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.
Highlights
Based on the early work of [1,2], the Chicago Board Options Exchange (CBOE) started calculating its implied volatility index VIX, today well-known as a “fear gauge”, back in 1993
For potential regime switches within the first trees, we propose the algorithm depicted by Figure 12: Firstly, the two copula families that appear most often within the rolling window analysis (RWA) are identified for each index pair; Secondly, we differentiate between pairs with overall positive and negative dependence measured by the empirical Kendall’s τ
In the present work, using an regular vine (R-vine) Markov-switching model with different, pre-defined R-vine structures, we aimed to find periods of “normal” and “abnormal” regimes within a data set consisting of North-American, European and Asian equity and volatility indices with an additional commodity index
Summary
Based on the early work of [1,2], the Chicago Board Options Exchange (CBOE) started calculating its implied volatility index VIX, today well-known as a “fear gauge”, back in 1993. Thereby, they introduced a new asset class and the first general index for market uncertainty. From a portfolio management perspective, similar to the well-known leverage effect initially discussed by [9], especially the usually observed asymmetric and negative dependence between implied volatility and equity indices can be useful for hedging purposes and risk management (cf the recent work of [10] based on quantile regression or [11] on asymmetric volatility clustering). Implied volatility is in discussion to potentially have some predictive power on future returns as well (cf. [12,13]) even though such results may not be generally valid as recently shown by [14] and therein
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