The dance of the markets: unveiling bitcoin’s time-varying financial correlations using a GAS-based approach

Purpose – This study proposes a novel framework for analysing the time-varying correlations between Bitcoin and traditional financial assets, specifically the S&P 500, NASDAQ, VIX, and WTI crude oil. Design/methodology/approach – The methodology employs an asymmetric Student-t distribution to model asset returns, enhanced by Generalised Autoregressive Score (GAS) dynamics to capture changing correlation patterns. Findings – The empirical analysis shows that there are time varying correlations across assets. Our proposed model is effective in capturing asymmetry and heavy tails. Furthermore, the results indicate that explanatory variables, particularly gold prices and the US Treasury yields, exert a significant influence on the correlations between Bitcoin and the considered financial market indices. Minimum variance portfolios constructed using the asymmetric Student-t model outperform those based on alternative models (including DCC) across all considered pairs. Originality/value – Our approach enables us to capture not only the first or second-order moments but also the broad density structure, and it further allows us to examine tail dependence, which is relevant in understanding extreme events such as market crashes or surges. By modelling extreme events jointly, one can assess how cryptocurrencies and stock indices behave under stress conditions, and understanding the complex relationship between cryptocurrencies and traditional financial assets offers insights for portfolio management and risk assessment.