The impact of oil price shocks on the U.S. stock market: A note on the roles of U.S. and non-U.S. oil production

Kang, Wensheng; Ratti, Ronald A.; Vespignani, Joaquin L.

doi:10.1016/j.econlet.2016.06.008

Cited by 116 publications

(43 citation statements)

References 7 publications

(17 reference statements)

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“…Whereas earlier studies assume linear and symmetrical adjustment processes for the underlying variables (Zhu et al 2011), the current view favors assuming an asymmetrical effect of oil prices on stock returns (Basher and Sadorsky 2006;Kilian 2008;Kilian and Park 2009;Arouri 2011;Aggarwal et al 2012;Asteriou and Bashmakova 2013;Narayan and Gupta 2015;Phan et al 2015;Kang et al 2016;Li et al 2017). This view is further supported by the nonlinear characteristics of the oil price-stock return relationship.…”

Section: Introductionmentioning

confidence: 85%

“…Performing this analysis shows that oil supply shocks were mainly influential from the second half of 2013 through the first half of 2015. Currently, oil supply shocks are no longer as important to macroeconomic developments, and aggregate demand shocks are seen as more influential (Kang et al 2016). Further, empirical evidence shows that the effect of the oil price on equity returns varies considerably across sectors depending on the nature of the structural shock (Broadstock and Filis 2014).…”

Section: Introductionmentioning

confidence: 99%

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

Fazelabdolabadi¹

2019

Financ Innov

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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.

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Section: Introductionmentioning

confidence: 85%

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

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“…Their findings report that G7 stock markets have no significant impact on oil prices; however, demand side shocks in oil prices have significant effect on stock markets. Kang et al, (2016) has statistically significant effect on US stock returns. Maghyereh et al, (2017) investigated the spillover between oil, stock and gold markets, the results reported the evidence of significant spillover among oil and stock markets.…”

Section: Projected Relationship Of Market Returns and Oil Pricesmentioning

confidence: 94%

The Role of CAPM and Oil Prices to Analyze the Firm’s Stock Return

Khalid¹,

Khan²,

Saleem³

2019

RAE

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This paper attempts to analyze the applicability of CAPM and the effect of oil prices on firm’s stock in case of Pakistan. To examine this research objective, we use the yearly data of 148 listed firms over the period of 2006 to 2015. We employ three different estimation techniques, panel correlated standard error estimation (PCSE), Driscoll and Kraay (DK) estimation and common correlated effects pooled (CCEP), to analyze the relationship between oil variables and firm’s stock return. Moreover, we further estimate the variables by using robust estimation techniques to validate the empirical results. The estimations report the inapplicable of market premium in case of Pakistani firms. However, the oil price and lagged oil prices provides the evidence of negative and statistically significance in textile, sugar, cement, chemical and engineering sectors. On contrary, the oil prices and lagged oil prices have positive and significant impact on stock return in transportation and energy sectors. In conclusion, it is difficult to escape the conclusion that oil price have higher influence on stock returns as compared to the market premium and nearly all the manufacturing sectors are inversely affected by the oil price rise.

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“…The energy market can undoubtedly be a driver of economic growth and job creation in a developing country, such as Romania. Previous examinations were undertaken for African stock markets [20,23], Asian countries [6,14,15,[24][25][26][27][28], European nations [18,29], the US stock market [1,16,[30][31][32], Egypt [33], Gulf states [7,34,35], India [36], Islamic stocks [19], Lebanon [37], Mexico [38,39], and Turkey [40,41]. To the best of our knowledge, this is the first investigation to examine the linkage between the energy market and the Romanian stock market.…”

Section: Introductionmentioning

confidence: 99%

On the Linkage between the Energy Market and Stock Returns: Evidence from Romania

2019

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his paper aims to establish whether the Romanian energy market has an influence on the good running of the associated capital market. In order to achieve this objective, we approached a series of econometric techniques that allowed us to study the cointegration between variables, the presence of short-term or long-term causality relationships, and the application of impulse-response functions to analyze how the BET index responds to the shocks applied. The empirical findings from the Johansen cointegration test, ARDL model, and VAR/VECM models confirmed both the presence of a long-term and short-term relationship between the energy market and capital market. From all energy market indicators, only hard coal presented a causal relationship with the BET index. We also noticed a unidirectional relationship from the WTI crude oil to the Romanian capital market. Our findings should be of interest to researchers, regulators, and market participants.

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The impact of oil price shocks on the U.S. stock market: A note on the roles of U.S. and non-U.S. oil production

Cited by 116 publications

References 7 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

The Role of CAPM and Oil Prices to Analyze the Firm’s Stock Return

On the Linkage between the Energy Market and Stock Returns: Evidence from Romania

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