Localizing Multivariate CAViaR

Abstract

Risk transmission among financial markets and their participants is time- evolving, especially for the extreme risk scenarios. Possibly sudden time variation of such risk structures ask for quantitative technology that is able to cope with such situations. Here we present a novel localized multivariate CAViaR-type model to respond to the challenge of time-varying risk contagion. For this purpose a local adaptive approach determines homogeneous, low risk variation intervals at each time point. Critical values for this technique are calculated via multiplier bootstrap, and the statistical properties of this “localized multivariate CAViaR” are derived. A comprehensive simulation study supports the effectiveness of our approach in detecting structural change in multivariate CAViaR. Finally, when applying for the US and German financial markets, we can trace out the dynamic tail risk spillovers and find that the US market appears to play dominate role in risk transmissions, especially in volatile market periods.

Keywords: conditional quantile auto-regression, local parametric approach, change point detection, multiplier bootstrap

JEL Classification: C32, C51, G17