alexa Forecast and Backtesting of VAR Models in Crude Oil Mar

Research & Reviews: Journal of Statistics and Mathematical Sciences
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Research Article

Forecast and Backtesting of VAR Models in Crude Oil Market

Yue-Xian Li1*, Jin-Guo Lian2 and Hong-Kun Zhang2

1Department of Mathematics and Statistics, Inner Mongolia Agricultural University, Hohhot City, Inner Mongolia Autonomous Region, PR China

2Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst MA 01003, USA

Corresponding Author:
Yue-Xian Li
Department of Mathematics and Statistics Inner Mongolia Agricultural University Hohhot City, Inner Mongolia Autonomous Region, PR China
E-mail: [email protected]

Received date: 10/03/2016 Accepted date: 14/05/2016 Published date: 18/05/2016

 

Abstract

The oil price has a very important effect on the world economy. In this paper, using data sets of Europe Brent and West Texas Intermediate (WTI) Cushing crude oil daily prices from Jan. 4, 2000 to Jan. 4, 2016, the VaR forecasting performance of GARCH-type models are analyzed and compared in a short horizon. Based on the Kupiecs POF-test and Christo ffersens interval forecast test, as well as a Back testing VaR Loss Function, the empirical results indicate that, for Europe Brent crude oil, EGARCH (1,1) has the best performance; while for WTI, APARCH (1,1) and GJR-GARCH (1,1) outperform other GARCH models. In fact, these results also give significant guidance on how to choose a better risk management model for the certain commodity of different companies even in the same time period.

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