EconPapers    
Economics at your fingertips  
 

Portfolio Efficiency Tests with Conditioning Information - Comparing GMM and GEL Estimators

Caio Vigo Pereira and Márcio Laurini ()

No 202014, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: We evaluate the use of Generalized Empirical Likelihood (GEL) estimators in port- folio efficiency tests for asset pricing models in the presence of conditional information. Estimators from GEL family present some optimal statistical properties, such as robustness to misspecification and better properties in finite samples. Unlike GMM, the bias for GEL estimators do not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. By means of Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. An extensive empirical analysis shows the properties of the estimators for different sample sizes and portfolios types for two asset pricing models.

Keywords: Portfolio; Efficiency.; Conditional; Information.; Efficiency; Tests.; GEL.; GMM (search for similar items in EconPapers)
JEL-codes: C12 C13 C58 G11 G12 (search for similar items in EconPapers)
Date: 2020-09, Revised 2020-09
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www2.ku.edu/~kuwpaper/2020Papers/202014.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:202014

Access Statistics for this paper

More papers in WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Professor Zongwu Cai ().

 
Page updated 2020-10-24
Handle: RePEc:kan:wpaper:202014