Forecasting crude oil market volatility: Further evidence using GARCH-class models

Wei, Yu; Wang, Yudong; Huang, Dengshi

doi:10.1016/j.eneco.2010.07.009

Cited by 318 publications

(174 citation statements)

References 41 publications

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“…For oil markets this "inverse leverage effect" has been confirmed by Wei et al (2010) who found significant indications of its existence in the ICE Brent market. However, together with Wang et al (2008), Agnolucci (2009) and Padungsaksawasdi and Daigler (2013) found no evidence of such effects in the WTI futures market.…”

Section: Daily Returnssupporting

confidence: 58%

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Forecasting Volatility of the U.S. Oil Market

et al. 2014

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We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve, contains predictive power beyond what is embedded in the implied volatility. In out-of-sample forecasting we find that econometric models based on realized volatility can be improved by including implied volatility and other variables. Our results show that including implied volatility significantly improves daily and weekly volatility forecasts, while including other market variables significantly improves daily, weekly and monthly volatility forecasts.

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Section: Daily Returnssupporting

confidence: 58%

“…Forecasting volatility has traditionally been done using the generalized autoregressive conditional heteroscedasticity (GARCH) approach of Bollerslev (1986) and Engle (1982), also in energy commodity markets (see e.g. Marzo and Zagaglia (2010) and Wei et al (2010)). …”

Section: Introductionmentioning

confidence: 99%

Forecasting Volatility of the U.S. Oil Market

et al. 2014

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“…However, the vast majority of the research has focused on financial markets, with the focus only recently turning to the energy markets 2 (Wilson et al, 1996;Yang et al, 2002;Linn and Zhu, 2004;Pindyck, 2004;Kuper and van Soest, 2006;Mohammadi and Su, 2010;Wei et al, 2010;Kang and Yoon, 2013).…”

Section: Introductionmentioning

confidence: 99%

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Baruník

Křehlík

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Terms of use: Documents inIn the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specifc estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the precrisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with arti_cial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.

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“…In the case of the car companies some may fail a second time because their products are still not yet ready for a high-priced oil environment (Wei et al, 2010). Governments may not be willing to spend the money to rescue these businesses (such as the car company bailouts in the U.S.) and should be prepared for increasing unemployment as vulnerable sectors contract.…”

Section: Government Risksmentioning

confidence: 99%

Global oil risks in the early 21st century

Fantazzini

Höök

Angelantoni³

2011

Energy Policy

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The Deepwater Horizon incident demonstrated that most of the oil left is deep offshore or in other difficult to reach locations. Moreover, obtaining the oil remaining in currently producing reservoirs requires additional equipment and technology that comes at a higher price in both capital and energy. In this regard, the physical limitations on producing ever-increasing quantities of oil are highlighted as well as the possibility of the peak of production occurring this decade. The economics of oil supply and demand are also briefly discussed showing why the available supply is basically fixed in the short to medium term. Also, an alarm bell for economic recessions is shown to be when energy takes a disproportionate amount of total consumer expenditures. In this context, risk mitigation practices in government and business are called for. As for the former, early education of the citizenry of the risk of economic contraction is a prudent policy to minimize potential future social discord. As for the latter, all business operations should be examined with the aim of building in resilience and preparing for a scenario in which capital and energy are much more expensive than in the business-as-usual one.

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Forecasting crude oil market volatility: Further evidence using GARCH-class models

Cited by 318 publications

References 41 publications

Forecasting Volatility of the U.S. Oil Market

Forecasting Volatility of the U.S. Oil Market

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Global oil risks in the early 21st century

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