In this paper, we extend the 5-factor model in Fama and French (2015) with the non-Normal errors distribution of SSAEPD (Standardized Standard Asymmetric Exponential Power Distribution) in Zhu and Zinde-Walsh (2009) and the GARCHtype volatility. The focus is on finding out whether our new model can outperform the original Fama-French 5-factor model. We use Fama-French 25 value-weighted portfolios to conduct our research. The MLE is used to estimate the parameters. The LR test and KS test are used for model diagnostics. Models are compared by AIC. Empirical results show that with GARCH-type volatilities and non-normal errors, the Fama-French 5 factors are still alive. Our new model can successfully capture the skewness, fat-tailness and asymmetric kurtosis in the data and has better in-sample fit than the 5-factors model in Fama and French (2015). Our study provides an update to existing asset pricing literature and reference for investors.
In this paper, we empirically test a new model with the data of US services sector, which is an extension of the 5-factor model in Fama and French (2015) [1]. 3 types of 5 factors (Global, North American and US) are compared. Empirical results show the Fama-French 5 factors are still alive! The new model has better in-sample fit than the 5-factor model in Fama and French (2015).
The GIL covered voltage levels from 110 kV to 1000 kV. For Yellow River Laxiwa Hydropower Station, it's first 800 kV power plant in China. It's located at altitude of 2300 -2500 meters. In high voltage and high altitude areas, normal XLPE cable was limited to be used. This paper introduces particular issues of 800kV GIL in construction, design, manufacturing, erection and commissioning. And gives special recommendations for GIL applied in high fall vertical shaft and high altitude region.
In this paper, a new model is proposed to empirically test the Capital Asset Pricing Theory. This model is based on the EGARCH-type volatilities in Nelson (1991) and the non-Normal errors of SSAEPD in Zhu and Zinde-Walsh (2009). Is the CAPM theory in Sharpe (1964), Lintner (1965) and Mossin (1966) still alive? Returns of Fama-French 25 stock portfolios (1926-2011) are analyzed. The Maximum Likelihood Estimation Method is used. Likelihood Ratio test (LR) and Kolmogorov-Smirnov test (KS) are used to do model diagnostics. Akaike Information Criterion (AIC) is used for model comparison. Simulation results show the MatLab program is valid. Empirical results show with non-Normal errors and the EGARCH-type volatilities, the CAPM theory is not alive. This new model can capture the skewness, fat-tailness, asymmetric effects and volatility persistence in the data. This new model has better in-sample fit than others. Portfolios with smaller size have larger Beta value.
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