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The systematic risk estimation models: A different perspective.

Le Tan Phuoc1, Chinh Duc Pham2

  • 1Becamex Business School - Eastern International University, Viet Nam.

Heliyon
|February 20, 2020
PubMed
Summary
This summary is machine-generated.

The non-parametric Bayes estimator outperforms the traditional parametric approach in the Capital Asset Pricing Model (CAPM). This method offers superior accuracy for estimating systematic risk (beta) and cost of equity, especially with outlier data.

Keywords:
Asset pricingBayes estimatorsBusinessCAPMCorporate financeCost of equityEconomicsFinancial marketInternational financePricingRisk managementStatisticsSystematic risk

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Area of Science:

  • Finance
  • Econometrics
  • Statistical Modeling

Background:

  • The Capital Asset Pricing Model (CAPM) commonly uses parametric estimators for beta and cost of equity.
  • Parametric estimators may perform poorly with data outliers, unlike non-parametric alternatives.

Purpose of the Study:

  • To evaluate the efficacy of a non-parametric Bayes estimator within the CAPM framework.
  • To compare the performance of non-parametric versus parametric Bayes estimators for asset pricing.

Main Methods:

  • Applied Bayesian inference to S&P 500 stock data (07/2007-05/2019).
  • Utilized evaluation criteria including AIC/DIC, model variance, fit, error, alpha, and beta standard deviation.

Main Results:

  • The non-parametric Bayes estimator produced fewer zeroed betas and smaller alpha values.
  • Statistically significant improvements were observed in AIC/DIC, model variance, and beta standard deviation with the non-parametric approach.
  • The non-parametric Bayes estimator demonstrated a higher model fit compared to the parametric estimator.

Conclusions:

  • The non-parametric Bayes estimator is superior to the parametric Bayes estimator for CAPM applications.
  • Recommends employing non-parametric estimators in asset pricing for enhanced accuracy and reliability.