A new GARCH model with higher moments for stock return predictability

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11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)93-103
Number of pages11
JournalJournal of International Financial Markets, Institutions and Money
Volume56
DOIs
Publication statusPublished - Sep 2018
Externally publishedYes

Keywords

  • Data frequencies
  • GARCH
  • Higher order moments
  • Predictive regression

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