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A New Parametric Approach to Modeling Generalized Autoregressive Conditional Density Model at Higher Order Moments

Journal of Financial Studies

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Title A New Parametric Approach to Modeling Generalized Autoregressive
Conditional Density Model at Higher Order Moments
 
Creator Kai-Li Wang
Jai-Hui Lin
 
Description This paper presents a flexible Autoregressive Conditional Density Moments-in-Mean (ARCD Moments-in-Mean) model. Our innovative approach not only allow the higher order moments to be time-varying function of conditioning information, but also extend the traditional ARCH-M model by explicitly modeling the influence of conditional moments (conditional variance, skewness and kurtosis) on the conditional expectation of the data series. The empirical results suggest a preponderance of evidence to support the performance of ARCD Moments-in-Mean model on competing alternatives. Of particular, the time varying skewness, capturing the up/downside risk, is found to exhibit significant effect in explaining the expected return than that of second and fourth moment. In addition, it is also found that the asymmetry of conditional distribution is potentially possible to substitute the asymmetry in the volatility specification, demonstrating the importance to specify the proper distribution specification on financial econometric modeling.

Keywords: GARCH model, skewness, kurtosis, moments, risk premium, distribution.
 
Publisher Journal of Financial Studies
財務金èžå­¸åˆŠ
 
Date 2011-06-03
 
Type —
 
Format application/pdf
 
Identifier http://www.jfs.org.tw/index.php/jfs/article/view/2011142
 
Source Journal of Financial Studies; Vol 11, No 2 (2003); 1
財務金èžå­¸åˆŠ; Vol 11, No 2 (2003); 1
 
Language —