The size of good and bad volatility shocks does matter for spillovers

Based on the rationale that the propagation of stock volatility shocks within a system can be affected by the size of shocks, we apply a tail-based approach of spillovers based on the variance decomposition of a quantile vector autoregression model. The analysis involves the decomposition of the realized variance into positive and negative realized semivariances using 5-min data on six major stock market indices from the US, Eurozone, UK, Japan, China, and India for the period February 14, 2000 - September 30, 2021. The results show that the propagation of volatility shocks within the system is not only shaped by the sign of the shocks (good versus bad volatility) but also by the shock size. For each good and bad volatility spillover, we detect a heterogeneity resulting from the difference in the size of spillovers between the upper and middle quantiles and thus reveal a relative intensity effect, as measured by the Relative Intensity of Shock Spillover (RISS) measure which we propose herein. This points to the necessity of going beyond studying spillovers of average shocks and employing tail-based models capable of uncovering the heterogeneous and intensity effects of the shock size. Otherwise, these features remain hidden, leading to suboptimal inferences and policy implications.