A new GARCH model with higher moments for stock return predictability

被引：16

作者：

Narayan, Paresh Kumar ^{[1
]}

Liu, Ruipeng ^{[2
]}

机构：

[1] Deakin Univ, Deakin Business Sch, Ctr Financial Econometr, Melbourne, Vic, Australia

[2] Deakin Univ, Deakin Business Sch, Dept Finance, Melbourne, Vic, Australia

来源：

JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY | 2018年 / 56卷

关键词：

GARCH; Predictive regression; Higher order moments; Data frequencies; CONDITIONAL VOLATILITY; EXCHANGE-RATES; SKEWNESS; FUNDAMENTALS; REGRESSIONS; INFERENCE; KURTOSIS; PANEL;

D O I：

10.1016/j.intfin.2018.02.016

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：93 / 103

页数：11

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