We propose exact simulation-based procedures for: (i) testing mean-variance efficiency when the zero-beta rate is unknown, and (ii) building confidence intervals for the zero-beta rate. On observing that this parameter may be weakly identified, we propose LR-type statistics as well as heteroskedascity and autocorrelation corrected (HAC) Wald-type procedures, which are robust to weak identification and allow for non-Gaussian distributions including parametric GARCH structures. In particular, we propose confidence sets for the zero-beta rate based on “inverting” exact tests for this parameter; these sets provide a multivariate extension of Fieller's technique for inference on ratios. The exact distribution of LR-type statistics for testing efficiency is studied under both the null and the alternative hypotheses. The relevant nuisance parameter structure is established and finite-sample bound procedures are proposed, which extend and improve available Gaussianspecific bounds. Furthermore, we study the invariance to portfolio repacking property for tests and confidence sets proposed. The statistical properties of available and proposed methods are analyzed via aMonte Carlo study. Empirical results on NYSE returns show that exact confidence sets are very different from the asymptotic ones, and allowing for non-Gaussian distributions affects inference results. Simulation and empirical results suggest that LR-type statistics - with p-values corrected using the Maximized Monte Carlo test method - are generally preferable to their Wald-HAC counterparts from the viewpoints of size control and power.

Voir le document

Dernières publications

2017MO-03 MO
Méthodes avancées d’évaluation d’investissements / Advanced Methods of Investment Evaluation - Tome 2
Marcel Boyer
Voir le document

2017MO-02 MO
Méthodes avancées d’évaluation d’investissements / Advanced Methods of Investment Evaluation - Tome 1
Marcel Boyer
Voir le document

2017s-08 CS
An experimental investigation of rating-market regulation
Claudia Keser, Asri Özgümüs, Emmanuel Peterlé et Martin Schmidt
Voir le document

2017s-07 CS
Statistical tests of the demand for insurance: an “all or nothing” decision
Anne Corcos, François Pannequin et Claude Montmarquette
Voir le document

2017RP-02 RP
Politiques favorables à l’innovation en santé
Nadia Benomar, Joanne Castonguay, Marie-Hélène Jobin et François Lespérance
Voir le document


Centre interuniversitaire de recherche en analyse des organisations
1130 rue Sherbrooke Ouest, suite 1400
Montréal, Québec (Canada) H3A 2M8
(514) 985-4000
(514) 985-4039
reception@cirano.qc.ca

© 2017 CIRANO. Tous droits réservés.



Partenaire de :