Comparing density forecast models

Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003; see Bao et al. (2004).

Tae-Hwy Lee, Yong Bao () and Burak Saltoğlu
Additional contact information
Tae-Hwy Lee: University of California, Riverside, California USA, Postal: University of California, Riverside, California USA
Burak Saltoğlu: Bosphorous University, Istanbul, Turkey, Postal: Bosphorous University, Istanbul, Turkey
Authors registered in the RePEc Author Service: Burak Saltoglu

Journal of Forecasting, 2007, vol. 26, issue 3, pages 203-225

Abstract: In this paper we discuss how to compare various (possibly misspecified) density forecast models using the Kullback-Leibler information criterion (KLIC) of a candidate density forecast model with respect to the true density. The KLIC differential between a pair of competing models is the (predictive) log-likelihood ratio (LR) between the two models. Even though the true density is unknown, using the LR statistic amounts to comparing models with the KLIC as a loss function and thus enables us to assess which density forecast model can approximate the true density more closely. We also discuss how this KLIC is related to the KLIC based on the probability integral transform (PIT) in the framework of Diebold et al. (1998). While they are asymptotically equivalent, the PIT-based KLIC is best suited for evaluating the adequacy of each density forecast model and the original KLIC is best suited for comparing competing models. In an empirical study with the S&P500 and NASDAQ daily return series, we find strong evidence for rejecting the normal-GARCH benchmark model, in favor of the models that can capture skewness in the conditional distribution and asymmetry and long memory in the conditional variance.  Copyright © 2007 John Wiley & Sons, Ltd.

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