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How do market volatility and risk aversion sentiment inter-influence over time? Evidence from Chinese SSE 50 ETF options

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  • Gong, Jue
  • Wang, Gang-Jin
  • Xie, Chi
  • Uddin, Gazi Salah

Abstract

We propose to implement the TVP-VAR-SV model and the spillover index approach to investigate the dynamics of mutual influences between market volatility and risk aversion sentiment (represented by variance risk premium), from aspects of realized variance (RV) and implied variance (IV). We innovatively study the evolution of volatility information transmission mechanisms by analyzing time-varying impulse response and dynamic connectedness in the Chinese market. Upon inspecting shock effects from investor sentiment to RV and IV, we discover that investor sentiment can cause sudden shocks on realized volatility but produce expansive shocks on expected volatility. As we compare spillover effects in call and put options, we find that (i) sentiments in put option contain more volatility information and can affect market volatility, while sentiments in call option primarily perceive volatility spillovers from market volatility, and (ii) investor sentiments in call (put) option become to present more volatility information in a bullish (bearish) market. Our study provides valuable insights on behavioral finance theory and portfolio risk management.

Suggested Citation

  • Gong, Jue & Wang, Gang-Jin & Xie, Chi & Uddin, Gazi Salah, 2024. "How do market volatility and risk aversion sentiment inter-influence over time? Evidence from Chinese SSE 50 ETF options," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    • Handle: RePEc:eee:finana:v:95:y:2024:i:pb:s1057521924003727
    DOI: 10.1016/j.irfa.2024.103440
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    as
    1. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    2. Jebabli, Ikram & Arouri, Mohamed & Teulon, Frédéric, 2014. "On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility," Energy Economics, Elsevier, vol. 45(C), pages 66-98.
    3. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022. "Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
    4. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    5. Tim Bollerslev & George Tauchen & Hao Zhou, 2009. "Expected Stock Returns and Variance Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4463-4492, November.
    6. Rama Cont & Jose da Fonseca, 2002. "Dynamics of implied volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 2(1), pages 45-60.
    7. Pan, Jun, 2002. "The jump-risk premia implicit in options: evidence from an integrated time-series study," Journal of Financial Economics, Elsevier, vol. 63(1), pages 3-50, January.
    8. Bollerslev, Tim & Gibson, Michael & Zhou, Hao, 2011. "Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities," Journal of Econometrics, Elsevier, vol. 160(1), pages 235-245, January.
    9. Bent Jesper Christensen & Charlotte Strunk Hansen, 2002. "New evidence on the implied-realized volatility relation," The European Journal of Finance, Taylor & Francis Journals, vol. 8(2), pages 187-205, June.
    10. Bollerslev, Tim & Marrone, James & Xu, Lai & Zhou, Hao, 2014. "Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(3), pages 633-661, June.
    11. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 29, pages 107-142, November.
    12. Hattori, Masazumi & Shim, Ilhyock & Sugihara, Yoshihiko, 2021. "Cross-stock market spillovers through variance risk premiums and equity flows," Journal of International Money and Finance, Elsevier, vol. 119(C).
    13. Ceylan, Özcan, 2021. "Time-varying risk aversion and its macroeconomic and financial determinants - A comparative analysis in the U.S. and French financial markets," Finance Research Letters, Elsevier, vol. 41(C).
    14. Jiang, Shangrong & Li, Yuze & Lu, Quanying & Wang, Shouyang & Wei, Yunjie, 2022. "Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    15. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    16. Gang-Jin Wang & Shuyue Yi & Chi Xie & H. Eugene Stanley, 2021. "Multilayer information spillover networks: measuring interconnectedness of financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 21(7), pages 1163-1185, July.
    17. Hui-Chu Shu & Jung-Hsien Chang, 2015. "Investor Sentiment and Financial Market Volatility," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 16(3), pages 206-219, July.
    18. Yang, Chunpeng & Zhou, Liyun, 2015. "Investor trading behavior, investor sentiment and asset prices," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 42-62.
    19. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    20. Londono, Juan M. & Zhou, Hao, 2017. "Variance risk premiums and the forward premium puzzle," Journal of Financial Economics, Elsevier, vol. 124(2), pages 415-440.
    21. Wang, Gang-Jin & Wan, Li & Feng, Yusen & Xie, Chi & Uddin, Gazi Salah & Zhu, You, 2023. "Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    22. 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).
    23. Ahmed Mohamed Dahir & Fauziah Mahat & Bany-Ariffin Amin Noordin & Nazrul Hisyam Ab Razak, 2020. "Dynamic connectedness between Bitcoin and equity market information across BRICS countries," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 16(3), pages 357-371, January.
    24. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    25. Tiantian Liu & Shigeyuki Hamori, 2021. "Does Investor Sentiment Affect Clean Energy Stock? Evidence from TVP-VAR-Based Connectedness Approach," Energies, MDPI, vol. 14(12), pages 1-21, June.
    26. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    27. Oleg Bondarenko, 2014. "Why Are Put Options So Expensive?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 1-50.
    28. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    29. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
    30. Jerome Yen & Bangren Chen & KangZhang Wu & Joseph Yen, 2021. "Fast generation of implied volatility surface: Optimize the traditional numerical analysis and machine learning," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 1-24, June.
    31. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    32. L.A. Smales, 2017. "The importance of fear: investor sentiment and stock market returns," Applied Economics, Taylor & Francis Journals, vol. 49(34), pages 3395-3421, July.
    33. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    34. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    35. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).
    36. Ahmed Mohamed Dahir & Fauziah Mahat & Bany-Ariffin Amin Noordin & Nazrul Hisyam Ab Razak, 2020. "Dynamic connectedness between Bitcoin and equity market information across BRICS countries," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 16(3), pages 357-371, January.
    37. Wu, Lingke & Liu, Dehong & Yuan, Jianglei & Huang, Zhenhuan, 2022. "Implied volatility information of Chinese SSE 50 ETF options," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 609-624.
    38. Chunpeng Yang & Bin Gao & Jianlei Yang, 2016. "Option pricing model with sentiment," Review of Derivatives Research, Springer, vol. 19(2), pages 147-164, July.
    39. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    40. Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Long, Jian-You & Zhou, Yang, 2023. "Forecasting stock market volatility under parameter and model uncertainty," Research in International Business and Finance, Elsevier, vol. 66(C).
    41. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    42. Ederington, Louis H. & Guan, Wei, 2010. "How asymmetric is U.S. stock market volatility?," Journal of Financial Markets, Elsevier, vol. 13(2), pages 225-248, May.
    43. Long, Wen & Zhao, Manyi & Tang, Yeran, 2021. "Can the Chinese volatility index reflect investor sentiment?," International Review of Financial Analysis, Elsevier, vol. 73(C).
    44. George Loewenstein, 2000. "Emotions in Economic Theory and Economic Behavior," American Economic Review, American Economic Association, vol. 90(2), pages 426-432, May.
    45. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    46. Zhang, Hongliang & Wang, Mengying & Jiang, Jie, 2017. "Investor protection and stock crash risk," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 256-266.
    47. Charles J. Corrado & Thomas W. Miller, 2006. "Estimating Expected Excess Returns Using Historical And Option‐Implied Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 29(1), pages 95-112, March.
    48. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    49. Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Zhu, You & Xie, Chi & Foglia, Matteo, 2023. "Spreading of cross-market volatility information: Evidence from multiplex network analysis of volatility spillovers," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    50. Donders, Monique W. M. & Vorst, Ton C. F., 1996. "The impact of firm specific news on implied volatilities," Journal of Banking & Finance, Elsevier, vol. 20(9), pages 1447-1461, November.
    51. Wang, Gang-Jin & Xiong, Lu & Zhu, You & Xie, Chi & Foglia, Matteo, 2022. "Multilayer network analysis of investor sentiment and stock returns," Research in International Business and Finance, Elsevier, vol. 62(C).
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    More about this item

    Keywords

    Market volatility; Risk aversion sentiment; Investor sentiment; Dynamic volatility spillover; Variance risk premium;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • G40 - Financial Economics - - Behavioral Finance - - - General

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