Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability

Activity: Talk or presentationScience to science

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.
Period9 Sep 202110 Sep 2021
Event title9th Austrian Stochastics Days
Event typeUnkonwn
Degree of RecognitionNational