Really happy to share that the paper “Modeling extreme events: time-varying extreme tail shape” was published on the Journal of Business & Economic Statistics and is available here to read (Open Access). Delighted to have been working with Prof. André Lucas (professor of Financial Econometrics Vrije Universiteit Amsterdam), Dr. Bernd Schwaab from the European Central Bank’s Financial Research Division, and Dr. Xin Zhan, Research Advisor at Sveriges Riksbank (Central Bank of Sweden).
This paper propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail parameters. We establish parameter regions for stationarity and ergodicity and for the existence of (unconditional) moments and consider conditions for consistency and asymptotic normality of the maximum likelihood estimator for the deterministic parameters in the model. Two empirical datasets illustrate the usefulness of the approach: daily U.S. equity returns, and 15-minute euro area sovereign bond yield changes.