Estimation of betas of stocks with low liquidity

Serra, Ricardo Goulart; Martelanc, Roy

doi:10.15728/bbr.2013.10.1.3

Cited by 4 publications

(9 citation statements)

References 16 publications

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“…The empirical results showed that the robust LTS and QR estimators outperformed the OLS in Beta estimation in terms of efficiency. This finding was consistent with other studies (e.g., Bowie and Bradfield, 1998 ; Chan and Lakonishok, 1992 ; Serra and Martelanc, 2013 ; Shalit and Yitzhaki, 2002 ). However, this research employed only one data type, horizon, and criterion in comparison.…”

Section: Literature Reviewsupporting

confidence: 94%

“… Serra and Martelanc (2013) conducted a similar study of Martin and Simin (2003) , a study of Beta estimation for small stocks whose shares are not traded every day. In this study, the betas are estimated by three different methods: 1) repetition of the last quotation (RUC), 2) trade-to-trade (TT), and 3) Scholes-Williams' adjustment (SW).…”

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%

“…However, the outliers – a very common problem in real stock returns ( Phuoc, 2018 ) may seriously affect the performance of the parametric estimators compared with the non-parametric estimators as shown in some recent studies ( Baissa and Rainey, 2018 ; Cenesizoglu et al., 2019 ; McDonald and Nelson, 1989 ; Reeves and Wu, 2013 ). Besides, the daily data may yield a more efficient Beta estimate compared with monthly data as suggested in some recent studies ( Phuoc, 2018 ; Phuoc et al., 2018 ; Serra and Martelanc, 2013 ).…”

Section: Introductionmentioning

confidence: 95%

“…According to our research, we found one empirical study of Beta estimation and testing in the literature that conducted Monte Carlo simulations and simultaneously employed different data types and horizon but only one estimator (e.g, Valkanov, 2003 ). Some other studies used only different data types and estimators (e.g., Bian et al., 2013 ; Phuoc, 2018 ; Phuoc et al., 2018 ; Serra and Martelanc, 2013 ; Wong and Bian, 2000 ) and different horizon data (e.g., Ang and Bekaert, 2007 ; Kothari and Warner, 2001 ; Levy and Schwartz, 1997 ; Theobald, 1981 ). In addition, these studies did not employ the confidence interval approach and applied only one or two evaluation criteria.…”

Section: Introductionmentioning

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

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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.

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Section: Literature Reviewsupporting

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

Section: Data Approach and Estimator And Evaluation Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

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

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The systematic risk estimation models: A different perspective

Phuoc

Pham

2020

Heliyon

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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.

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The Performance of Option Pricing Models Estimating the Marketability Discount in a Pre-IPO Real-World Data Setting: Evidence from Europe

Grbenic

2022

Journal of Business Valuation and Economic Loss Analysis

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Valuation analysts adjust prices of private firm’s stock downward from their fully marketable counterparts, reflecting the private firms’ lower level of marketability. This discount for lack of marketability can be substantial in magnitude. This study examines the performance (according to bias and accuracy employing estimation error methodology) of seven popular option pricing models in generating discount estimates to coincide with empirically observed discount benchmarks (based on the pre-IPO methodology) in European Union member countries over the period 2004 until 2018. The results allow for the general conclusion that some option pricing models are superior in most settings, coinciding with their individual benefits and deficiencies. The detailed analysis indicates that (i) the superiority of these option pricing models holds for a wide range of periods of assumed restricted marketability, (ii) segmenting discount benchmarks according to their size improves the performance of the option pricing models, (iii) segmenting discount benchmarks according to both, the underlying volatility of stock returns and dividend yields, does not improve the performance of the option pricing models, and (iv) IPO underperformance has no material impact on relative option pricing model’s performance.

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Estimation of betas of stocks with low liquidity

Cited by 4 publications

References 16 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

The Performance of Option Pricing Models Estimating the Marketability Discount in a Pre-IPO Real-World Data Setting: Evidence from Europe

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