We investigate the predictive performance of various classes of value-at-risk (VaR) models in several dimensions-unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the conditional distribution function. By using the reality check test of White (2000), we compare the predictive power of alternative VaR models in terms of the empirical coverage probability and the predictive quantile loss for the stock markets of five Asian economies that suffered from the 1997-1998 financial crisis. The results based on these two criteria are largely compatible and indicate some empirical regularities of risk forecasts. The Riskmetrics model behaves reasonably well in tranquil periods, while some extreme value theory (EVT)-based models do better in the crisis period. Filtering often appears to be useful for some models, particularly for the EVT models, though it could be harmful for some other models. The CaViaR quantile regression models of Engle and Manganelli (2004) have shown some success in predicting the VaR risk measure for various periods, generally more stable than those that invert a distribution function. Overall, the forecasting performance of the VaR models considered varies over the three periods before, during and after the crisis. Copyright © 2006 John Wiley & Sons, Ltd.

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.

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We use a large non-student sample to test how distinct measures of risk-attitudes relate to each other, to demographic characteristics and to real-life risk taking in the financial domain. These measures, namely the Bomb Risk Elicitation Task (BRET), self-reported willingness to take risks in general, the choice in a hypothetical lottery, the score in the Domain Specific Risk-Taking (DOSPERT) scale, appear to be positively correlated and exhibit a certain degree of consistency. Furthermore, a subset is driven by similar demographic characteristics as they are related positively to gender and negatively to age. Using extensive data on the retirement portfolios of the participants during the years 2008-2014, we find that all of these measures except the BRET, are positively correlated with the riskiness of individual portfolios. We observe that the most significant correlation is observed for self-reported willingness to take risks in general.

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