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4 - Beyond Exceedance-Based Backtesting of Value-at-Risk Models: Methods for Backtesting the Entire Forecasting Distribution Using Probability Integral Transform

Published online by Cambridge University Press:  02 March 2023

By
Diana Iercosan ,
Alysa Shcherbakova ,
David McArthur  and
Rebecca Alper
Edited by
David Lynch ,
Iftekhar Hasan  and
Akhtar Siddique
David Lynch
Affiliation:
Federal Reserve Board of Governors
Iftekhar Hasan
Affiliation:
Fordham University Graduate Schools of Business
Akhtar Siddique
Affiliation:
Office of the Comptroller of the Currency
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Summary

This chapter assesses the accuracy and possible misspecification of VaR models and offers a comparison of backtesting results using PITs over exceedances for the same sample of real portfolios. It investigates results from a set of tests used to assess unconditional coverage, conditional coverage, and independence properties of the realized VaR exceptions. This also presents a comprehensive overview of tests used to assess the uniformity and independence properties of a series of PIT estimates generated from real-world risk models. The analysis includes tests based on the empirical CDF (e.g., Kolmogorov–Smirnov; Cramér–Von Mises; and Anderson–Darling) as well as tests of dependence based on regression analysis of observed PITs.

Keywords

Beyond exceedance
Type
Chapter
Information
Validation of Risk Management Models for Financial Institutions
Theory and Practice
 , pp. 57 - 83
Publisher: Cambridge University Press
Print publication year: 2023

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References

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