Abstract #301575

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JSM 2003 Abstract #301575
Activity Number: 19
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: Business & Economics Statistics Section
Abstract - #301575
Title: Residual-Based Finite-Sample Misspecification Tests in Multivariate Regressions with Applications to Asset Pricing Models
Author(s): Lynda Khalaf*+ and Jean-Marie Dufour and Marie-Claude Beaulieu
Companies: Universite Laval and Universite De Montreal and Universite Laval
Address: Pavillion J-A-De Seve, Laval, PQ, G1K 7P4, Canada
Keywords: goodness-of-fit [GF] test ; exact test ; multivariate GARCH ; variance ratio test ; Monte Carlo test ; capital asset pricing model
Abstract:

We propose a general exact method for specification testing in multivariate regressions. We consider distributional goodness-of-fit [GF] tests, tests for serial correlation and multivariate GARCH. Our GF tests are based on comparing empirical multivariate skewness and kurtosis criteria to a simulation-based estimate of their expected value under the hypothesized distribution, with focus on the multivariate normal, the Student-t and stable distributions. In the Gaussian case, we first derive finite sample versions of the standard multivariate skewness and kurtosis tests. To do this, we exploit the Monte Carlo (MC) simulation-based test technique (Dufour 2002). The second category of tests we consider concerns departures from the i.i.d. errors hypothesis. Our procedures provide exact versions of those proposed in (Shanken 1990). Furthermore, our methodology deals, with a finite test perspective, with nonnormal errors. Since non-Gaussian based tests are not pivotal, we apply the "maximized MC" test method to control the test's level. The tests proposed are applied to an asset pricing model with observable risk-free rates.

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