This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models. Then, we...

The technique of using densities and conditional distributions to carry out consistent specification testing and model selection amongst multiple diffusion processes have received considerable attention from both financial theoreticians and empirical econometricians over the last two decades....

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This paper proposes and analyzes tests that can be used to compare the accuracy of alternative conditional density forecasts of a variable. The tests are also valid in the broader context of model selection based on out-of-sample predictive ability. We restrict attention to the case of density...

This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various authors including Christoffersen and...

In recent years, an impressive body or research on predictive accuracy testing and model comparison has been published in the econometrics discipline. Key contributions to this literature include the paper by Diebold and Mariano (DM: 1995) that sets the groundwork for much of the subsequent work...

Predictive regressions are widely used in empirical economics and finance to investigate the Granger causality test, linear rational expectations hypothesis test, and market efficiency hypothesis. This paper develops a new unified predictability test regardless of the properties of predictors....

We consider a particular concept of accuracy of predictions, and we develop a class of non-parametric, spectral density tests capable of deciding whether a given random variable can predict a time series. Under standard assumptions, we show that those tests are consistent, robust and admissible....

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap...

Conventional tests of present-value models over-reject the null of no predictability. In order to better account for the intrinsic probability of detecting predictive relations by chance alone, we develop a new nonparametric Monte Carlo testing method, which does not rely on distributional...