Modeling and Forecasting the Volatility of Gas Futures Prices

We examine the ability of three different GARCH-class models, with four innovation distributions, to capture the volatility properties of natural gas futures contracts traded on the New York Mercantile Exchange. We jointly estimate the long-memory processes for conditional return and variance invest...

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Bibliographic Details
Published inRevista Brasileira de Finanças Vol. 15; no. 4; pp. 511 - 535
Main Authors Aiube, Fernando Antonio Lucena, Samanez, Carlos Patrício, Resende, Larissa de Oliveira, Baidya, Tara Keshar Nanda
Format Journal Article
LanguageEnglish
Published 20.06.2018
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Summary:We examine the ability of three different GARCH-class models, with four innovation distributions, to capture the volatility properties of natural gas futures contracts traded on the New York Mercantile Exchange. We jointly estimate the long-memory processes for conditional return and variance investigating the long-memory and persistence of long and short maturities contracts. We examine the ability of these models and distributions to forecast the conditional variance. We find that AR(FI)MA-FIAPARCH model is a better fit for short- and long-term contracts. However, there is not a single innovation distribution that provides a better fit for all of the data examined. The out-of- sample forecast of variance also provides mixed results concerning the best innovation distribution. Further, the persistence decreases as the maturity of contracts increases.
ISSN:1679-0731
1984-5146
DOI:10.12660/rbfin.v15n4.2017.63724