With the rapid growth of the cryptocurrency market, researchers increasingly study the price fluctuations and market behavior of digital assets. Gold, as a traditional safe-haven asset, often shows an inverse relationship with high-risk financial assets. Recently, scholars have focused on how gold market volatility affects cryptocurrencies, exploring potential co-movement or substitution effects. This study uses Python and econometric tools, including the Vector Autoregression (VAR) model, Granger causality test, impulse response functions, and forecast error variance decomposition, to analyze the impact of gold price changes on Bitcoin and Ethereum. Using weekly closing prices from 2018 to 2024, the results show that Bitcoin’s price is positively influenced by gold futures in the short to medium term, while gold shows a negative feedback response to Bitcoin’s returns with a two-period lag. Ethereum appears more independent and less affected by gold or Bitcoin. Strong interlinkages exist between Bitcoin and Ethereum, with Bitcoin playing a dominant role in influencing Ethereum’s price. This study has improved the understanding of the connections between cryptocurrencies and traditional assets., which also provides investors with insightful information on portfolio management.
With the increasingly turbulent political situation and the outbreak of public health events without warning, it will not only affect people’s physical health, but also affect the global financial market, causing the market to fall into a huge crisis, thus leading to a continued decline in the worldwide economy. During periods of financial market turmoil, many investors fall into panic and urgently need a “haven” to protect their assets. With the rise of the digital economy, gold no longer seems to be the only safe-haven option. Bitcoin has gradually entered the investors’ field of vision. Some investors believe that Bitcoin can become an emerging safe-haven asset that is as important as or surpasses gold. Based on an analysis of the safe-haven properties of Bitcoin and gold during major political and historical events and public health events, this article will clarify which of the two is more suitable as a reliable contemporary safe-haven asset and provide advice to investors.
Abstract Bitcoin has emerged as a highly attractive and reliable investment asset for financial managers, businesses, and economic firms due to its unique features such as high security, decentralization, and potential for increased income. Consequently, Bitcoin price prediction has become a significant topic of interest among financial and economic analysts and researchers. Forecasting in such contexts often involves uncertain conditions and limited information. Grey systems theory, which specializes in analyzing problems with small samples and insufficient information, offers a promising approach. This study aims to predict the price of Bitcoin using an advanced model of grey systems theory: the fractional multivariable grey model (FGM(1, N )). The FGM(1, N ) model stands out by incorporating external factors into its predictions. Specifically, this research utilizes the FGM(1,3) model, considering the crude oil and gold prices to forecast Bitcoin price. The results demonstrate that the FGM(1,3) model provides more accurate predictions and better performance than the FGM(1,1) model, which does not include external factors like oil and gold prices. This study highlights the significant impact of crude oil and gold price trends on Bitcoin's market and underscores the effectiveness of the multivariable fractional grey model in financial forecasting.
The cryptocurrency market has undergone unprecedented growth and transformation, driven by technological advancements, global crises, and shifts in financial paradigms. This thesis comprises three empirical studies that collectively enhance our understanding of diverse cryptocurrency types, namely Shariah-compliant cryptocurrencies, green cryptocurrencies, stablecoins (fiat-backed, gold-backed, crypto-backed), and traditional cryptocurrencies, during major global shocks (the COVID-19 pandemic, the Russia-Ukraine war, and the FTX exchange collapse), with a focus on their resilience, safe-haven properties, market connectedness, portfolio performance and stability.The first empirical chapter investigates the resilience and safe-haven characteristics of Shariah-compliant cryptocurrencies relative to conventional ones. Using wavelet coherence and DCC-GARCH, quantile, and threshold regressions as robustness, it reveals that Shariah-compliant cryptocurrencies offer superior short- and medium-term safe-haven properties, particularly during geopolitical turmoil. Portfolio optimisation demonstrates that these assets deliver higher risk-adjusted returns during shocks, underscoring their potential in crisis-resilient portfolio construction.The second empirical chapter analyses the dynamic connectedness of green and non-green cryptocurrencies with traditional and environmental assets during the COVID-19 pandemic and the Russia-Ukraine war. Using a TVP-VAR framework and three portfolio strategies, namely minimum variance, correlation, and connectedness, the study shows that green cryptocurrencies, despite heightened volatility during crises, enhance hedging effectiveness when combined with strategic assets such as carbon futures, gold, and energy commodities.The third empirical chapter evaluates the impact of the FTX collapse on the return and volatility of stablecoins and traditional cryptocurrencies using Difference-in-Differences and event study methods. The findings reveal that while stablecoins were more resilient than traditional cryptocurrencies, they were not immune to systemic shocks. Notably, gold- and crypto-backed stablecoins did not outperform fiat-backed ones during crises, underscoring the importance of liquidity and transparency over collateral type.Overall, this thesis provides novel empirical insights into the evolving cryptocurrency landscape, offering actionable implications for investors, regulators, and policymakers navigating digital assets in times of systemic stress.
This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices.
This paper introduces a model for forecasting daily Bitcoin returns using data sourced from Yahoo Finance. Additionally, I propose a simple trading strategy that leverages the model's forecasts for Bitcoin trading.
In the AI era, we contribute to the literature by uncovering that the price dynamics of most AI tokens could be fully characterized by the processes driven by fractal Brownian motion, which robustly supports the principles of the fractal markets hypothesis. Using rescaled range (R/S, i.e., Fractal) analysis and the wavelet coherence technique, we analyzed daily log-returns from seven major AI tokens and Bitcoin over the period 2020–2024. Our empirical results rejected the weak form of the Efficient Market Hypothesis (EMH), supporting the Fractal Market Hypothesis (FMH) as a better explanation for the dynamics of AI crypto tokens. More importantly, the log-returns of all analyzed AI tokens, each exhibiting a Hurst exponent exceeding 0.58, provided evidence of persistent behavior and an inherent tendency toward positive price trajectories. These results implied that Fractal analysis can enhance investors' ability to model return dynamics and identify potential appreciation in AI tokens, particularly as short-term trading activity intensifies during episodes of elevated market turbulence. Finally, this work reveals that AI tokens exhibit strong coherence patterns with Bitcoin, varying across time and frequency domains, suggesting Bitcoin's limited role as a hedge against AI tokens. Crucially, this study highlights the significant role of AI tokens as potential safe-haven assets during market turmoil, offering valuable insights for portfolio diversification for crypto investors with intuitive and plausible results that carry strong policy implications.
Cryptocurrencies have emerged as a transformative force across various sectors of the global economy, particularly in financial markets, where they influence asset classes and market dynamics. In this context, Asia's leadership in both cryptocurrency adoption and Islamic finance provides a unique opportunity to assess whether Islamic stock markets outperform their conventional counterparts amid cryptocurrency volatility. This study employs advanced econometric techniques, including panel unit root tests, Johansen-Fisher cointegration, pairwise Granger causality tests, and regression analysis, to empirically examine the influence of cryptocurrencies on the performance of Islamic and conventional stocks. Weekly data from 13 Asian countries spanning 2016–2019 are analyzed, with a focus on two distinct periods: before and after the 2017–2018 cryptocurrency crash. The findings reveal bidirectional significant causality between conventional stock returns and cryptocurrency returns. In contrast, Islamic stock returns exhibit a unidirectional influence on cryptocurrency prices, with no reciprocal effect observed across all panels. The findings indicate that during both overall and pre-crash periods, cryptocurrency returns positively affect Islamic and conventional stock markets, with Islamic indices experiencing a stronger impact. However, post-crash, both conventional and Islamic stocks suffer negative consequences from cryptocurrency fluctuations, with conventional stocks experiencing more pronounced losses, while Islamic stocks display greater resilience. This suggests that investor sentiment and risk appetite in Islamic markets differ from those in conventional markets, particularly during periods of cryptocurrency instability. Overall, our findings indicate that rising cryptocurrency returns, especially post-crash, may divert investors from stock markets across Asia, with conventional markets being more affected than Islamic markets. The study offers valuable insights for investors, policymakers, and regulators, emphasizing that conventional stock market investors face greater exposure to cryptocurrency risks. It advocates for the implementation of robust policies to mitigate these risks and recommends expanding future research to encompass other regions and incorporate additional control variables.