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A new GARCH model with higher moments for stock return predictability

journal contribution
posted on 01.09.2018, 00:00 authored by Paresh Narayan, Ruipeng LiuRuipeng Liu
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.

History

Journal

Journal of international financial markets, institutions and money

Volume

56

Pagination

93 - 103

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

1042-4431

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2018, Elsevier B.V.