Multinomial Backtesting of Distortion Risk Measures
S\"oren Bettels, Sojung Kim, Stefan Weber
TL;DR
This paper introduces a multinomial backtesting approach for a broad class of distortion risk measures, enhancing risk model validation through stratification and randomization techniques.
Contribution
It proposes a novel multinomial backtesting method applicable to general distortion risk measures, expanding existing validation tools.
Findings
Method performs well in numerical case studies
Effective stratification and randomization improve backtesting accuracy
Applicable to various distortion risk measures
Abstract
We extend the scope of risk measures for which backtesting models are available by proposing a multinomial backtesting method for general distortion risk measures. The method relies on a stratification and randomization of risk levels. We illustrate the performance of our methods in numerical case studies.
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Taxonomy
TopicsCredit Risk and Financial Regulations · Risk and Portfolio Optimization · Statistical Methods and Inference