Volatility forecasting for crude oil futures
AbstractThis article 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. 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 1 to 3 days ahead. For horizons from 1 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. Exponential GARCH models display the best performance in this case. The swings in oil prices that gave investors and traders whiplash in 2004 are not preventing new investors from rushing into oil and other energy-related commodities this year. (…) Ultimately, the rising number of speculator could lead to even more price volatility in 2005, pushing the highs higher and the lows lower. (…) After a generation in the wilderness, the oil futures that are used to make a bet on oil prices have become a bona fide investment, said Charles O'Donnell, who manages Lake Asset Management, a small energy fund based in London. Heather Timmons, The New York Times1
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Economics Letters.
Volume (Year): 17 (2010)
Issue (Month): 16 ()
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Other versions of this item:
- M. Marzo & P. Zagaglia, 2007. "Volatility Forecasting for Crude Oil Futures," Working Papers 599, Dipartimento Scienze Economiche, Universita' di Bologna.
- Marzo, Massimiliano & Zagaglia, Paolo, 2007. "Volatility forecasting for crude oil futures," Research Papers in Economics 2007:9, Stockholm University, Department of Economics.
- 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
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