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Leverage Effect, Volatility Feedback, and Self-Exciting Market Disruptions

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  • Carr, Peter
  • Wu, Liuren

Abstract

Equity index volatility variation and its interaction with the index return can come from three distinct channels. First, index volatility increases with the market’s aggregate financial leverage. Second, positive shocks to systematic risk increase the cost of capital and reduce the valuation of future cash flows, generating a negative correlation between the index return and its volatility, regardless of financial leverage. Finally, large negative market disruptions show self-exciting behaviors. This article proposes a model that incorporates all three channels and examines their relative contribution to index option pricing and stock option pricing for different types of companies.

Suggested Citation

  • Carr, Peter & Wu, Liuren, 2017. "Leverage Effect, Volatility Feedback, and Self-Exciting Market Disruptions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(5), pages 2119-2156, October.
    • Handle: RePEc:cup:jfinqa:v:52:y:2017:i:05:p:2119-2156_00
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    Cited by:

    1. Peixuan Yuan, 2022. "Time-Varying Skew in VIX Derivatives Pricing," Management Science, INFORMS, vol. 68(10), pages 7761-7791, October.
    2. Yeguang Chi & Wenyan Hao, 2020. "A Horserace of Volatility Models for Cryptocurrency: Evidence from Bitcoin Spot and Option Markets," Papers 2010.07402, arXiv.org.
    3. Xinglin Yang & Ji Chen, 2021. "VIX term structure: The role of jump propagation risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 785-810, June.
    4. Carverhill, Andrew & Luo, Dan, 2023. "A Bayesian analysis of time-varying jump risk in S&P 500 returns and options," Journal of Financial Markets, Elsevier, vol. 64(C).
    5. Chi, Yeguang & Hao, Wenyan, 2021. "Volatility models for cryptocurrencies and applications in the options market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    6. Dimitrios Koutmos, 2023. "Investor sentiment and bitcoin prices," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 1-29, January.
    7. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    8. Fu, Yang & Zheng, Zeyu, 2020. "Volatility modeling and the asymmetric effect for China’s carbon trading pilot market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    9. Yeguang Chi & Wenyan Hao & Yifei Zhang, 2022. "Volatility model applications in China's SSE50 options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1704-1720, September.
    10. Du Du & Dan Luo, 2019. "The Pricing of Jump Propagation: Evidence from Spot and Options Markets," Management Science, INFORMS, vol. 67(5), pages 2360-2387, May.
    11. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    12. Ruan, Xinfeng & Zhang, Jin E., 2021. "Time-varying uncertainty and variance risk premium," Journal of Macroeconomics, Elsevier, vol. 69(C).
    13. Zhang, Chuanhai & Zhang, Zhengjun & Xu, Mengyu & Peng, Zhe, 2023. "Good and bad self-excitation: Asymmetric self-exciting jumps in Bitcoin returns," Economic Modelling, Elsevier, vol. 119(C).
    14. Shvimer, Yossi & Herbon, Avi, 2020. "Comparative empirical study of binomial call-option pricing methods using S&P 500 index data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    15. Dohyun Chun & Donggyu Kim, 2021. "State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data," Papers 2102.13404, arXiv.org.
    16. Qu, Yan & Dassios, Angelos & Zhao, Hongbiao, 2023. "Shot-noise cojumps: exact simulation and option pricing," LSE Research Online Documents on Economics 111537, London School of Economics and Political Science, LSE Library.
    17. Ballotta, Laura & Rayée, Grégory, 2022. "Smiles & smirks: Volatility and leverage by jumps," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1145-1161.
    18. Hashem Zarafat & Sascha Liebhardt & Mustafa Hakan Eratalay, 2022. "Do ESG Ratings Reduce the Asymmetry Behavior in Volatility?," JRFM, MDPI, vol. 15(8), pages 1-32, July.
    19. Zhao, Yixiu & Upreti, Vineet & Cai, Yuzhi, 2021. "Stock returns, quantile autocorrelation, and volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 73(C).
    20. Peter H. Gruber & Claudio Tebaldi & Fabio Trojani, 2021. "The Price of the Smile and Variance Risk Premia," Management Science, INFORMS, vol. 67(7), pages 4056-4074, July.
    21. Fulop, Andras & Li, Junye, 2019. "Bayesian estimation of dynamic asset pricing models with informative observations," Journal of Econometrics, Elsevier, vol. 209(1), pages 114-138.
    22. Brini, Alessio & Lenz, Jimmie, 2022. "Assessing the resiliency of investors against cryptocurrency market crashes through the leverage effect," Economics Letters, Elsevier, vol. 220(C).
    23. Hong, Hui & Bian, Zhicun & Chen, Naiwei, 2020. "Leverage effect on stochastic volatility for option pricing in Hong Kong: A simulation and empirical study," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).

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