The main purpose of the paper is to propose a new GARCH-SK predictive regression model that accommodates higher order moments (skewness and kurtosis) in testing the null hypothesis of no predictability. Using an extensive and well-known time-series dataset on stock returns and 19 predictors for the United States, we show that our proposed GARCH-SK model outperforms a model without these higher moments. The superior performance of our proposed model holds both statistically and economically and is robust to different data frequencies.
|Number of pages||11|
|Journal||Journal of International Financial Markets, Institutions and Money|
|Publication status||Published - Sep 2018|
- Data frequencies
- Higher order moments
- Predictive regression