Backtesting Systemic Risk Forecasts using Multi-​Objective Elicitability

Activity: Talk or presentationScience to professionals/public

Description

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 https://arxiv.org/abs/2104.10673 which is joint work with Yannick Hoga.
Period2 Dec 2021
Event titleTalks in Financial and Insurance Mathematics
Event typeUnkonwn