Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 007

Localizing Multivariate CAViaR

Yegor Klochkov
Wolfgang Karl Härdle
Xiu Xu

The risk transmission among financial markets is time-evolving, especially for
the extreme risk scenarios. The possibly sudden time variations of these 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 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.

conditional quantile autoregression, local parametric approach, change point
detection, multiplier bootstrap

JEL Classification:
C32, C51, G17