An analysis of factors affecting price volatility of the US oil market

Yang, Chao-Ping; Hwang, M. J.; Huang, Bwo‐Nung

doi:10.1016/s0140-9883(01)00092-5

Cited by 122 publications

(57 citation statements)

References 16 publications

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“…Past researches find that in the absence of alternative sources of energy, oil price rise will increase the production cost, which in turn leads to the rise of inflation and interest rate, consequently affects business operational cost and causes stock price to decline (Gisser and Goodwin, 1986;Huang et al, 1996;Yang et al, 2002;Henriques and Sadorsky, 2008). Additionally, Jones and Kaul (1996) through examining developed countries such as USA, the United Kingdom, Canada, and Japan confirm the negative relationships between oil prices and these countries' stock index returns.…”

Section: Use Vector Auto Regression (Var) To Analyze Monthly Data Of mentioning

confidence: 83%

Explore the Impact of the Trading Value, The Oil Price and Quantitative Easing Policy on the Taiwan and Korea Stock Market Return with Quantile Regression

Hsu

Tsai

2017

Asian Economic and Financial Review

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Section: Use Vector Auto Regression (Var) To Analyze Monthly Data Of mentioning

confidence: 83%

Explore the Impact of the Trading Value, The Oil Price and Quantitative Easing Policy on the Taiwan and Korea Stock Market Return with Quantile Regression

Hsu

Tsai

2017

Asian Economic and Financial Review

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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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“…How to analyze and use these factors to forecast the crude oil price has attracted increasing attention from academics and practitioners in the past decade. The literature on factors affecting crude oil price relates oil shocks either to the instability of the market structures or to the effect of the price elasticity of demand [1][2][3][4]. Mork [1] and Huntington [2] demonstrated the asymmetric relationship that a reduction in oil prices does not necessarily lead to noticeable output growth, while an increase can have a negative impact on output growth.…”

Section: Introductionmentioning

confidence: 99%

“…The study of Ferderer points to the observation that disruptions in oil market not only give rise to higher prices, but also increase oil price volatility [3]. C.W.Yang [4] analyze factors affecting price volatility of the US oil market by examining the market structure of OPEC, the stable and unstable demand structure, and related elasticity of demand.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price

Wang

Zhang

et al. 2007

Lecture Notes in Computer Science

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Abstract. In this paper, a new hybrid approach is presented to analyze factors affecting crude oil price using rough set and wavelet neural network. Related factors that affect crude oil price are found using text mining technique and Brent oil price is chosen as the decision price because it plays an important role in world crude oil markets. The relevant subsets of the factors are discovered by rough set module and the main factors are got, and then the important degrees of these are measured using wavelet neural network. Based on the novel hybrid approach, the predictability of crude oil price is discussed.

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An analysis of factors affecting price volatility of the US oil market

Cited by 122 publications

References 16 publications

Explore the Impact of the Trading Value, The Oil Price and Quantitative Easing Policy on the Taiwan and Korea Stock Market Return with Quantile Regression

Explore the Impact of the Trading Value, The Oil Price and Quantitative Easing Policy on the Taiwan and Korea Stock Market Return with Quantile Regression

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

A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price

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