Multivariate Tests of Mean-Variance Efficiency and Spanning With a Large Number of Assets and Time-Varying Covariances |
| |
Authors: | Sermin Gungor Richard Luger |
| |
Affiliation: | 1. Funds Management and Banking Department, Bank of Canada, Ottawa, K1A 0G9, Ontario, Canada sgungor@bankofcanada.ca;2. Department of Finance, Insurance and Real Estate, Laval University, Quebec City, G1V 0A6, Quebec, Canada richard.luger@fsa.ulaval.ca |
| |
Abstract: | We develop a finite-sample procedure to test the mean-variance efficiency and spanning hypotheses, without imposing any parametric assumptions on the distribution of model disturbances. In so doing, we provide an exact distribution-free method to test uniform linear restrictions in multivariate linear regression models. The framework allows for unknown forms of nonnormalities as well as time-varying conditional variances and covariances among the model disturbances. We derive exact bounds on the null distribution of joint F statistics to deal with the presence of nuisance parameters, and we show how to implement the resulting generalized nonparametric bounds tests with Monte Carlo resampling techniques. In sharp contrast to the usual tests that are not even computable when the number of test assets is too large, the power of the proposed test procedure potentially increases along both the time and cross-sectional dimensions. |
| |
Keywords: | Exact distribution-free inference Monte Carlo bounds test Multi-beta asset pricing model Multivariate GARCH Multivariate linear regression |
|
|