Oil price shocks, competition, and oil &amp; gas stock returns — Global evidence

Gupta, Kartick

doi:10.1016/j.eneco.2016.04.019

Cited by 85 publications

(48 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many studies have focused on the effect of the oil price-stock returns relationship at the sector level (Cong et al 2008;Arouri 2011;Elyasiani et al 2011;Narayan and Sharma 2011;Lee et al 2012;Li et al 2012;Moya-Martinez et al 2014;Caporale et al 2015;Xu 2015;Zhu et al 2016;Li et al 2017;Peng et al 2017), and many specifically focus on the oil and gas sector (Sadorsky 2001;Boyer and Filion 2007;Cong et al 2008;Nandha and Faff 2008;Gupta 2016;Li et al 2017). A key conclusion of these studies is that oil price increases positively affect the stock returns of firms in the oil and gas sector (Smyth and Narayan 2018), with a prolonged nonlinear relationship that strengthens over time (Managi and Okimoto 2013).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and energy-sector equity returns in Iran: a Bayesian and quasi-Monte Carlo time-varying analysis

Fazelabdolabadi¹

2019

Financ Innov

View full text Add to dashboard Cite

This study investigates whether the implied crude oil volatility and the historical OPEC price volatility can impact the return to and volatility of the energy-sector equity indices in Iran. The analysis specifically considers the refining, drilling, and petrochemical equity sectors of the Tehran Stock Exchange. The parameter estimation uses the quasi-Monte Carlo and Bayesian optimization methods in the framework of a generalized autoregressive conditional heteroskedasticity model, and a complementary Bayesian network analysis is also conducted. The analysis takes into account geopolitical risk and economic policy uncertainty data as other proxies for uncertainty. This study also aims to detect different price regimes for each equity index in a novel way using homogeneous/non-homogeneous Markov switching autoregressive models. Although these methods provide improvements by restricting the analysis to a specific price-regime period, they produce conflicting results, rendering it impossible to draw general conclusions regarding the contagion effect on returns or the volatility transmission between markets. Nevertheless, the results indicate that the OPEC (historical) price volatility has a stronger effect on the energy sectors than the implied volatility has. These types of oil price shocks are found to have no effect on the drilling sector price pattern, whereas the refining and petrochemical equity sectors do seem to undergo changes in their price patterns nearly concurrently with future demand shocks and oil supply shocks, respectively, gaining dominance in the oil market.

show abstract

Section: Introductionmentioning

confidence: 99%

Uncertainty and energy-sector equity returns in Iran: a Bayesian and quasi-Monte Carlo time-varying analysis

Fazelabdolabadi¹

2019

Financ Innov

View full text Add to dashboard Cite

show abstract

“…To overcome the challenges in the stock market analysis, several computational models based on soft-computing and machine learning paradigms have been used in the stock-market analysis, prediction, and trading. Techniques like Support Vector Machine (SVM) [2,5], DTs [6], neural networks [7], Naïve Bayes [8,9] and artificial neural networks (ANN) [10,11] were reported to have performed better in stockmarket prediction than conventional arithmetic methods like Logistic regression (LR), in respect of error prediction and accuracy. Nevertheless, ensemble learning (EL) based on a learning-paradigm that combines multiple learning algorithms, forming committees to improve-predictions (stacking and blending) or decrease variance (bagging), and bias (boosting) is believed to perform better than single classifiers and regressors [12,13].…”

mentioning

confidence: 99%

A comprehensive evaluation of ensemble learning for stock-market prediction

2020

View full text Add to dashboard Cite

show abstract

“…Overtime, the major interest in this study has basically been on global and national scales respectively. While studies like Jones and Kaul (1996), Lean and Badeeb 2017, Bastianin et al (2017), Broadstock and Filis (2014), Dutta (2017), Ftiti (2015), Gupta (2016, Kayalar et al (2017), Sharmar (2017), , Silvapulle et al 2017and Khalifa (2017) analyse either on a panel or country by country basis, studies like Cong et al (2008), Fowowe (2013), Benkraiem et al (2018), Zheng and Su (2017), Bams et al (2017), , Bouri et al (2016), Gil-Alana and Yaya (2014), Henriques and Sadorsky (2008), Huang et al (2017), Tule et al (2017), Tursol and Faisal (2017), Guo (2017), andSoyemi et al (2017) carry out the study nationally.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

“…The study records that stock returns are sensitive to the fluctuations in the implied oil volatility index and that timevarying jumps do exist in the stock returns. Gupta (2016) analyses oil price shocks, competition, and oil & gas stock returns, using a comprehensive firm-level monthly data from 70 countries spanning from 1983 to 2014. The study uses panel Ordinary Least Squares and finds that oil price shocks positively impact firm-level returns.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Oil Price Shocks and Sectoral Stocks Behaviour in Nigeria: How Relevant is Asymmetry and Structural Breaks?

Fasanya

Adekoya

Odudu

2020

Preprint

View full text Add to dashboard Cite

In this paper, we model the relationship between oil price and stock returns for selected sectors in Nigeria using monthly data from January 2007 to December 2016. We employ both the Linear (Symmetric) ARDL by Pesaran et al. (2001) and Nonlinear (Asymmetric) ARDL by Shin et al. (2014) and we also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models. Our results indicate that the strength of this relationship varies across sectors, albeit asymmetric and breaks. We identify two structural breaks that occur in 2008 and 2010/2011 which coincidentally correspond to the global financial crisis and the Arab spring (Libyan shut-downs), respectively.Moreover, we observe strong supportfor asymmetry and structural breaks for some sectorsin the reaction of sector returns to movement in oil prices.These findings are robust and insensitive when considering different oil proxy.While further extensions can be pursued, the consideration of asymmetric effects as well as structural breaks should not be jettisoned when modelling this nexus.JEL codes: C22; C51; G12; Q43

show abstract

Oil price shocks, competition, and oil & gas stock returns — Global evidence

Cited by 85 publications

References 43 publications

Uncertainty and energy-sector equity returns in Iran: a Bayesian and quasi-Monte Carlo time-varying analysis

Uncertainty and energy-sector equity returns in Iran: a Bayesian and quasi-Monte Carlo time-varying analysis

A comprehensive evaluation of ensemble learning for stock-market prediction

Oil Price Shocks and Sectoral Stocks Behaviour in Nigeria: How Relevant is Asymmetry and Structural Breaks?

Contact Info

Product

Resources

About