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Conditional Quantiles and Tail Dependence

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If you have a question about this talk, please contact Mustapha Amrani.

Systemic Risk: Mathematical Modelling and Interdisciplinary Approaches

Co-author: Claudia Czado

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 dependence. In particular, we provide a new graphical characterization of tail dependence and intermediate tail dependence from plots of conditional quantiles with normalized marginal distributions. 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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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