Regime Switching Vine Copula Models for Global Equity and Volatility Indices

Abstract

For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic literature that correlations and higher moments between different indices tend to vary in time. However, to the best of our knowledge, no one has yet considered a global setup including both equity and implied volatility indices of various continents, and allowing for a changing dependence structure. We aim to close this gap by applying Markov-switching R-vine models to investigate the existence of different, global dependence regimes. In particular, we identify times of "normal" and "abnormal" states within a data set consisting of North-American, European and Asian indices. Our results confirm the existence of joint points in a time at which global regime switching between two different R-vine structures takes place.

Risk Measurement and Risk Modelling Using Applications of Vine Copulas

“…We undertake an empirical application in the next section, which features a Value at Risk, (VaR) analysis, and this provides an illustration of its use in risk-assessment. Fink et al, (2017) [36] use a Markov-switching R-vine model to explore the existence of different global dependence regimes. They explore the relationships between stock and volatility indices in Asia, Europe and the USA.…”

An integrated early warning system for stock market turbulence

“…The crisis classifier consists of two key components, namely the switching ARCH (SWARCH) model (Hamilton and Susmel, 1994) and two-peak (or valley-of-two-peaks) method (Rosenfeld and De La Torre, 1983). Instead of focusing on the return horizon, the proposed classifier tackles the problem from the perspective of the volatility (Rodriguez, 2007;Kim, 2013;Fink et al, 2016;BenSaïda, 2018;BenMim and BenSaïda, 2019). The switching ARCH (SWARCH) model is adopted to label crisis/noncrisis episodes with high/low volatility regimes that imply market turbulence/tranquility (Hamilton and Susmel, 1994;Hamilton and Gang, 1996;Ramchand and Susmel, 1998;Edwards and Susmel, 2001).…”

About tests of the “simplifying” assumption for conditional copulas

“…Third, similar situation occur with most Markov-switching copula models, where a nite set of copulas is managed. In such models, the (unobservable, in general) underlying state of the economy determines the index of the box: see [12], [47], [41], [19], among others.…”