also play key roles, with GDP per capita and institutional quality exerting positive and significant direct effects. Quantile regression reveals that FPI has a stronger influence at higher quantiles of REINV, with coefficients rising from 0.745 to 1.445, highlighting distributional inequality in fintech impact. SFA results show that FPI also enhances technical efficiency (0.912), though diminishing marginal returns are evident. Greater financial depth and electricity access reduce inefficiency, while inflation worsens it. Spatial quantile regression further confirms that regional spillovers are more pronounced among high-investment countries, underscoring the role of spatial dynamics in clean energy financing. The findings suggest that fintech can be a catalyst for renewable energy growth, especially in countries with higher institutional and financial capacity. Policy recommendations include strengthening digital infrastructure, enhancing regulatory coordination, and ensuring macroeconomic stability to fully leverage fintech's potential. Future research should explore emerging fintech tools such as decentralized finance and blockchain-based green bonds.
This paper examines the interconnection and wavelet coherence between the green cryptocurrency market and the green conventional market, utilizing daily data. The research period covers 1 July 2020 to 30 September 2024. Employing the time-varying parametric vector autoregression (TVP-VAR) model and wavelet coherence analysis, we capture both short- and long-term spillovers across markets. The results show that cryptocurrencies, particularly Binance and Litecoin, act as dominant transmitters of volatility and return shocks, while green conventional indices function mainly as receivers with strong self-dependence. Spillover intensity is highly time-varying, with peaks during periods of systemic stress, particularly during the COVID-19 pandemic, and troughs indicating diversification opportunities. These findings advance the literature on systemic risk and portfolio design by showing that crypto assets can simultaneously amplify vulnerabilities and enhance diversification when combined with green finance instruments. For policy, the results highlight the need for regulatory frameworks that integrate sustainability taxonomies, mandate environmental disclosures for digital assets, and incentivize energy-efficient blockchain adoption to align crypto markets with sustainable finance objectives. This research enhances our understanding of the interrelationship between green investments and cryptocurrencies, providing valuable insights for investors and policymakers on risk management and diversification strategies in an increasingly sustainable financial landscape.
Accreditation has historically played a central role in securities regulation, seeking to balance investor protection, market access, and capital formation. Traditionally, regulatory frameworks have relied on wealth or income thresholds as proxies for investor sophistication, premised on the assumption that individuals with greater financial resources are better equipped to manage risk and obtain professional advice. However, in rapidly evolving crypto-asset markets, these wealth-based criteria have become increasingly misaligned with market realities. Such thresholds frequently exclude technically proficient but less affluent participants, thereby perpetuating inequality and conflicting with the inclusive ethos of digital finance. Moreover, these criteria have failed to prevent significant losses among wealthy accredited investors, as evidenced by the collapses of Terra-Luna, Three Arrows Capital, and FTX. Competence-based frameworks are still underdeveloped, unevenly applied, and can become overly formal, while traditional disclosure rules do not fully address the technical and behavioral challenges of decentralized finance. This article takes a critical look at accreditation in crypto-asset markets, drawing on legal, empirical, and normative analysis. By comparing the United States, European Union, Singapore, and Russia, and examining cases like the ICO boom, Singaporeâs regulatory sandboxes, and the Terra-Luna and FTX collapses, the article shows that wealth-based accreditation falls short in fairness and effectiveness. It proposes a hybrid approach that combines competence assessments, crypto-specific disclosure, prudential safeguards, regulatory sandboxes, and international cooperation. This article contends that reforming accreditation constitutes a fundamental transformation in the approach to investor protection, advancing principles of fairness, legitimacy, and systemic robustness. By introducing a hybrid framework grounded in fairness and empirical evidence, the article contributes to policy discourse and informs scholarly understanding of the evolution of financial regulation in the context of digital innovation.
Mayank Arora, M V Gururaj, Ankush Sharma, Naveen Chilamkurti
The transition towards decentralized energy systems has spurred the need for innovative consensus mechanisms to facilitate efficient and transparent energy trading among prosumers. In response to this challenge, we propose a novel Proof of Energy Authentication and Contribution (PoEAC) consensus mechanism tailored for decentralized energy trading systems. PoEAC integrates cryptographic authentication and contribution verification to empower authenticated prosumers in the energy market. Prosumers authenticate themselves by proving ownership of energy-producing assets or storage devices, while demonstrating their contribution to the energy system through verifiable evidence of energy production or storage capacity. Leveraging cryptographic techniques such as zero-knowledge proofs and digital signatures, prosumers generate proofs of their authenticated status and contribution, which are evaluated by the consensus algorithm to validate energy transactions. The proposed model was simulated in MATLAB, with four prosumers over a 24hour horizon. Simulation results confirm that PoEAC successfully validates all legitimate energy transactions while rejecting 100 % of invalid or unauthorized trades. This paper presents the design and implementation of PoEAC, highlighting its advantages in enhancing trust, transparency, and incentivized participation in decentralized energy trading systems.
We examine how Bitcoin and Ethereum volatilities react to macroeconomic data releases from the US, Germany, and Japan before, during, and after their official announcements. Analyzing 5-minute observations from 2016 to 2023, we find that volatility responds significantly to select news categories, particularly in the pre-announcement period. US monetary policy news consistently drives volatility across all phases, with a heightened impact during the pandemic. Ethereum shows greater sensitivity to US announcements than Bitcoin but remains unresponsive to non-US news, especially before the pandemic. Our findings highlight the need to account for both pre- and post-announcement periods when evaluating the intraday price impact of macroeconomic news on cryptocurrencies. ⢠We examine the response of Bitcoin and Ethereum volatilities to macroeconomic figures. ⢠We show that volatility reacts only to a few news categories. ⢠US monetary policy news consistently affects volatility before, during, and after its release. ⢠Ethereum volatility is more sensitive to US announcements compared to Bitcoin. ⢠Ethereum exhibits less pre-announcement volatility and less sensitivity to non-US news.
This paper surveys the academic literature concerning the bubble periods in the cryptocurrency market. This study aims to understand the historical and developmental trajectory of the cryptocurrency market through its various bubble periods. This study also identifies the factors contributing to bubble formation. The study is based on the PRISMA framework for literature review. Based on the review, the cryptocurrency market experienced four major bubbles in 2011, 2013, 2017, and 2021. The enthusiasm for cryptocurrency innovation triggered the 2011 bubble. The 2013 bubble was influenced by the economic crisis that channeled funds to the cryptocurrency market due to their centralized nature. In 2017, the possibilities of Web 3.0 and altcoins increased the enthusiasm of crypto investors. The crypto winter of 2017 subsided with the rise of non-fungible tokens (NFTs), stimulating interest and driving prices in the cryptocurrency market. Specifically, speculation, media coverage, investor sentiment, herding, volatility, and coexplosivity are significant factors that trigger bubble development. Moreover, government policies and regulations can be crucial in sustaining and bursting the bubbles. This review offers a comprehensive view of academic studies on bubble periods in the cryptocurrency market. This study also provides a chronological overview of major bubble periods that have significantly influenced the market development. This study is one of the first reviews conducted to understand the development of the cryptocurrency market through bubble periods and the factors contributing to bubble formation. The study also follows the PRISMA framework for structuring the review, as the literature lacks reviews on bubble periods on the basis of this framework.
CÄtÄlin Gheorghe, Oana Panazan, Hind Alnafisah, Ahmed Jeribi
This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016â2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence.