Estimating Systematic Risk Under Extremely Adverse Market Conditions

Maarten van Oordt and Chen Zhou ()

Staff Working Papers from Bank of Canada

Abstract: This paper considers the problem of estimating a linear model between two heavy-tailed variables if the explanatory variable has an extremely low (or high) value. We propose an estimator for the model coefficient by exploiting the tail dependence between the two variables and prove its asymptotic properties. Simulations show that our estimation method yields a lower mean squared error than regressions conditional on tail observations. In an empirical application we illustrate the better performance of our approach relative to the conditional regression approach in projecting the losses of industry-specific stock portfolios in the event of a market crash.

Keywords: Econometric and statistical methods; Financial markets (search for similar items in EconPapers)
JEL-codes: C14 G01 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2016
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Related works:
Journal Article: Estimating Systematic Risk under Extremely Adverse Market Conditions (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:16-22

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