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

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Generalized empirical likelihood (GEL) estimators enhance portfolio efficiency tests by offering superior robustness and finite sample properties compared to traditional methods. These estimators improve performance, especially with contaminated data, for asset pricing models.

Keywords:
GELGMMconditional informationefficiency testsportfolio efficiency

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Area of Science:

  • Econometrics
  • Financial Modeling
  • Statistical Inference

Background:

  • Conditional information is crucial for efficient portfolio management, enabling asset allocations to leverage all available market data.
  • Generalized Empirical Likelihood (GEL) estimators offer statistical advantages, including robustness to model misspecification and improved finite sample performance.
  • Traditional Generalized Method of Moments (GMM) estimators face challenges with increasing bias as more moment conditions are added, a common issue in conditional efficiency analysis.

Purpose of the Study:

  • To evaluate the efficacy of Generalized Empirical Likelihood (GEL) estimators in portfolio efficiency tests within asset pricing models.
  • To assess the performance of GEL estimators concerning size, power, and robustness, particularly when utilizing conditional information.
  • To compare GEL estimators against traditional methods like GMM in the context of conditional efficiency analysis.

Main Methods:

  • Utilizing Monte Carlo simulations to rigorously test the performance of GEL estimators under various conditions.
  • Conducting extensive empirical analyses across different sample sizes and portfolio types for two prominent asset pricing models.
  • Evaluating the impact of data contaminations, including heavy tails and outliers, on the performance of GEL estimators.

Main Results:

  • GEL estimators demonstrate superior performance in portfolio efficiency tests, especially when dealing with data contaminated by heavy tails and outliers.
  • The bias of GEL estimators does not escalate with the number of moment conditions, unlike GMM estimators, making them more suitable for conditional efficiency analysis.
  • Monte Carlo experiments confirm that GEL estimators exhibit better size, power, and robustness properties in finite samples.

Conclusions:

  • Generalized Empirical Likelihood (GEL) estimators provide a more robust and reliable approach for portfolio efficiency tests in asset pricing, particularly with conditional information.
  • The findings suggest that GEL estimators are advantageous in practical portfolio management scenarios where data quality may be compromised.
  • The study advocates for the adoption of GEL estimators in financial econometrics for enhanced accuracy and reliability in asset allocation and performance evaluation.