Selected Robust Methods for Camp Model Estimation

Trzpiot, Grażyna

doi:10.2478/v10031-012-0032-7

Cited by 2 publications

(9 citation statements)

References 13 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also found that the robust MM and LTS estimators and daily returns data outperformed the OLS estimator in terms of efficiency. This finding was then validated by the Jackknife resampling method and consistent with other studies (e.g., Bowie and Bradfield, 1998 ; Chan and Lakonishok, 1992 ; Serra and Martelanc, 2013 ; Shalit and Yitzhaki, 2002 ; Trzpiot, 2013 ). However, this research employed only one horizon, five-year data.…”

Section: Literature Reviewsupporting

confidence: 86%

“…A similar study, Phuoc (2018) was conducted to estimate the Jensen's Alpha using the CAPM using both daily and monthly data, five-year horizon data, and the OLS, MM, parametric Bayes estimators. The author found the daily data yielded a more efficient Alpha estimate than monthly data using all three estimators and in line with another study ( Trzpiot, 2013 ). More importantly, this finding was checked with and validated by Jackknife resampling results.…”

Section: Literature Reviewsupporting

confidence: 86%

“… Trzpiot (2013) conducted a study of both Jensen's Alpha and Beta estimation of stocks listed in the WIG20 index for July 2011–August 2012 using daily data. In this study, Trzpiot proposed using the robust least trimmed square (LTS) and quantile regression (QR) estimators instead of the OLS estimator due to the serious effects of outliers in the OLS estimator.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, the data that yielded less zeroed and stable/efficient Beta estimates would be a preferred one. Some recent studies applied this criterion in the work (e.g., Bowie and Bradfield, 1998 ; Chan and Lakonishok, 1992 ; Phuoc, 2018 ; Phuoc et al., 2018 ; Phuoc and Pham, 2020 ; Serra and Martelanc, 2013 ; Shalit and Yitzhaki, 2002 ; Trzpiot, 2013 ; Valkanov, 2003 ).…”

Section: Data Approach and Estimator And Evaluation Criteriamentioning

confidence: 99%

“…The third evaluation criterion was the model fit using the Bayesian r-squared (R2B) since it measured the explanatory power of the model. Also, some studies (e.g., Buse, 1973 ; Lee, 1991 ; Phuoc and Pham, 2020 ; Trzpiot, 2013 ) applied this criterion in the work. The data that yielded higher R2B would be a preferred one.…”

Section: Data Approach and Estimator And Evaluation Criteriamentioning

confidence: 99%

See 4 more Smart Citations

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

Pham

Phuoc

2020

Heliyon

View full text Add to dashboard Cite

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. These findings show 1) daily data is more reliable and efficient, has higher forecasting power, and fits better with the assumption of market efficiency compared with monthly data. 2) Medium-horizon data is more reliable and efficient, has more explanatory power, and fits better with the assumption of market efficiency compared with monthly data. Therefore, these findings challenge the common practices of using monthly (quarterly/annually) and short-horizon data among the practitioners and researchers in asset pricing work.

show abstract

Section: Literature Reviewsupporting

confidence: 86%

Section: Literature Reviewsupporting

confidence: 86%

Section: Literature Reviewmentioning

confidence: 99%

Section: Data Approach and Estimator And Evaluation Criteriamentioning

confidence: 99%

Section: Data Approach and Estimator And Evaluation Criteriamentioning

confidence: 99%

See 3 more Smart Citations

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

Pham

Phuoc

2020

Heliyon

View full text Add to dashboard Cite

show abstract

The systematic risk estimation models: A different perspective

Phuoc

Pham

2020

Heliyon

View full text Add to dashboard Cite

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. This research argued for the non-parametric Bayes estimator to be employed in the CAPM by applying both advance and basic evaluation criteria such as hypotheses/confidence intervals of the AIC/DIC, model variance, fit, and error, alpha, and beta and its standard deviation. Using all the S&P 500 stocks having monthly data from 07/2007-05/2019 (450 stocks) and the Bayesian inference, we showed the non-parametric Bayes estimator yielded less number of zeroed betas and smaller alpha compared with the parametric Bayes estimator. More importantly, this non-parametric Bayes yielded the statistically significantly smaller AIC/DIC, model variance, and beta standard deviation and higher model fit compared with the parametric Bayes estimator. These findings indicate the CAPM using the non-parametric Bayes estimator is superior compared with the parametric Bayes estimator, a contrast of common practice. Hence, the non-parametric estimator is recommended to be employed in asset pricing work.

show abstract

Selected Robust Methods for Camp Model Estimation

Cited by 2 publications

References 13 publications

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

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

The systematic risk estimation models: A different perspective

Contact Info

Product

Resources

About