Regime-Dependent Dependence: Cryptocurrencies and Traditional Assets Under Structural Breaks

This study proposes a multi-stage approach to modelling dependencies using Vector Autoregression (VAR), Nonlinear Autoregressive Neural Network (NAR-NN) models, and copulas. This paper aims to assess the dynamic dependency structure between cryptocurrencies and traditional financial assets, considering regime changes and safe-haven properties. The proposed methodology integrates change point detection with copula-based conditional correlation Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to identify structural breaks and nonlinear de-pendencies between regimes. A sequential change point procedure based on fast signal segmentation is employed to detect structural changes. According to the study, cryp-tocurrencies and traditional assets exhibit regime-dependent dependence. It was also determined that during periods of market stress, this dependency significantly in-creases. This paper has significant implications for portfolio diversification, risk man-agement, and the role of digital assets in financial markets. Furthermore, this proposed methodology contributes to literature by capturing nonlinear, time-dependent, and re-gime-dependent dependency structures.