TY - JOUR
T1 - A new GARCH model with higher moments for stock return predictability
AU - Narayan, Paresh Kumar
AU - Liu, Ruipeng
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/9
Y1 - 2018/9
N2 - 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.
AB - 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.
KW - Data frequencies
KW - GARCH
KW - Higher order moments
KW - Predictive regression
UR - http://www.scopus.com/inward/record.url?scp=85044540930&partnerID=8YFLogxK
U2 - 10.1016/j.intfin.2018.02.016
DO - 10.1016/j.intfin.2018.02.016
M3 - Article
AN - SCOPUS:85044540930
SN - 1042-4431
VL - 56
SP - 93
EP - 103
JO - Journal of International Financial Markets, Institutions and Money
JF - Journal of International Financial Markets, Institutions and Money
ER -