Corrigendum to "An augmented capital asset pricing model using new macroeconomic determinants" [Volume 6, Issue 10 (October 2020) e05185].

[This corrects the article DOI: 10.1016/j.heliyon.

Corrigendum to "The systematic risk estimation models: A different perspective" [Volume 6, Issue 2 (February 2020) e03371].

[This corrects the article DOI: 10.1016/j.heliyon.

Corrigendum to "An augmented capital asset pricing model using new macroeconomic determinants" [Heliyon 6 (10) October 2020 e05185].

[This corrects the article DOI: 10.1016/j.heliyon.

An augmented capital asset pricing model using new macroeconomic determinants.

Using the interview results of 26 experienced scholars, managers, and professional stock traders in conjunction with findings of recent studies in economics, we proposed an augmented asset pricing model using the macroeconomic determinants representing the macroeconomic state variables to explain the nexus between these risks and the U.S. stock returns.

Is estimating the Capital Asset Pricing Model using monthly and short-horizon data a good choice?

This research argued for estimating the Capital Asset Pricing Model (CAPM) using daily and medium-horizon data over monthly and short horizon-data. Using a Gibbs sample, the Bayesian framework via both parametric and non-parametric Bayes estimators, confidence interval approach, and six data sets (two daily, two weekly, and two monthly data) from a sample of 150 randomly selected S&P 500 stocks from 2007 - 2019, the empirical results showed that the CAPM using daily data yielded a statistically significant higher model fit and smaller Beta standard deviation, model error, and Alpha compared with monthly data. The CAPM using medium-horizon data yielded a statistically significant higher model fit, smaller Beta standard deviation and Alpha, and much less zeroed Betas compared with short-horizon data.

The systematic risk estimation models: A different perspective.

In practice, the capital asset pricing model (CAPM) using the parametric estimator is almost certainly being used to estimate a firm's systematic risk (beta) and cost of equity as in Eq. (1). However, the parametric estimators, even when data is normal, may not yield better performance compared with the non-parametric estimators when outliers existed.