VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles

By Halbert White, Tae-Hwan Kim, Simone Manganelli. Social Science Research Network, 2015

358 citations recorded by the source corpus.

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 analyze 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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/9cf59461f1499a841e50086fcfe30a4be64e8388

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Network quantile autoregression (2019, methodology): “(Pseudo Quantile Impulse Response Function) Similar to White et al. (2015), we can define the pseudo impulse response function.”
Quantile Factor Models (2017, positive): “…of QFA in the propagation of structural shocks in factor-augmented VAR (FAVAR) models, where recent developments in quantile VAR estimation, as in White, Kim, and Manganelli (2015), provide useful tools to address this issue; (ii) relax the independence assumption on the error terms which, in…”
Quantile coherency: A general measure for dependence between cyclical economic variables (2015, neutral): “where τ 1 = τ 2 ∈ (0, 1), Hagemann (2013) proposed a version of the traditional L2-periodogram where the observations are replaced with I {F̂n,j (Xt,j ) ≤ τ } = I {Rn;t,j ≤ nτ }.”