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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

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.

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Chapter
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Publisher: Cambridge University Press
Print publication year: 2023

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