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Estimating Systematic Risk under Extremely Adverse Market Conditions

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  • Maarten R C van Oordt
  • Chen Zhou

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.

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  • Maarten R C van Oordt & Chen Zhou, 2019. "Estimating Systematic Risk under Extremely Adverse Market Conditions," Journal of Financial Econometrics, Oxford University Press, vol. 17(3), pages 432-461.
    • Handle: RePEc:oup:jfinec:v:17:y:2019:i:3:p:432-461.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbx033
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    Cited by:

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    3. Maarten R.C. Van Oordt, 2023. "Calibrating the Magnitude of the Countercyclical Capital Buffer Using Market‐Based Stress Tests," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 465-501, March.

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    More about this item

    Keywords

    extreme value theory; heavy tails; risk management; tail dependence;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G01 - Financial Economics - - General - - - Financial Crises

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