This paper proposes a new empirical methodology for computing a cross-market index – coined CMI – based on the Factor DCC-model. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to a...

This article proposes a new empirical methodology for computing a cross-market volatility index - coined CMIX - based on the Factor-Dynamic Conditional Correlation (DCC) model, implemented on volatility surprises. This approach solves problems in treating high-dimensional data and estimating...

This article adopts the asymmetric DCC with one exogenous variable (ADCCX) model developed by Vargas (2008), by updating the concept of ‘volatility surprise’ to capture cross-market relationships. Current methods for measuring spillovers do not focus on volatility interactions, and neglect...

This paper proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor DCC-model, implemented on volatility surprises. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional...