A new GARCH model with higher moments for stock return predictability

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)93-103
Number of pages11
JournalJournal of International Financial Markets, Institutions and Money
Publication statusPublished - Sep 2018
Externally publishedYes


  • Data frequencies
  • Higher order moments
  • Predictive regression

Cite this