Congratulations to Professor Lynda Khalaf! Her paper “Multilevel and Tail Risk Management” (with Giovanni Urga (Cass Business School, London, and the University of Bergamo) and Arturo Leccadito (University of Calabria)) has been accepted at the Journal of Financial Econometrics—a leading journal with a focus on the relationship between econometrics and finance, both at the methodological and at the empirical levels.
Congratulations, Lynda, on your continued success with high-quality peer-reviewed publications!
We introduce backtesting methods to assess Value-at-Risk (VaR) and Expected Shortfall (ES) that require no more than desktop VaR violations as inputs. Maintaining an integrated VaR perspective, our methodology relies on multiple testing to combine evidence on the frequency and dynamic evolution of violations, and to capture more information than a single threshold can provide about the magnitude of violations. Contributions include a formal finite sample analysis of the joint distribution of multi-threshold violations, and limiting results that unify discrete and continuous definitions of cumulative violations across thresholds. Simulation studies demonstrate the power advantages of the proposed tests, particularly with small samples and when underlying models are unavailable to assessors. Results also reinforce the usefulness of CaViaR approaches not just for VaR but also as ES backtests. Empirically, we assess desktop data by Bloomberg on exchange traded funds. We find that tail risk is not adequately reflected via a wide spectrum of combined tests and forecasts in tail risk management.