Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/230783 
Year of Publication: 
2019
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2019-007
Publisher: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
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.
Subjects: 
conditional quantile autoregression
local parametric approach
change point detection
multiplier bootstrap
JEL: 
C32
C51
G17
Document Type: 
Working Paper

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