Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/167811 
Autor:innen: 
Erscheinungsjahr: 
2016
Quellenangabe: 
[Journal:] International Journal of Financial Studies [ISSN:] 2227-7072 [Volume:] 4 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2016 [Pages:] 1-13
Verlag: 
MDPI, Basel
Zusammenfassung: 
The study adds an empirical outlook on the predicting power of using data from the future to predict future returns. The crux of the traditional Capital Asset Pricing Model (CAPM) methodology is using historical data in the calculation of the beta coefficient. This study instead uses a battery of Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the beta formula. The covariance of the portfolio and market returns are assumed to remain constant in the time-varying beta calculations. The data spans from 3 January 2005 to 29 December 2014. One ten-year, two five-year, and three three-year sample periods were used, for robustness, with ten different portfolios. Out of sample forecasts, mean absolute error (MAE) and mean squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural Network, and the standard market ex-post model. Find that the time-varying MGARCH and SGARCH beta performed better with out-of-sample testing than the other ex-ante models. Although the simplest approach, constant ex-post beta, performed as well or better within this empirical study.
Schlagwörter: 
CAPM
empirical
GARCH
ex-ante beta
artificial neural network
time-varying beta
JEL: 
B23
C22
C52
C53
G12
G15
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