A moments-based procedure for evaluating risk forecasting models
Kevin Dowd** Nottingham University Business School, Jubilee Campus, Nottingham, UK
This chapter examines the important problem of evaluating a risk forecasting model [e.g. a values-at-risk (VaR) model]. Its point of departure is the likelihood ratio (LR) test applied to data that have gone through Probability Integral Transform and Berkowitz transformations to become standard normal under the null hypothesis of model adequacy. However, the LR test is poor at detecting model inadequacy that manifests itself in the transformed data being skewed or fat-tailed. To remedy this problem, the chapter proposes a new procedure that combines tests of the predictions of the first four ...
Get The Analytics of Risk Model Validation now with O’Reilly online learning.
O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.