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Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators
We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are e...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777388/ https://www.ncbi.nlm.nih.gov/pubmed/36554110 http://dx.doi.org/10.3390/e24121705 |
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author | Vigo-Pereira, Caio Laurini, Márcio |
author_facet | Vigo-Pereira, Caio Laurini, Márcio |
author_sort | Vigo-Pereira, Caio |
collection | PubMed |
description | We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are efficiently exploiting all the information available in the market. Estimators from the GEL family present some optimal statistical properties, such as robustness to misspecifications and better properties in finite samples. Unlike generalized method of moments (GMM) estimators, the bias for GEL estimators does not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. Due to these better properties in finite samples, our main hypothesis is that portfolio efficiency tests using GEL estimators may have better properties in terms of size, power, and robustness. Using Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. Extensive empirical analyses show the properties of the estimators for different sample sizes and portfolio types for two asset pricing models. |
format | Online Article Text |
id | pubmed-9777388 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97773882022-12-23 Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators Vigo-Pereira, Caio Laurini, Márcio Entropy (Basel) Article We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are efficiently exploiting all the information available in the market. Estimators from the GEL family present some optimal statistical properties, such as robustness to misspecifications and better properties in finite samples. Unlike generalized method of moments (GMM) estimators, the bias for GEL estimators does not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. Due to these better properties in finite samples, our main hypothesis is that portfolio efficiency tests using GEL estimators may have better properties in terms of size, power, and robustness. Using Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. Extensive empirical analyses show the properties of the estimators for different sample sizes and portfolio types for two asset pricing models. MDPI 2022-11-22 /pmc/articles/PMC9777388/ /pubmed/36554110 http://dx.doi.org/10.3390/e24121705 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Vigo-Pereira, Caio Laurini, Márcio Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators |
title | Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators |
title_full | Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators |
title_fullStr | Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators |
title_full_unstemmed | Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators |
title_short | Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators |
title_sort | portfolio efficiency tests with conditioning information—comparing gmm and gel estimators |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777388/ https://www.ncbi.nlm.nih.gov/pubmed/36554110 http://dx.doi.org/10.3390/e24121705 |
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