The heterogeneity dependence between crude oil price changes and industry stock market returns in China: Evidence from a quantile regression approach

Zhu, Huiming; Guo, Yawei; You, Wanhai; Xu, Yu

doi:10.1016/j.eneco.2015.12.027

Cited by 213 publications

(91 citation statements)

References 57 publications

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“…The findings on impacts of oil price uncertainty present that the effects of oil price uncertainty on sectoral stock returns are nonlinear and regime dependent. These results are in line with existing studies regarding time-varying effects of oil price on stock returns (Aloui et al, 2012;Aloui & Jammazi, 2009;Arouri et al, 2016;Jammazi & Aloui, 2010;Jammazi & Nguyen, 2015;Moya-Martínez et al, 2014;Reboredo, 2010;Zhu et al, 2016Zhu et al, , 2017. The findings also show that mostly impacts of oil price changes are statistically significant in a high-volatility regime.…”

Section: Ms Dynamic Regression Frameworksupporting

confidence: 91%

“…Therefore, these findings on GEPU and oil price impacts validate the primary assumption of nonlinear relationship between sectoral stock returns and global risk factors. Moreover, the existing studies also discussed about nonlinear relationship between sectoral stock returns and global factors with empirical evidences (Aloui & Jammazi, 2009;Aloui, Nguyen, & Njeh, 2012;Arouri et al, 2016;Jammazi & Aloui, 2010;Jammazi & Nguyen, 2015;Moya-Martínez et al, 2014;Reboredo, 2010;Zhu et al, 2017;Zhu, Guo, You, & Xu, 2016).…”

Section: Linear Regression Frameworkmentioning

confidence: 99%

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The impacts of global economic policy uncertainty on stock market returns in regime switching environment: Evidence from sectoral perspectives

Hoque

Zaidi

2018

Int. J Fin Econ

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This study contributes in building emerging literature by investigating the impacts of global economic policy uncertainty on Malaysian sectoral stock performance. This study models sectoral stock returns as time‐varying transition probability Markovian processes and employs two‐stage Markov‐switching model for findings impacts of global economic policy uncertainty on sectoral stock returns in regime switching environment. The empirical results reveal that linear framework unable to detect the effects global economic policy uncertainty, and the Markov‐switching model exhibits significant effects of global economic policy uncertainty on all sectoral stock returns excluding technology sector in Malaysia stock market. The findings also expose that the effects of global economic policy uncertainty vary across regime states, sectors, and nature of effects, where the negative effects of global economic policy uncertainty dominate over positive effects. The global economic policy uncertainty exhibits greater impacts on stock returns in high‐volatility regime. Thus, the findings confirm the existence of asymmetric, nonlinear, nonmonotonic, and state‐dependent relationship between global economic policy uncertainty and sectoral stock returns in Malaysia. Therefore, the overall empirical findings can be applied in asset pricing and investment decision‐making purposes. The findings also suggest that global economic policy uncertainty can be a systemic risk factor and predictor of stock market returns.

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Section: Ms Dynamic Regression Frameworksupporting

confidence: 91%

Section: Linear Regression Frameworkmentioning

confidence: 99%

The impacts of global economic policy uncertainty on stock market returns in regime switching environment: Evidence from sectoral perspectives

Hoque

Zaidi

2018

Int. J Fin Econ

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“…Only Hammoudeh et al [27] analyzed the impact of four energy prices on the European Union CO 2 emission allowance prices by the quantile regression. Quantile regression has been widely employed in empirical studies [28][29][30][31][32][33][34][35][36][37][38]. Many scholars used the quantile regression approach to investigate the dynamics of changes in different wage distribution [28][29][30] and dynamic relationship between exchange rate and stock price [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Impacts on CO2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

Zhang

2016

Sustainability

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A pilot regional carbon emission trading scheme (ETS) has been implemented in China for more than two years. An investigation into the impacts of different factors on carbon dioxide (CO 2 ) emission allowance prices provides guidance for price-making in 2017 when the nation-wide ETS of China will be established. This paper adopts a quantile regression approach to estimate the impacts of different factors in Shanghai emission trading scheme (SH-ETS), namely, economic growth, energy prices and temperature. The empirical analysis shows that: (i) the economic growth in Shanghai leads to a drop in the carbon allowance prices; (ii) the oil price has a slightly positive effect on the allowance prices regardless of the ordinary least squares (OLS) or quantile regression method; (iii) a long-run negative relationship exists between the coal price and the Shanghai emission allowances (SHEA) prices, but a positive interaction under different quantiles, especially the 25%-50% quantiles; (iv) temperature has a significantly positive effect at the 20%-30% quantiles and a conspicuous negative impact at the right tail of the allowances prices.

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“…Third, our study contributes to extending the applicability of Baur's (2013) approach, which Mensi, Hammoudeh, Reboredo, and Nguyen (2014) and Zhu, Guo, You, and Xu (2016) have also used. This powerful and flexible technique employs a quantile regression approach to capture the degree to which a variable depends on another factor based on market conditions, and the structure of that dependence.…”

Section: Introductionmentioning

confidence: 96%

The quantile dependence of commodity futures markets on news sentiment

Omura

Todorova

2019

Journal of Futures Markets

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Focusing on energy commodities, industrial metals, and gold, this paper examines the degree to which commodity futures returns depend on news sentiment under various market conditions, and the structure of that dependence. We observe an asymmetric market reaction to positive and negative news sentiment, which changes in periods of financial turmoil. The quantile regression analysis shows that news sentiment's influence on the futures returns follows an upward trend at higher percentiles. This structure flattens for positive news during the global financial crisis, while the slope for the negative component steepens in backwardation periods.

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The heterogeneity dependence between crude oil price changes and industry stock market returns in China: Evidence from a quantile regression approach

Cited by 213 publications

References 57 publications

The impacts of global economic policy uncertainty on stock market returns in regime switching environment: Evidence from sectoral perspectives

The impacts of global economic policy uncertainty on stock market returns in regime switching environment: Evidence from sectoral perspectives

Impacts on CO2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

The quantile dependence of commodity futures markets on news sentiment

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