Robust Estimation of Beta Coefficients: Evidence from a Small Stock Market

Bowie, D C; Bradfield, D. J.

doi:10.1111/1468-5957.00196

Cited by 20 publications

(12 citation statements)

References 28 publications

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“…The empirical results showed that in most cases, the Gini estimator was more efficient and consistent than the OLS estimator. This finding was consistent with other studies ( Chan and Lakonishok, 1992 ; Bowie and Bradfield, 1998 ). However, this research employed only one data type, horizon data, and criterion in comparison.…”

Section: Literature Reviewsupporting

confidence: 94%

“…This TT method worked best in the case of daily periodicity because it produced the smallest beta standard deviation estimates compared with weekly and monthly periodicity. This finding was consistent with other studies (e.g., Chan and Lakonishok, 1992 ; Bowie and Bradfield, 1998 ; Shalit and Yitzhaki, 2002 ). However, this research employed only one horizon data and criterion in comparison.…”

Section: Literature Reviewsupporting

confidence: 94%

“… Bowie and Bradfield (1998) studied the Beta estimation of small stock markets and small-cap stocks. In the small markets and small-cap stocks, the trading volume and trade frequencies are often low.…”

Section: Literature Reviewmentioning

confidence: 99%

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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 Reviewsupporting

confidence: 94%

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

Pham

Phuoc

2020

Heliyon

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“…aside the performance of the methods with respect to alpha estimation. Bowie and Bradfield (1998) extended the work of Chan and Lakonishok (1992) by assessing the relative performance of a wider range of robust estimators when applied for beta estimation of securities listed on the Johannesburg Stock Exchange. Their results, based on jackknife measures of efficiency, indicate that robust methods are less sensitive than OLS to model misspecification -such as extreme excess market returns -, and that the superior efficiency of the robust estimators was caused by non-normality in the distribution of residuals.…”

Section: Robust Estimation Of Asset Pricing Modelsmentioning

confidence: 99%

Identifying outliers in asset pricing data with a new weighted forward search estimator

Aronne

Grossi

Bressan

2020

Rev. contab. finanç.

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The purpose of this work is to present the Weighted Forward Search (FSW) method for the detection of outliers in asset pricing data. This new estimator, which is based on an algorithm that downweights the most anomalous observations of the dataset, is tested using both simulated and empirical asset pricing data. The impact of outliers on the estimation of asset pricing models is assessed under different scenarios, and the results are evaluated with associated statistical tests based on this new approach. Our proposal generates an alternative procedure for robust estimation of portfolio betas, allowing for the comparison between concurrent asset pricing models. The algorithm, which is both efficient and robust to outliers, is used to provide robust estimates of the models’ parameters in a comparison with traditional econometric estimation methods usually used in the literature. In particular, the precision of the alphas is highly increased when the Forward Search (FS) method is used. We use Monte Carlo simulations, and also the well-known dataset of equity factor returns provided by Prof. Kenneth French, consisting of the 25 Fama-French portfolios on the United States of America equity market using single and three-factor models, on monthly and annual basis. Our results indicate that the marginal rejection of the Fama-French three-factor model is influenced by the presence of outliers in the portfolios, when using monthly returns. In annual data, the use of robust methods increases the rejection level of null alphas in the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, with more efficient estimates in the absence of outliers and consistent alphas when outliers are present.

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“…Luego, un análisis comparativo de los diferentes estimadores robustos fue introducido por Bowie & Bradfield (1998), concluyendo que el estimador de influencia limitada de la familia de estimadores M es el que presenta una mayor eficiencia en la estimación, en comparación con el estimador MCO. Los autores también indican que las diferencias sistemáticas entre MCO y los estimadores robustos, motivan a los investigadores a profundizar en la relación que existe entre el retorno de las acciones y el retorno del mercado cuando se presentan condiciones extremas derivadas de la baja liquidez de algunos activos, tal como se presenta en mercados financieros de poca profundidad y desarrollo.…”

Section: Relevancia De La Estadística Robusta En Finanzasunclassified

Estimación robusta de betas y el ratio de cobertura sobre futuros de índices bursátiles en el Mercado Integrado Latinoamericano (MILA)

Betancur

Díaz²,

Fernández³

2017

ecos.econ.

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ResumenEl presente trabajo tiene por objeto estudiar el efecto que ejercen los datos atípicos en el parámetro beta de acciones pertenecientes al Mercado Integrado Latinoamericano (MILA), estimado por dos diferentes métodos: mínimos cuadrados ordinarios (MCO) y método robusto MM (RMM). Adicionalmente, para ilustrar la relevancia empírica de las betas calculadas, se efectuó una aplicación de cobertura con futuros sobre índices. Los resultados indican que las estimaciones realizadas por el método RMM, ofrecen un mejor ajuste y una mayor eficiencia de la cobertura cuando existe presencia de datos atípicos en la ventana de estimación de la beta. AbstractThis paper examines the effect exerted by outliers in the equity betas in the Integrated Latin American Market (MILA), estimated by two different methods: ordinary least squares (OLS) and robust estimation (RMM). To illustrate the empirical relevance of the estimated betas, we evaluate the hedging ratio using stock index futures. The results indicate that the estimates made by the RMM method provide a better fit and increase the efficiency of a hedging strategy when there are outliers in the estimation window of beta.Palabras clave: estimación de beta; método robusto MM (RMM); método mínimos cuadrados ordinarios (MCO); cobertura con futuros sobre índices MILA Keywords: estimation of beta; robust statistics MM (RMM); ordinary least squares (OLS); hedging ratio with stock MILA market index futures JEL Classifications: G12, G17, C14, C18Estimación robusta de betas y el ratio de cobertura sobre futuros de índices bursátiles en el Mercado Integrado Latinoamericano (MILA) Específicamente, al evaluar los rendimientos históricos de los mercados financieros del MILA, se puede validar lo indicado anteriormente, encontrando tendencias marcadas de sesgo y colas pesadas en sus distribuciones. En consecuencia, el manejo estadístico de los datos históricos disponibles del MILA requiere de enfoques complementarios con el fin de alcanzar valores adecuados para la estimación de betas.Al respecto, algunos practicantes de las finanzas han optado por emplear diversos métodos para eliminar los efectos de los datos extremos que inducen distorsiones, tales como la winsorización, entre otros. Sin embargo, aunque estos métodos permiten mitigar el problema de estimación, no logran proteger el modelo contra distorsiones en la estimación de la beta por MCO.Teniendo en cuenta estas limitaciones, en las últimas décadas se incrementó el interés de los académicos por encontrar métodos de estimación resistentes a la presencia de los valores extremos comunes en los mercados financieros, que arrojen parámetros de betas confiables bajo distribuciones no normales, y que conserven su eficiencia aun cuando la distribución de los retornos sea normal.De aquí que, en la presente investigación, se propone el uso de un método alternativo y complementario al MCO fundamentado en la estadística robusta para la estimación de betas, considerando que sus estimadores conservan adecuadas propiedades de eficiencia y estab...

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Robust Estimation of Beta Coefficients: Evidence from a Small Stock Market

Cited by 20 publications

References 28 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?

Identifying outliers in asset pricing data with a new weighted forward search estimator

Estimación robusta de betas y el ratio de cobertura sobre futuros de índices bursátiles en el Mercado Integrado Latinoamericano (MILA)

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