Modeling and forecasting the volatility of petroleum futures prices

Kang, Sang Hoon; Yoon, Seong‐Min

doi:10.1016/j.eneco.2012.09.010

Cited by 98 publications

(56 citation statements)

References 41 publications

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“…Therefore, risk management of energy products prices becomes very important for both academicians and market participants, and many risk measurement tools have been proposed in the literature. A non-exhausted list includes: Cabedo and Moya (2003), Costello, Asem and Gardner (2008), Krehbiel and Adkins (2005), Marimoutou, Raggad and Trabelsi (2009), Kang and Yoon (2013), Youssef, Belkacem, and Mokni (2015), and Fiano and Grossi (2015). These papers employ a widely-used risk measure, Value-at-Risk (VaR) originally proposed by J.P. Morgan in 1994 (see Duffie and Pan, 1997, for a discussion of this measure), but differ in the model assumptions.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Factor Model for Risk Management of Commodity Derivatives

Guo¹

2017

Proceedings of the 7th Economics &Amp; Finance Conference, Tel Aviv

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Section: Introductionmentioning

confidence: 99%

A Stochastic Factor Model for Risk Management of Commodity Derivatives

Guo¹

2017

Proceedings of the 7th Economics &Amp; Finance Conference, Tel Aviv

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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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“…The results suggested that "gasoline prices have more pronounced short-run dynamics relative to those of WTI and heating oil prices, which are affected by market shocks." (Kang et al, 2013) In their crude oil price analysis, Kyriakou et al (2016) state that supply is inelastic, which means that a small change in demand can lead to abrupt price changes. They also concluded that, even though most petroleum products are affected by seasonality effects, the crude oil market doesn't refl ect seasonality "in the term structure of futures prices."…”

Section: Current Conditions In Futures and Options Marketsmentioning

confidence: 99%

Oil and Gas Futures and Options Market

Nosić¹,

Sedlar

Jukić

2017

MGPB

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Energy mineral resource markets are represented by complex supply and demand ratios which are depending on diff erent factors such as technical (transport) and geopolitical. The main characteristicof energy markets is represented by an uneven geographic distribution of hydrocarbon reserves and production on one hand, and energy consumption on the other. World oil markets, although geographically localized, because of specifi c market trade, represent a unique global market with a decreasing price diff erence. Price diff erences are the result of the development of transport possibilities of oil supply. The development of transport routes of natural gas and an increasing number of liquefi ed natural gas terminals in the world give pressure to the natural gas market and its integration into the global gas market. The integration of regional gas markets into a common European gas market is the main energy policy of EU concerning natural gas. On the other hand, there are still signifi cant price diff erences on some markets (e.g. United States of America -South East Asia). The development of global energy markets is enabled by the development of futures and options contracts of an energy trade which have replaced bilateral contract deals between producers and consumers. Futures contracts are standardized contracts traded on exchanges. A buyer agrees to buy a certain quantity of stock for an agreed upon price and with some future delivery date. An option is a contract which gives a buyer the option of the right to buy (or sell, depending on the option) an asset at a predetermined price and at a later date. A stock's price risk can be managed with the purchase and selling futures and options contracts. This paper deals with futures and options energy markets and their market strategies.

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Modeling and forecasting the volatility of petroleum futures prices

Cited by 98 publications

References 41 publications

A Stochastic Factor Model for Risk Management of Commodity Derivatives

A Stochastic Factor Model for Risk Management of Commodity Derivatives

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

Oil and Gas Futures and Options Market

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