“KLICing” there and back again: Portfolio selection using the empirical likelihood divergence and Hellinger distance

Haley, M. Ryan; McGee, M. Kevin

doi:10.1016/j.jempfin.2010.12.002

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…Empirical likelihood methods have been incorporated into this field over time, and a few papers explored this family of estimators focusing on this audience [ 10 , 11 ]. This family of estimators was employed in applications in specific contexts in asset pricing, such as for valuing risk and option pricing [ 12 , 13 , 14 , 15 , 16 ], and specifically in portfolio theory [ 17 , 18 , 19 ]. On the other hand, to address some of the issues present in the standard methods of estimation in the portfolio theory literature, Bayesian approaches were also introduced [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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

Pereira

Laurini

2022

Entropy

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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.

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Section: Introductionmentioning

confidence: 99%

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

Pereira

Laurini

2022

Entropy

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“…Darsinos and Satchell (2004)). We refer the reader to Stutzer (2000Stutzer ( , 2003, Haley and McGee (2006), Haley and Whiteman (2008) and Haley and McGee (2011) for an alternative definition of tilting in a portfolio context, namely modifying the probability return distribution.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Portfolio Tilting to Harvest Higher Moment Gains

Boudt

Cornilly

Holle³

et al. 2019

SSRN Journal

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Many financial portfolios are not mean-variance-skewness-kurtosis efficient. We recommend tilting these portfolios in a direction that increases their estimated mean and third central moment and decreases their variance and fourth central moment. The advantages of tilting come at the cost of deviation from the initial optimality criterion. In this paper, we show the usefulness of portfolio tilting applied to the equally-weighted, equal-risk-contribution and maximum diversification portfolios in a UCITS-compliant asset allocation setting.

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K-fold cross validation performance comparisons of six naive portfolio selection rules: how naive can you be and still have successful out-of-sample portfolio performance?

Haley

2017

Ann Finance

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“KLICing” there and back again: Portfolio selection using the empirical likelihood divergence and Hellinger distance

Cited by 10 publications

References 25 publications

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

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

Algorithmic Portfolio Tilting to Harvest Higher Moment Gains

K-fold cross validation performance comparisons of six naive portfolio selection rules: how naive can you be and still have successful out-of-sample portfolio performance?

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