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A Nonparametric GARCH Model of Crude Oil Price Return Volatility

HOU, Ai Jun LU and Suardi, Sandy (2010)
Abstract
The use of parametric GARCH models to characterise crude oil price volatility is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast oil price return volatility. Focusing on two crude oil markets, Brent and West Texas Intermediate (WTI), we show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. These results are supported by the use of robust loss functions and the Hansen’s (2005) superior predictive ability test. The improvement in forecasting accuracy of oil price return volatility based on the nonparametric GARCH model suggests... (More)
The use of parametric GARCH models to characterise crude oil price volatility is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast oil price return volatility. Focusing on two crude oil markets, Brent and West Texas Intermediate (WTI), we show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. These results are supported by the use of robust loss functions and the Hansen’s (2005) superior predictive ability test. The improvement in forecasting accuracy of oil price return volatility based on the nonparametric GARCH model suggests that this method o¤ers an attractive and viable alternative to the commonly used parametric GARCH models. (Less)
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author
organization
publishing date
type
Working Paper
publication status
submitted
subject
keywords
Volatility estimation, Non-parametric method, Crude oil prices, GARCH modelling, Forecasts
pages
32 pages
language
English
LU publication?
yes
id
56e536b6-155d-4827-9a08-cc74a7e8cc16 (old id 1763396)
date added to LUP
2011-01-21 15:04:47
date last changed
2016-04-16 10:59:19
@misc{56e536b6-155d-4827-9a08-cc74a7e8cc16,
  abstract     = {The use of parametric GARCH models to characterise crude oil price volatility is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast oil price return volatility. Focusing on two crude oil markets, Brent and West Texas Intermediate (WTI), we show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. These results are supported by the use of robust loss functions and the Hansen’s (2005) superior predictive ability test. The improvement in forecasting accuracy of oil price return volatility based on the nonparametric GARCH model suggests that this method o¤ers an attractive and viable alternative to the commonly used parametric GARCH models.},
  author       = {HOU, Ai Jun and Suardi, Sandy},
  keyword      = {Volatility estimation,Non-parametric method,Crude oil prices,GARCH modelling,Forecasts},
  language     = {eng},
  pages        = {32},
  title        = {A Nonparametric GARCH Model of Crude Oil Price Return Volatility},
  year         = {2010},
}