Volatility forecasting for crude oil futures
AbstractThis paper studies the forecasting properties of linear GARCH models for closing-day futures prices on crude oil, first position, traded in the New York Mercantile Exchange from January 1995 to November 2005. In order to account for fat tails in the empirical distribution of the series, we compare models based on the normal, Student’s t and Generalized Exponential distribution. We focus on out-of-sample predictability by ranking the models according to a large array of statistical loss functions. The results from the tests for predictive ability show that the GARCH-G model fares best for short horizons from one to three days ahead. For horizons from one week ahead, no superior model can be identified. We also consider out-of-sample loss functions based on Value-at-Risk that mimic portfolio managers and regulators’ preferences. EGARCH models display the best performance in this case.
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Bibliographic InfoPaper provided by Stockholm University, Department of Economics in its series Research Papers in Economics with number 2007:9.
Length: 33 pages
Date of creation: 21 Jun 2007
Date of revision:
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Postal: Department of Economics, Stockholm, S-106 91 Stockholm, Sweden
Phone: +46 8 16 20 00
Fax: +46 8 16 14 25
Web page: http://www.ne.su.se/
More information through EDIRC
GARCH models; kurtosis; oil prices; forecasting;
Other versions of this item:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G19 - Financial Economics - - General Financial Markets - - - Other
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-06-30 (All new papers)
- NEP-ENE-2007-06-30 (Energy Economics)
- NEP-ETS-2007-06-30 (Econometric Time Series)
- NEP-FOR-2007-06-30 (Forecasting)
- NEP-RMG-2007-06-30 (Risk Management)
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