Conditional quantiles and tail dependence

Carole Bernard and Claudia Czado

Journal of Multivariate Analysis, 2015, vol. 138, issue C, 104-126

Abstract: Conditional quantile estimation is a crucial step in many statistical problems. For example, the recent work on systemic risk relies on estimating risk conditional on an institution being in distress or conditional on being in a crisis (Adrian and Brunnermeier, 2010; Brownlees and Engle, 2011). Specifically, the CoVaR systemic risk measure is based on a conditional quantile when one of the variable is in the tail of the distribution. In this paper, we study properties of conditional quantiles and how they relate to properties of the copula. In particular, we provide a new graphical characterization of tail dependence and intermediate tail dependence from plots of conditional quantiles with normalized marginal distributions (probit scale). A popular method to estimate conditional quantiles is the quantile regression (Koenker, 2005; Koenker and Bassett, 1978). We discuss the properties and pitfalls of this estimation approach.

Keywords: Intermediate tail dependence; Quantile regression; Copula; Linear conditional quantiles; Systemic risk (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (25)

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DOI: 10.1016/j.jmva.2015.01.011

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