Interaction between Oil Price and Investor Sentiment: Nonlinear Causality, Time- Varying Influence, and Asymmetric Effect

References

• Alquist, R., and L. Kilian. 2010. What do we learn from the price of crude oil futures? Journal of Applied Econometrics 25 (4):539–73. doi:10.1002/jae.1159.
   Web of Science ®Google Scholar
• Apergis, N., A. Cooray, and M. U. Rehman. 2018. Do energy prices affect US investor sentiment? Journal of Behavioral Finance 19 (2):125–40. doi:10.1080/15427560.2017.1373354.
   Web of Science ®Google Scholar
• Apergis, N., and G. Vouzavalis. 2018. Asymmetric pass through of oil prices to gasoline prices: Evidence from a new country sample. Energy Policy 114:519–28. doi:10.1016/j.enpol.2017.12.046.
   Web of Science ®Google Scholar
• Arezki, R., P. Loungani, R. van der Ploeg, and A. J. Venables. 2014. Understanding International Commodity Price Fluctuations. Journal of International Money and Finance 42:1–8. doi:10.1016/j.jimonfin.2013.08.002.
   Web of Science ®Google Scholar
• Atems, B., D. Kapper, and E. Lam. 2015. Do exchange rates respond asymmetrically to shocks in the crude oil market? Energy Economics 49:227–38. doi:10.1016/j.eneco.2015.01.027.
   Web of Science ®Google Scholar
• Atil, A., A. Lahiani, and D. K. Nguyen. 2014. Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices. Energy Policy 65:567–73. doi:10.1016/j.enpol.2013.09.064.
   Web of Science ®Google Scholar
• Awartani, B., and A. I. Maghyereh. 2013. Dynamic spillovers between oil and stock markets in the gulf cooperation council countries. Energy Economics 36:28–42. doi:10.1016/j.eneco.2012.11.024.
   Web of Science ®Google Scholar
• Baker, M., and J. Wurgler. 2006. Investor sentiment and the cross‐section of stock returns. The Journal of Finance 61 (4):1645–80. doi:10.1111/j.1540-6261.2006.00885.x.
   Web of Science ®Google Scholar
• Baker, M., and J. Wurgler. 2007. Investor sentiment in the stock market. Journal of Economic Perspectives 21 (2):129–51. doi:10.1257/jep.21.2.129.
   Web of Science ®Google Scholar
• Barber, B. M., T. Odean, and N. Zhu. 2008. Do retail trades move markets? The Review of Financial Studies 22 (1):151–86. doi:10.1093/rfs/hhn035.
   Web of Science ®Google Scholar
• Basak, S., and A. Pavlova. 2016. A model of financialization of commodities. The Journal of Finance 71 (4):1511–56. doi:10.1111/jofi.2016.71.issue-4.
   Web of Science ®Google Scholar
• Bekiros, S. D., and C. G. H. Diks. 2008. The nonlinear dynamic relationship of exchange rates: Parametric and nonparametric causality testing. Journal of Macroeconomics 30 (4):1641–50. doi:10.1016/j.jmacro.2008.04.001.
   Web of Science ®Google Scholar
• Borovkova, S. 2011. News analytics for energy futures. SSRN working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1719582/.
   Google Scholar
• Bouri, E., B. Awartani, and A. Maghyereh. 2016. Crude oil prices and sectoral stock returns in Jordan around the Arab uprisings of 2010. Energy Economics 56:205–14. doi:10.1016/j.eneco.2016.03.021.
   Web of Science ®Google Scholar
• Brown, G. W. 1999. Volatility, sentiment, and noise traders. Financial Analysts Journal 55 (2):82–90. doi:10.2469/faj.v55.n2.2263.
   Google Scholar
• Ch, C., M., . R. Gupta, N. Nkambule, and N. Tlotlego. 2012. Forecasting key macroeconomic variables of the South African economy using bayesian variable selection. Journal of Applied Sciences 12:645–52. doi:10.3923/jas.2012.645.652.
   Google Scholar
• Chau, F., R. Deesomsak, and D. Koutmos. 2016. Does investor sentiment really matter? International Review of Financial Analysis 48:221–32. doi:10.1016/j.irfa.2016.10.003.
   Web of Science ®Google Scholar
• Choi, S. 2010. Does investor’s sentiment cause market prices? A case study of the NYMEX petroleum futures markets. Journal of International Finance and Economics 10 (4):38–49.
   Google Scholar
• Choudhry, T., F. I. Papadimitriou, and S. Shabi. 2016. Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests. Journal of Banking & Finance 66:89–101. doi:10.1016/j.jbankfin.2016.02.005.
   Web of Science ®Google Scholar
• Cifarelli, G., and G. Paladino. 2010. Oil price dynamics and speculation: A multivariate financial approach. Energy Economics 32 (2):363–72. doi:10.1016/j.eneco.2009.08.014.
   Web of Science ®Google Scholar
• Cogley, T., and T. J. Sargent. 2001. Evolving post-World War II US inflation dynamics. NBER Macroeconomics Annual 16:331–73.
   Web of Science ®Google Scholar
• Cogley, T., and T. J. Sargent. 2005. Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics 8 (2):262–302. doi:10.1016/j.red.2004.10.009.
   Web of Science ®Google Scholar
• Coleman, L. 2012. Explaining crude oil prices using fundamental measures. Energy Policy 40:318–24. doi:10.1016/j.enpol.2011.10.012.
   Web of Science ®Google Scholar
• Cologni, A., and M. Manera. 2008. Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries. Energy Economics 30 (3):856–88. doi:10.1016/j.eneco.2006.11.001.
   Web of Science ®Google Scholar
• D’Agostino, A., L. Gambetti, and D. Giannone. 2013. Macroeconomic forecasting and structural change. Journal of Applied Econometrics 28 (1):82–101. doi:10.1002/jae.1257.
   Web of Science ®Google Scholar
• Da, Z., J. Engelberg, and P. Gao. 2014. The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies 28 (1):1–32. doi:10.1093/rfs/hhu072.
   Web of Science ®Google Scholar
• Dai, Z., and F. Wen. 2018a. Some improved sparse and stable portfolio optimization problems. Finance Research Letters 27:46–52. doi:10.1016/j.frl.2018.02.026.
   Web of Science ®Google Scholar
• Dai, Z., and F. Wen. 2018b. A generalized approach to sparse and stable portfolio optimization problem. Journal of Industrial & Management Optimization 14 (4):1651–66. doi:10.3934/jimo.2018025.
   Web of Science ®Google Scholar
• De Oliveira, E. M., F. Cunha, F. L. Cyrino-Oliveira, and C. P. Samanez. 2017. Dynamic relationships between crude oil prices and socially responsible investing in Brazil: Evidence for linear and non-linear causality. Applied Economics 49 (22):2125–40. doi:10.1080/00036846.2016.1234695.
   Web of Science ®Google Scholar
• Deeney, P., M. Cummins, M. Dowling, and A. Bermingham. 2015. Sentiment in oil markets. International Review of Financial Analysis 39:179–85. doi:10.1016/j.irfa.2015.01.005.
   Web of Science ®Google Scholar
• Dergiades, T., G. Martinopoulos, and L. Tsoulfidis. 2013. Energy consumption and economic growth: Parametric and non-parametric causality testing for the case of Greece. Energy Economics 36:686–97. doi:10.1016/j.eneco.2012.11.017.
   Web of Science ®Google Scholar
• Diks, C., and V. Panchenko. 2006. A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control 30 (9):1647–69. doi:10.1016/j.jedc.2005.08.008.
   Web of Science ®Google Scholar
• Ding, Z., Z. Liu, Y. Zhang, and R. Long. 2017. The contagion effect of international crude oil price fluctuations on Chinese stock market investor sentiment. Applied Energy 187:27–36. doi:10.1016/j.apenergy.2016.11.037.
   Web of Science ®Google Scholar
• Dowling, M., M. Cummins, and B. M. Lucey. 2016. Psychological barriers in oil futures markets. Energy Economics 53:293–304. doi:10.1016/j.eneco.2014.03.022.
   Web of Science ®Google Scholar
• Du, D., R. J. Gunderson, and X. Zhao. 2016. Investor sentiment and oil prices. Journal of Asset Management 17 (2):73–88. doi:10.1057/jam.2015.39.
   Web of Science ®Google Scholar
• Du, D., and X. Zhao. 2017. Financial investor sentiment and the boom/bust in oil prices during 2003–2008. Review of Quantitative Finance and Accounting 48 (2):331–61. doi:10.1007/s11156-016-0553-5.
   Web of Science ®Google Scholar
• Edelstein, P., and L. Kilian. 2009. How sensitive are consumer expenditures to retail energy prices? Journal of Monetary Economics 56 (6):766–79. doi:10.1016/j.jmoneco.2009.06.001.
   Web of Science ®Google Scholar
• Fan, Y., and J. H. Xu. 2011. What has driven oil prices since 2000? A structural change perspective. Energy Economics 33 (6):1082–94. doi:10.1016/j.eneco.2011.05.017.
   Web of Science ®Google Scholar
• Fisher, K. L., and M. Statman. 2003. Consumer confidence and stock returns. Journal of Portfolio Management 30 (1):115–27. doi:10.3905/jpm.2003.319925.
   Web of Science ®Google Scholar
• Gong, X., F. Wen, X. H. Xia, J. Huang, and B. Pan. 2017. Investigating the risk-return trade-off for crude oil futures using high-frequency data. Applied Energy 196:152–61. doi:10.1016/j.apenergy.2016.11.112.
   Web of Science ®Google Scholar
• Gong, X., F. Wen, Z. He, J. Yang, X. Yang, and B. Pan. 2016. Extreme Return, Extreme Volatility and Investor Sentiment. Filomat 30 (15):3949–61. doi:10.2298/FIL1615949G.
   Web of Science ®Google Scholar
• Granger, C. W. J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society 37 (3):424–38. doi:10.2307/1912791.
   Web of Science ®Google Scholar
• Güntner, J. H. F., and K. Linsbauer. 2018. The effects of oil supply and demand shocks on US consumer sentiment. Journal of Money, Credit and Banking 50 (7):1617–44. doi:10.1111/jmcb.12512.
   Web of Science ®Google Scholar
• Hamilton, J. D. 2009. Understanding crude oil prices. Energy Journal 30:179–206. doi:10.5547/ISSN0195-6574-EJ-Vol30-No2-9.
   Web of Science ®Google Scholar
• Hammoudeh, S., A. Lahiani, D. K. Nguyen, and R. M. Sousa. 2014. Asymmetric and nonlinear pass-through of energy prices to CO2 emission allowance prices. NIPE Working Paper Series.
   Google Scholar
• He, Z., C. Huang, X. Gong, X. Yang, and F. Wen. 2017. Do trading volume and downside trading volume help forecast the downside risk? EURASIA Journal of Mathematics, Science and Technology Education 13 (12):8367–82.
   Web of Science ®Google Scholar
• He, Z., and F. Zhou. 2018. Time-varying and asymmetric effects of the oil-specific demand shock on investor sentiment. PloS One 13 (8):e0200734. doi:10.1371/journal.pone.0200734.
   PubMed Web of Science ®Google Scholar
• He, Z., L. He, and F. Wen. 2019. Risk compensation and market returns: The role of investor sentiment in the stock market. Emerging Markets Finance and Trade 55 (3):704–18. doi:10.1080/1540496X.2018.1460724.
   Web of Science ®Google Scholar
• Hiemstra, C., and J. D. Jones. 1994. Testing for linear and nonlinear Granger causality in the stock price‐volume relation. The Journal of Finance 49 (5):1639–64.
   Web of Science ®Google Scholar
• Hu, C., X. Liu, B. Pan, B. Chen, and X. Xia. 2018. Asymmetric impact of oil price shock on stock market in China: A combination analysis based on SVAR model and NARDL model. Emerging Markets Finance and Trade 54 (8):1693–705. doi:10.1080/1540496X.2017.1412303.
   Web of Science ®Google Scholar
• Irwin, S. H., and D. R. Sanders. 2011. Index funds, financialization, and commodity futures markets. Applied Economic Perspectives and Policy 33 (1):1–31. doi:10.1093/aepp/ppq032.
   Web of Science ®Google Scholar
• Ji, Q., and Y. Fan. 2016. Modelling the joint dynamics of oil prices and investor fear gauge. Research in International Business and Finance 37:242–51. doi:10.1016/j.ribaf.2015.11.016.
   Web of Science ®Google Scholar
• Joëts, M., V. Mignon, and T. Razafindrabe. 2017. Does the volatility of commodity prices reflect macroeconomic uncertainty? Energy Economics 68:313–26. doi:10.1016/j.eneco.2017.09.017.
   Web of Science ®Google Scholar
• Katrakilidis, C., and E. Trachanas. 2012. What drives housing price dynamics in Greece: New evidence from asymmetric ARDL cointegration. Economic Modelling 29 (4):1064–69. doi:10.1016/j.econmod.2012.03.029.
   Web of Science ®Google Scholar
• Kaufmann, R. K. 2011. The role of market fundamentals and speculation in recent price changes for crude oil. Energy Policy 39 (1):105–15. doi:10.1016/j.enpol.2010.09.018.
   Web of Science ®Google Scholar
• Kilian, L., and C. Park. 2009. The impact of oil price shocks on the US stock market. International Economic Review 50 (4):1267–87. doi:10.1111/iere.2009.50.issue-4.
   Web of Science ®Google Scholar
• Kling, G., and L. Gao. 2008. Chinese institutional investors’ sentiment. Journal of International Financial Markets, Institutions and Money 18 (4):374–87. doi:10.1016/j.intfin.2007.04.002.
   Google Scholar
• Kumar, A. 2009. Who gambles in the stock market? The Journal of Finance 64 (4):1889–933. doi:10.1111/j.1540-6261.2009.01483.x.
   Web of Science ®Google Scholar
• Kumar, M. 2010. A time-varying parameter vector autoregression model for forecasting emerging market exchange rates. International Journal of Business and Economic Sciences Applied Research (IJBESAR) 3 (2):21–39.
   Google Scholar
• Kumar, S. 2017. On the nonlinear relation between crude oil and gold. Resources Policy 51:219–24. doi:10.1016/j.resourpol.2017.01.003.
   Web of Science ®Google Scholar
• Lechthaler, F., and L. Leinert. 2012. Moody oil: What is driving the crude oil price? CERETH Economic Working Paper No. 12/168. Accessed October 15. https://ssrn.com/abstract=2161816/.
   Google Scholar
• Li, X., J. Ma, S. Wang, and X. Zhang. 2015. How does Google search affect trader positions and crude oil prices? Economic Modelling 49:162–71. doi:10.1016/j.econmod.2015.04.005.
   Web of Science ®Google Scholar
• Ludvigson, S. C. 2004. Consumer confidence and consumer spending. The Journal of Economic Perspectives 18 (2):29–50. doi:10.1257/0895330041371222.
   Web of Science ®Google Scholar
• Maslyuk-Escobedo, S., K. Rotaru, and A. Dokumentov. 2016. News sentiment and jumps in energy spot and futures markets. Pacific-Basin Finance Journal 45:186–210. doi:10.1016/j.pacfin.2016.07.001.
   Web of Science ®Google Scholar
• Nakajima, J. 2011. Time-varying parameter VAR model with stochastic volatility: An overview of methodology and empirical applications. Discussion Paper Series no. 2011-E-9, Institute for Monetary and Economic Studies, Bank of Japan. www.imes.boj. or.jp/research/papers/english/11-E-09.pdf.
   Google Scholar
• Nakajima, J., M. Kasuya, and T. Watanabe. 2011. Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy. Journal of the Japanese and International Economies 25 (3):225–45. doi:10.1016/j.jjie.2011.07.004.
   Web of Science ®Google Scholar
• Nazlioglu, S. 2011. World oil and agricultural commodity prices: Evidence from nonlinear causality. Energy Policy 39 (5):2935–43. doi:10.1016/j.enpol.2011.03.001.
   Web of Science ®Google Scholar
• Pesaran, M. H., Y. Shin, and R. J. Smith. 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16:289–326. doi:10.1002/(ISSN)1099-1255.
   Web of Science ®Google Scholar
• Pesaran, M. H., Y. Shin, and R. P. Smith. 1999. Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association 94 (446):621–34. doi:10.1080/01621459.1999.10474156.
   Web of Science ®Google Scholar
• Primiceri, G. E. 2005. Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies 72 (3):821–52. doi:10.1111/j.1467-937X.2005.00353.x.
   Web of Science ®Google Scholar
• Qadan, M., and H. Nama. 2018. Investor sentiment and the price of oil. Energy Economics 69:42–58. doi:10.1016/j.eneco.2017.10.035.
   Web of Science ®Google Scholar
• Ratti, R. A., and J. L. Vespignani. 2016. Oil prices and global factor macroeconomic variables. Energy Economics 59:198–212. doi:10.1016/j.eneco.2016.06.002.
   Web of Science ®Google Scholar
• Reboredo, J. C., M. A. Rivera-Castro, and G. F. Zebende. 2014. Oil and US dollar exchange rate dependence: A detrended cross-correlation approach. Energy Economics 42:132–39. doi:10.1016/j.eneco.2013.12.008.
   Web of Science ®Google Scholar
• Sanzo, S. D., and M. Bella. 2015. Public debt and growth in the euro area: Evidence from parametric and nonparametric Granger causality. The BE Journal of Macroeconomics 15 (2):631–48.
   Web of Science ®Google Scholar
• Shahzad, S. J. H., S. M. Nor, R. Ferrer, and S. Hammoudeh. 2017. Asymmetric determinants of CDS spreads: US industry-level evidence through the NARDL approach. Economic Modelling 60:211–30. doi:10.1016/j.econmod.2016.09.003.
   Web of Science ®Google Scholar
• Shin, Y., B. Yu, and M. Greenwood-Nimmo. 2014. Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Sickles R., Horrace W. (eds), Festschrift in Honor of Peter Schmidt (pp. 281–314). New York: Springer. doi:10.1007/978-1-4899-8008-3.
   Google Scholar
• Sodeyfi, S., and S. Katircioglu. 2016. Interactions between business conditions, economic growth and crude oil prices. Economic Research-Ekonomska Istraživanja 29 (1):980–90. doi:10.1080/1331677X.2016.1235504.
   Web of Science ®Google Scholar
• Sun, L., M. Najand, and J. Shen. 2016. Stock return predictability and investor sentiment: A high-frequency perspective. Journal of Banking & Finance 73:147–64. doi:10.1016/j.jbankfin.2016.09.010.
   Web of Science ®Google Scholar
• Tang, K., and W. Xiong. 2012. Index investment and the financialization of commodities. Financial Analysts Journal 68 (6):54–74. doi:10.2469/faj.v68.n6.5.
   Web of Science ®Google Scholar
• Verma, R., and P. Verma. 2008. Are survey forecasts of individual and institutional investor sentiments rational? International Review of Financial Analysis 17 (5):1139–55. doi:10.1016/j.irfa.2007.04.001.
   Google Scholar
• Wang, Y. H., A. Keswani, and S. J. Taylor. 2006. The relationships between sentiment, returns and volatility. International Journal of Forecasting 22 (1):109–23. doi:10.1016/j.ijforecast.2005.04.019.
   Web of Science ®Google Scholar
• Wen, F., C. Zhao, and C. Hu. 2018c. Time-varying effects of international copper price shocks on China’s producer price index. Resources Policy. in press. doi:10.1016/j.resourpol.2018.10.006.
   Web of Science ®Google Scholar
• Wen, F., F. Min, Y. J. Zhang, and Y. Can. 2018a. Crude oil price shocks, monetary policy, and China’s economy. International Journal of Finance & Economics 1–16. doi:10.1002/ijfe.1692.
   Web of Science ®Google Scholar
• Wen, F., J. Xiao, C. Huang, and X. Xia. 2018b. Interaction between oil and US dollar exchange rate: Nonlinear causality, time-varying influence and structural breaks in volatility. Applied Economics 50 (3):319–34. doi:10.1080/00036846.2017.1321838.
   Web of Science ®Google Scholar
• Wen, F., J. Xiao, X. Xia, B. Chen, Z. Xiao, and J. Li. 2019a. Oil prices and chinese stock market: Nonlinear causality and volatility persistence. Emerging Markets Finance and Trade 55 (6):1247–63. doi:10.1080/1540496X.2018.1496078.
   Web of Science ®Google Scholar
• Wen, F., X. Gong, and S. Cai. 2016. Forecasting the volatility of crude oil futures using HAR-type models with structural breaks. Energy Economics 59:400–13. doi:10.1016/j.eneco.2016.07.014.
   Web of Science ®Google Scholar
• Wen, F., X. Yang, and W. X. Zhou. 2019b. Tail dependence networks of global stock markets. International Journal of Finance & Economics 24 (1):558–67. doi:10.1002/ijfe.1679.
   Web of Science ®Google Scholar
• Wen, F., Y. P. Zhao, M. Zhang, and C. Hu. 2019c. Forecasting realized volatility of crude oil futures with equity market uncertainty. Applied Economics 1–17. doi:10.1080/00036846.2019.1619023.
   Web of Science ®Google Scholar
• Wen, F., Z. He, X. Gong, and A. Liu. 2014b. Investors’ risk preference characteristics based on different reference point. Discrete Dynamics in Nature and Society 2014:1–9. doi:10.1155/2014/158386.
   Web of Science ®Google Scholar
• Wen, F., Z. He, Z. Dai, and X. Yang. 2014a. Characteristics of investors’ risk preference for stock markets. Economic Computation and Economic Cybernetics Studies and Research 48 (3):235–54.
   Web of Science ®Google Scholar
• Xiao, J., C. Hu, G. Ouyang, and F. Wen. 2019. Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach. Energy Economics 80:297–309. doi:10.1016/j.eneco.2019.01.016.
   Web of Science ®Google Scholar
• Xiao, J., M. Zhou, F. Wen, and F. Wen. 2018a. Asymmetric impacts of oil price uncertainty on Chinese stock returns under different market conditions: Evidence from oil volatility index. Energy Economics 74:777–86. doi:10.1016/j.eneco.2018.07.026.
   Web of Science ®Google Scholar
• Xiao, J., X. Zhu, C. Huang, X. Yang, F. Wen, and M. Zhong. 2018b. A new approach for stock price analysis and prediction based on SSA and SVM. International Journal of Information Technology & Decision Making 1–24. doi:10.1142/S021962201841002X.
   Web of Science ®Google Scholar
• Yu, J., and Y. Yuan. 2011. Investor sentiment and the mean–Variance relation. Journal of Financial Economics 100 (2):367–81. doi:10.1016/j.jfineco.2010.10.011.
   Web of Science ®Google Scholar
• Zhang, Y. J., and Y. M. Wei. 2010. The crude oil market and the gold market: Evidence for cointegration, causality and price discovery. Resources Policy 35 (3):168–77. doi:10.1016/j.resourpol.2010.05.003.
   Web of Science ®Google Scholar
• Zhang, Y. J., and Y. M. Wei. 2011. The dynamic influence of advanced stock market risk on international crude oil returns: An empirical analysis. Quantitative Finance 11 (7):967–78. doi:10.1080/14697688.2010.538712.
   Web of Science ®Google Scholar