A new GARCH model with higher moments for stock return predictability

Paresh Kumar Narayan and Ruipeng Liu

Journal of International Financial Markets, Institutions and Money, 2018, vol. 56, issue C, 93-103

Abstract: 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.

Keywords: GARCH; Predictive regression; Higher order moments; Data frequencies (search for similar items in EconPapers)
JEL-codes: F47 G12 G17 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (46)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfin:v:56:y:2018:i:c:p:93-103

DOI: 10.1016/j.intfin.2018.02.016

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