Multivariate tests of mean-variance efficiency and spanning with a large number of assets and time-varying covariances

Arbeitspapier

We develop a finite-sample procedure to test for mean-variance efficiency and spanning 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 non-normalities, and time-varying conditional variances and covariances among the model disturbances. We derive exact bounds on the null distribution of joint F statistics in order to deal with the presence of nuisance parameters, and we show how to implement the resulting generalized non-parametric bounds tests with Monte Carlo resampling techniques. In sharp contrast to the usual tests that are not computable when the number of test assets is too large, the power of the new test procedure potentially increases along both the time and cross-sectional dimensions.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Bank of Canada Working Paper ; No. 2013-16

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Hypothesis Testing: General ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Portfolio Choice; Investment Decisions ; Asset Pricing; Trading Volume; Bond Interest Rates
• Thema: Econometric and statistical methods ; Asset pricing ; Financial markets

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Gungor, Sermin ; Luger, Richard
• Ereignis: Veröffentlichung
• (wer): Bank of Canada
• (wo): Ottawa
• (wann): 2013

Weitere Informationen

• DOI: doi:10.34989/swp-2013-16
• Handle: http://hdl.handle.net/10419/80796
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ