Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability

Activity: Talk or presentationScience to science


Backtesting risk measure forecasts requires identifiability (for model validation) and elicitability (for model comparison).
The systemic risk measures CoVaR (conditional value-at-risk), CoES (conditional expected shortfall) and MES (marginal expected shortfall), measuring the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable.
We establish the joint identifiability of CoVaR, MES and (CoVaR, CoES) together with the value-at-risk (VaR) of the reference position X, but show that an analogue result for elicitability fails.
The novel notion of multi-objective elicitability however, relying on multivariate scores equipped with an order, leads to a positive result when using the lexicographic order on R^2.
We establish comparative backtests of Diebold--Mariano type for superior systemic risk forecasts and comparable VaR forecasts, accompanied by a traffic-light approach.
We demonstrate the viability of these backtesting approaches in an empirical application to DAX 30 and S&P 500 returns.

The talk is based on the preprint which is joint work with Yannick Hoga.
Period9 Sep 202110 Sep 2021
Event title9th Austrian Stochastics Days
Event typeUnkonwn
Degree of RecognitionNational