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VAR for VaR: measuring tail dependence using multivariate regression quantiles

Simone Manganelli (), Halbert White and Tae-Hwan Kim ()

No 1814, Working Paper Series from European Central Bank

Abstract: This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently thought of as a vector autoregressive (VAR) extension to quantile models. We estimate a simple version of the model using market equity returns data to analyse spillovers in the values at risk (VaR) between a market index and financial institutions. We construct impulse-response functions for the quantiles of a sample of 230 financial institutions around the world and study how financial institution-specific and system-wide shocks are absorbed by the system. We show how the long-run risk of the largest and most leveraged financial institutions is very sensitive to market wide shocks in situations of financial distress, suggesting that our methodology can prove a valuable addition to the traditional toolkit of policy makers and supervisors. JEL Classification: C13, C14, C32

Keywords: CAViaR; codependence; quantile impulse-responses; spillover (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban and nep-rmg
Date: 2015-06
Note: 196912
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Journal Article: VAR for VaR: Measuring tail dependence using multivariate regression quantiles (2015) Downloads
Working Paper: VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles (2012) Downloads
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