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.

Additional Metadata
Keywords C14, extreme value theory, G01, heavy tails, risk management, tail dependence
Persistent URL,
Journal Journal of Financial Econometrics
Note corresponding author at De Nederlandsche Bank
van Oordt, M.R.C, & Zhou, C. (2019). Estimating Systematic Risk under Extremely Adverse Market Conditions. Journal of Financial Econometrics, 17(3), 432–461. doi:10.1093/jjfinec/nbx033