Multivariate Tests of Mean-Variance Efficiency and Spanning With a Large Number of Assets and Time-Varying Covariances

Datos Bibliográficos

ID19417536
AutoresSermin Gungor (0000-0003-4941-9120, Bank of Canada), Richard Luger (0000-0002-6595-0029, Department of Finance, Insurance and Real Estate, Laval University, Quebec City, G1V 0A6, Quebec, Canada)
Año2016
Volumen34
Número2
Páginas161-175
Fecha de publicación2016-04-02
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaJournal of Business and Economic Statistics (JOURNAL)
Identificadores de la revistaISSN: 0735-0015 • E-ISSN: 1537-2707
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2015.1019510
OpenAlexW2255215021
IdiomaEN
Citas recibidas3
Referencias citadas48

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

Autoregressive conditional heteroskedasticity · Conditional variance · Econometrics · Estimator · Monte Carlo method · Multivariate statistics · Nonparametric statistics · Nuisance parameter · Parametric statistics · Resampling · Statistics · Advanced Statistical Methods and Models · Financial Risk and Volatility Modeling · Mathematics · Statistical Methods and Inference

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