Localizing Multivariate CAViaR

Yegor Klochkov, Wolfgang Härdle and Xiu Xu

No 2019-007, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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 autoregression; local parametric approach; change point detection; multiplier bootstrap (search for similar items in EconPapers)
JEL-codes: C32 C51 G17 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2019007

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