Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 65, Issue -, Pages 233-241Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2016.08.045
Keywords
Oil price volatility; Artificial neural network; GARCH models
Ask authors/readers for more resources
This paper builds on previous research and seeks to determine whether improvements can be achieved in the forecasting of oil price volatility by using a hybrid model and incorporating financial variables. The main conclusion is that the hybrid model increases the volatility forecasting precision by 30% over previous models as measured by a heteroscedasticity-adjusted mean squared error (HMSE) model. Key financial variables included in the model that improved the prediction are the Euro/Dollar and Yen/Dollar exchange rates, and the DJIA and FTSE stock market indexes. (C) 2016 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available