IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v84y2019ics0140988319303573.html
   My bibliography  Save this article

How to effectively estimate the time-varying risk spillover between crude oil and stock markets? Evidence from the expectile perspective

Author

Listed:
  • Zhang, Yue-Jun
  • Ma, Shu-Jiao

Abstract

With the integration and financialization of world economy, massive hot money has frequently flowed between crude oil and stock markets, and has brought significant extreme risks and their spillover. For this reason, this paper develops the ARCH-Expectile model with embedded Conditional AutoRegressive structure (namely CAR-ARCHE model) and expectile-based VaR (EVaR) approach, and investigates the time-varying risk spillover between WTI futures market and US, UK, Japanese and global stock markets, respectively. The results indicate that, for one thing, the EVaR approach based on CAR-ARCHE model is more adequate than the conventional quantile-based VaR (QVaR) approach based on GED-GARCH for WTI and stock markets, which is due to the evident advantages of expectile compared to quantile. For another, the unidirectional downside risk spillover effects from WTI to the four stock markets and vice-versa are only remarkable during major events and present variations with jumps, but the bidirectional downside risk spillover effects between them are significant for each time point during the in-sample period, which indicate that the simultaneous risk spillover between WTI and stock markets are fairly pronounced.

Suggested Citation

  • Zhang, Yue-Jun & Ma, Shu-Jiao, 2019. "How to effectively estimate the time-varying risk spillover between crude oil and stock markets? Evidence from the expectile perspective," Energy Economics, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303573
    DOI: 10.1016/j.eneco.2019.104562
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988319303573
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2019.104562?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kuan, Chung-Ming & Yeh, Jin-Huei & Hsu, Yu-Chin, 2009. "Assessing value at risk with CARE, the Conditional Autoregressive Expectile models," Journal of Econometrics, Elsevier, vol. 150(2), pages 261-270, June.
    2. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
    3. Basher, Syed A. & Sadorsky, Perry, 2006. "Oil price risk and emerging stock markets," Global Finance Journal, Elsevier, vol. 17(2), pages 224-251, December.
    4. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
    5. Lu, Feng-bin & Hong, Yong-miao & Wang, Shou-yang & Lai, Kin-keung & Liu, John, 2014. "Time-varying Granger causality tests for applications in global crude oil markets," Energy Economics, Elsevier, vol. 42(C), pages 289-298.
    6. Yue-Jun Zhang & Yi-Ming Wei, 2011. "The dynamic influence of advanced stock market risk on international crude oil returns: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 967-978.
    7. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    8. Yao, Qiwei & Tong, Howell, 1996. "Asymmetric least squares regression estimation: a nonparametric approach," LSE Research Online Documents on Economics 19423, London School of Economics and Political Science, LSE Library.
    9. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    10. Araújo Santos, Paulo & Fraga Alves, Isabel & Hammoudeh, Shawkat, 2013. "High quantiles estimation with Quasi-PORT and DPOT: An application to value-at-risk for financial variables," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 487-496.
    11. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    12. Belkacem Abdous & Bruno Remillard, 1995. "Relating quantiles and expectiles under weighted-symmetry," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(2), pages 371-384, June.
    13. Aigner, D J & Amemiya, Takeshi & Poirier, Dale J, 1976. "On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 17(2), pages 377-396, June.
    14. Marcel Fratzscher & Daniel Schneider & Ine Van Robays, 2013. "Oil Prices, Exchange Rates and Asset Prices," CESifo Working Paper Series 4264, CESifo.
    15. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    16. Bai, Shuming & Koong, Kai S., 2018. "Oil prices, stock returns, and exchange rates: Empirical evidence from China and the United States," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 12-33.
    17. Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
    18. Du, Limin & He, Yanan, 2015. "Extreme risk spillovers between crude oil and stock markets," Energy Economics, Elsevier, vol. 51(C), pages 455-465.
    19. Jong-Min Kim and Hojin Jung, 2018. "Dependence Structure between Oil Prices, Exchange Rates, and Interest Rates," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    20. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    21. Fan, Ying & Zhang, Yue-Jun & Tsai, Hsien-Tang & Wei, Yi-Ming, 2008. "Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach," Energy Economics, Elsevier, vol. 30(6), pages 3156-3171, November.
    22. Yudong Wang & Li Liu, 2016. "Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging," Empirical Economics, Springer, vol. 50(4), pages 1481-1509, June.
    23. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    24. Hong, Yongmiao, 2001. "A test for volatility spillover with application to exchange rates," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 183-224, July.
    25. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    26. Wang, Ching-Ping & Huang, Hung-Hsi, 2016. "Optimal insurance contract under VaR and CVaR constraints," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 110-127.
    27. Zhang, Yue-Jun & Chevallier, Julien & Guesmi, Khaled, 2017. "“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets," Energy Economics, Elsevier, vol. 68(C), pages 228-239.
    28. Fenghua Wen & Feng Min & Yue‐Jun Zhang & Can Yang, 2019. "Crude oil price shocks, monetary policy, and China's economy," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 812-827, April.
    29. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    30. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    31. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
    32. James W. Taylor, 2008. "Estimating Value at Risk and Expected Shortfall Using Expectiles," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 231-252, Spring.
    33. Chai, Jian & Guo, Ju-E. & Meng, Lei & Wang, Shou-Yang, 2011. "Exploring the core factors and its dynamic effects on oil price: An application on path analysis and BVAR-TVP model," Energy Policy, Elsevier, vol. 39(12), pages 8022-8036.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dai, Zhifeng & Zhu, Haoyang & Zhang, Xinhua, 2022. "Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle," Energy Economics, Elsevier, vol. 109(C).
    2. Gong, Xu & Liu, Yun & Wang, Xiong, 2021. "Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method," International Review of Financial Analysis, Elsevier, vol. 76(C).
    3. Zhu, Bo & Lin, Renda & Deng, Yuanyue & Chen, Pingshe & Chevallier, Julien, 2021. "Intersectoral systemic risk spillovers between energy and agriculture under the financial and COVID-19 crises," Economic Modelling, Elsevier, vol. 105(C).
    4. Ding, Qian & Huang, Jianbai & Chen, Jinyu, 2021. "Dynamic and frequency-domain risk spillovers among oil, gold, and foreign exchange markets: Evidence from implied volatility," Energy Economics, Elsevier, vol. 102(C).
    5. Shi, Xunpeng & Shen, Yifan, 2021. "Macroeconomic uncertainty and natural gas prices: Revisiting the Asian Premium," Energy Economics, Elsevier, vol. 94(C).
    6. Zhang, Hua & Chen, Jinyu & Shao, Liuguo, 2021. "Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19," International Review of Financial Analysis, Elsevier, vol. 77(C).
    7. Yang, Yang & Liu, Zhen & Saydaliev, Hayot Berk & Iqbal, Sajid, 2022. "Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves," Resources Policy, Elsevier, vol. 77(C).
    8. Mensi, Walid & Ziadat, Salem Adel & Rababa'a, Abdel Razzaq Al & Vo, Xuan Vinh & Kang, Sang Hoon, 2024. "Oil, gold and international stock markets: Extreme spillovers, connectedness and its determinants," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 1-17.
    9. Liu, Zhenhua & Shi, Xunpeng & Zhai, Pengxiang & Wu, Shan & Ding, Zhihua & Zhou, Yuqin, 2021. "Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach," Resources Policy, Elsevier, vol. 74(C).
    10. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    11. Zhang, Yue-Jun & Bouri, Elie & Gupta, Rangan & Ma, Shu-Jiao, 2021. "Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    12. Dai, Zhifeng & Zhu, Haoyang, 2022. "Time-varying spillover effects and investment strategies between WTI crude oil, natural gas and Chinese stock markets related to belt and road initiative," Energy Economics, Elsevier, vol. 108(C).
    13. Ali, Syed Riaz Mahmood & Mensi, Walid & Anik, Kaysul Islam & Rahman, Mishkatur & Kang, Sang Hoon, 2022. "The impacts of COVID-19 crisis on spillovers between the oil and stock markets: Evidence from the largest oil importers and exporters," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 345-372.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qing Peng & Fenghua Wen & Xu Gong, 2021. "Time‐dependent intrinsic correlation analysis of crude oil and the US dollar based on CEEMDAN," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 834-848, January.
    2. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2022. "Spillover effects between commodity and stock markets: A SDSES approach," Resources Policy, Elsevier, vol. 79(C).
    3. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    4. Zhifang He & Jiaqi Chen & Fangzhao Zhou & Guoqing Zhang & Fenghua Wen, 2022. "Oil price uncertainty and the risk‐return relation in stock markets: Evidence from oil‐importing and oil‐exporting countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1154-1172, January.
    5. Dai, Xingyu & Wang, Qunwei & Zha, Donglan & Zhou, Dequn, 2020. "Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach," Energy Economics, Elsevier, vol. 88(C).
    6. Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian & Shahzad, Syed Jawad Hussain, 2020. "The predictive power of oil price shocks on realized volatility of oil: A note," Resources Policy, Elsevier, vol. 69(C).
    7. Huang, Jionghao & Li, Ziruo & Xia, Xiaohua, 2021. "Network diffusion of international oil volatility risk in China's stock market: Quantile interconnectedness modelling and shock decomposition analysis," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 1-39.
    8. Zhang, Yue-Jun & Bouri, Elie & Gupta, Rangan & Ma, Shu-Jiao, 2021. "Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    9. Liu, Renren & Chen, Jianzhong & Wen, Fenghua, 2021. "The nonlinear effect of oil price shocks on financial stress: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    10. Mohammedi, Mustapha & Bouzebda, Salim & Laksaci, Ali, 2021. "The consistency and asymptotic normality of the kernel type expectile regression estimator for functional data," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
    11. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    12. Antonio Rubia Serrano & Lidia Sanchis-Marco, 2015. "Measuring Tail-Risk Cross-Country Exposures in the Banking Industry," Working Papers. Serie AD 2015-01, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    13. Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2024. "Testing Granger non-causality in expectiles," Econometric Reviews, Taylor & Francis Journals, vol. 43(1), pages 30-51, January.
    14. Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
    15. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    16. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    17. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    18. Zheng, Yan & Zhou, Min & Wen, Fenghua, 2021. "Asymmetric effects of oil shocks on carbon allowance price: Evidence from China," Energy Economics, Elsevier, vol. 97(C).
    19. Li, Wenlan & Cheng, Yuxiang & Fang, Qiang, 2020. "Forecast on silver futures linked with structural breaks and day-of-the-week effect," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    20. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.

    More about this item

    Keywords

    CAR-ARCHE model; EVaR; Prudence level; Time-varying downside risk spillover effect;
    All these keywords.

    JEL classification:

    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303573. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.