Dynamic standard analysis of cryptocurrencies and financial market indices using (MGARCH) models
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Abstract
This research aims to examine the relationship between cryptocurrencies and traditional financial markets using standard methods, specifically MGARCH models, to measure and analyze their potential to revolutionize the world of financial technology. However, their long-term impact on financial markets may be far more profound in the future. They could fundamentally alter the economic and financial structure and transform the way financial institutions and banks operate. Cryptocurrencies are numerous, with Bitcoin being the most widely traded and representing a modern form of private money. Thanks to its technological capabilities, its global transaction networks are relatively secure and fast, offering promising prospects for further development. Nevertheless, cryptocurrencies are unlikely to challenge the dominance of sovereign currencies and central banks, particularly in regions with major currencies. As with other innovations, cryptocurrencies present a challenge to financial regulators, primarily due to the anonymity of their issuers and their status as cross-border financial assets. This necessitates a legal framework that supports highly transparent transactions and fosters an environment conducive to greater alignment with transnational and transcontinental transactions in traditional and modern financial markets. This framework also addresses the potential for replacement or competition between traditional financial markets and cryptocurrency markets. The research yielded several key findings, including: conditional covariance analysis reveals that the transfer of volatility between traditional financial markets and cryptocurrencies is limited for most periods, with only temporary spikes during shocks. Cryptocurrencies emerge as relatively independent assets from traditional markets, while a clearer correlation is evident within the cryptocurrency market itself. These findings underscore the importance of using dynamic MGARCH models when studying co-risks and diversifying investment portfolios. Furthermore, the results of the AR (1)–GARCH (1,1) model estimation confirm that the returns of financial markets and cryptocurrencies exhibit complex dynamic behavior involving self-dependency and highly sustained conditional covariance. Digital currencies, especially Bitcoin, stand out as a source of high risk due to the strength of shocks and the continuity of their time impact, which calls for the adoption of advanced standard tools when analyzing them or predicting their fluctuations. Daily data for five indices was used during the period from August 16, 2011 to June 21, 2024. All time series for the indices are converted into logarithmic returns, resulting in (3281) daily views for each indice.
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