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Summary
This summary is machine-generated.

This study introduces robust estimators for the single index model, enhancing portfolio optimization. The new method improves accuracy by addressing outliers in financial data analysis.

Keywords:
minimum divergence methodsrobustnesssingle index model

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Area of Science:

  • Quantitative Finance
  • Econometrics

Background:

  • The single index model simplifies portfolio analysis for large asset numbers by reducing covariance parameters.
  • Traditional maximum likelihood estimators are sensitive to outliers, limiting their reliability in dynamic market conditions.

Purpose of the Study:

  • To develop robust estimators for the single index model.
  • To construct new robust optimal portfolios using these estimators.
  • To demonstrate the practical benefits of the proposed method with real financial data.

Main Methods:

  • Definition of minimum pseudodistance estimators for single index model parameters.
  • Construction of robust optimal portfolios based on these estimators.
  • Theoretical analysis of estimator properties including consistency, asymptotic normality, equivariance, and robustness.

Main Results:

  • The proposed minimum pseudodistance estimators offer robustness against outliers.
  • New robust optimal portfolios are constructed, outperforming traditional methods in the presence of data anomalies.
  • Theoretical properties of the estimators are rigorously proven.

Conclusions:

  • Minimum pseudodistance estimators provide a robust alternative to maximum likelihood estimators for the single index model.
  • The developed portfolio optimization method is effective for real financial data, especially when outliers are present.
  • This research contributes to more reliable portfolio management strategies in volatile markets.