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 dependencies between regimes. A sequential change point procedure based on fast signal segmentation is employed to detect structural changes. According to this study, cryptocurrencies and traditional assets exhibit regimedependent dependence. It was also determined that during periods of market stress, this dependency significantly increases. This paper has significant implications for portfolio diversification, risk management, and the role of digital assets in financial markets. Furthermore, this proposed methodology contributes to the literature by capturing nonlinear, time-dependent, and regime-dependent dependency structures.