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A new GARCH model with higher moments for stock return predictability
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.
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Journal
Journal of international financial markets, institutions and moneyVolume
56Pagination
93 - 103Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
1042-4431Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2018, Elsevier B.V.Usage metrics
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