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A fuzzy multifactor asset pricing model.

Alfred Mbairadjim Moussa1, Jules Sadefo Kamdem1

  • 1MRE EA 7491, University of Montpellier, UFR d'Economie Avenue Raymond DUGRAND - Site de Richter C.S. 79606, 34960 Montpellier Cedex 2, France.

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Summary

This study proposes a novel fuzzy approach for estimating multifactor asset pricing models, improving financial decision-making by accounting for non-normal asset returns and market microstructure noise.

Keywords:
Asset pricing theoryFuzzy linear regressionFuzzy setMonthly volatilityMultifactor modelWeak-BLUE estimator

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Area of Science:

  • Quantitative Finance
  • Financial Econometrics
  • Asset Pricing

Background:

  • Traditional asset pricing models often assume normal distribution of asset returns, which is frequently violated in empirical data.
  • Market microstructure noise and intra-period activity can introduce biases and complexities not fully captured by standard methods.
  • Existing uncertainty assessment techniques like confidence intervals rely on normality assumptions, limiting their applicability.

Purpose of the Study:

  • To introduce a new methodology for multifactor asset pricing model estimation using fuzzy random variables.
  • To address the limitations of normality assumptions in asset return distributions and incorporate market microstructure effects.
  • To provide an alternative approach for uncertainty assessment in financial modeling.

Main Methods:

  • Modeling monthly financial asset returns as fuzzy random variables.
  • Estimating the multifactor asset pricing model as a fuzzy linear model.
  • Applying fuzzy linear regression for uncertainty assessment, bypassing normality assumptions.

Main Results:

  • The fuzzy approach effectively incorporates biases from market microstructure noise and intra-period activity.
  • Empirical studies using Fama-French data demonstrate the method's effectiveness compared to Ordinary Least Squares (OLS).
  • The proposed method offers a robust alternative for uncertainty assessment when returns deviate from normal distributions.

Conclusions:

  • The fuzzy random variable approach offers a more realistic and effective method for multifactor asset pricing model estimation.
  • This technique enhances financial decision-making by providing a superior way to handle uncertainty and non-normal return distributions.
  • The study highlights the practical applicability and advantages of fuzzy logic in financial econometrics.