On the Short-Term Stationarity of Beta Coefficients

Levy, Robert A.

doi:10.2469/faj.v27.n6.55

Cited by 196 publications

(124 citation statements)

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“…Blume finds 0.62(0.67) and 0.91(0.93) mean correlation (rank correlation) for 1-and 10-sized portfolios using 84-month estimation periods respectively. Furthermore, based on 52-week estimation periods, Levy ( 1971) records 0.44 and 0.82 mean correlation for 1-and 10-sized portfolios on the same exchange. On the UK market between 1955and 1979, Dimson and Marsh (1983 used value-weighted market index, monthly returns interval measurement and sixty-month estimation periods to obtain an average correlation of 0.56 and 0.91 for 1-or 10-sized portfolios respectively.…”

Section: Beta Stabilitymentioning

confidence: 99%

The Behavior of Beta in the 19th Century

Mensah¹

2013

AFR

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This paper uses completely new data to study the variations in beta when it deviates from the constancy assumption presumed by the market model. The concentration of the literature on beta is based on post 1926 data. This makes the 19 th century Brussels Stock Exchange (BSE) data a very good out-sample dataset to test beta variations. Various models proposed in the literature to capture the variations in beta were studied. Blume's correlation techniques reveal that beta is not stable at the individual stocks level and that the stability can be fairly improved by portfolio formations. Using root mean square error (RMSE) criterion, it was shown that the market model betas are weak in predicting future betas. The predictability can be improved by adjusting betas with the Blume and Vasicek mean reversion techniques. Further results from this study reveal that few stocks have lead or lag relationship with the market index. Small sized stocks were detected to be more prone to outliers. In effect betas in the 19 th century exhibits similar pattern as betas in the post 1926 era.

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Section: Beta Stabilitymentioning

confidence: 99%

The Behavior of Beta in the 19th Century

Mensah¹

2013

AFR

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“…También señaló que los coeficientes betas históricos para los portafolios más grandes son notablemente precisos en la predicción de futuras betas de un portafolio. Levy (1971) encontró que la evaluación de los riesgos futuros es muy fiable para grandes portafolios, algo menos fiable para los portafolios más pequeños y muy poco fiable para valores individuales. También encontró que la previsibilidad mejora materialmente cuando el periodo del pronósti-co se alarga, con una mayor mejoría al pasar de 13 semanas a 26 semanas, que cuando se pasa de 26 semanas a 52 semanas.…”

Section: Revisión De Literaturaunclassified

Hacia Un Modelo De Valuación De Activos De Capital Para México: Análisis De Activos Individuales Con Coeficientes Variantes en El Tiempo

Herrera¹,

Herrera²

2017

PE-IPN

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Analizando el comportamiento de los coeficientes beta variantes en el tiem- po, estimados mediante el filtro de Kalman para una muestra de activos mexicanos que cotizan en la bolsa mexicana de valores, se observa que un modelo con betas variantes en el cual los rendimientos de los activos depen- den de un factor local y uno internacional permiten estimar las primas de riesgo de estos activos. Dado la dependencia que exhiben todos los activos analizados en relación con un factor doméstico, la evidencia que se muestra en este trabajo da soporte a la hipótesis de que el mercado mexicano no está integrado completamente al internacional. Es decir, en nuestro análi- sis se encuentra que las características del riesgo sistemático de los activos mexicanos estudiados son congruentes con la valuación de activos en un contexto de mercado parcialmente segmentado. Como consecuencia, los resultados aquí mostrados sugieren que es adecuado el uso de un modelo internacional de valuación de activos (ICAPM), con un factor internacional y un factor doméstico, y coeficientes beta variantes en el tiempo en lugar de modelos con un solo factor nacional.

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“…Thus, bringing the beta coefficient to zero allows to achieve the effect, in which the variability of the portfolio is not related to market volatility. (Blume, 1975), (Levy, 1971) According with the latter statement, portfolios whose beta is zero are referred as market neutral portfolios. The simplest model of market-neutral strategy should not assume an active portfolio management.…”

Section: Methodsmentioning

confidence: 99%

Market Risk Neutral Strategies: Modeling and Algorithmization

Gloukhov¹,

Ilin²,

Koposov³

et al. 2014

ASS

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Traditional "buy & hold" strategies in the stock market are effective within the established trend direction. Under condition of the absence of directional dynamics of stock market indicators the efficiency of such strategies is low. Traditional strategies do not entirely exclude the market risk and the risk of an incorrect choice of the moment to open or close positions, and require a lot of experience with financial instruments. This causes the need of the development of investment alternatives that can provide an adequate rate of return on investment regardless of the stock market indicators movement. The development of automated trading systems (trading robots) in the 2010 th is supposed to make market risk neutral strategies available to be used by the wide range of investors.The aim of this paper is to study and develop the market risk neutral strategies to manage the investment capital on the stock market. As a result of the research the algorithm of the pairs trading investment strategy was developed, which is effective regardless of the direction of market assets movement. Long-term pairs trading allow to take into account fundamental factors, as well as to hedge market risks. Such an algorithm is able to provide an adequate level of return on investment.

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On the Short-Term Stationarity of Beta Coefficients

Cited by 196 publications

References 0 publications

The Behavior of Beta in the 19th Century

The Behavior of Beta in the 19th Century

Hacia Un Modelo De Valuación De Activos De Capital Para México: Análisis De Activos Individuales Con Coeficientes Variantes en El Tiempo

Market Risk Neutral Strategies: Modeling and Algorithmization

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