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Does bid-ask spread explains the smile? On DVF and DML

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  • Li, Pengshi
  • Lin, Yan
  • Yu, Xing
  • Liu, Guifang

Abstract

In this paper, we investigate the potential effect of the bid-ask spread on pricing and implied volatilities of the newly established CSI 300 index options in China. We use the deterministic volatility function (DVF) to analyze the pricing errors and employ the double machine learning (DML) technique to evaluate the effect of liquidity costs on implied volatility in the presence of economic confounders. Our research shows that the deterministic volatility function modified to incorporate the bid-ask spread work better than the Black-Scholes model. And a sizable and statistically liquidity costs effect on implied volatility is observed in the CSI 300 options market.

Suggested Citation

  • Li, Pengshi & Lin, Yan & Yu, Xing & Liu, Guifang, 2025. "Does bid-ask spread explains the smile? On DVF and DML," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
  • Handle: RePEc:eee:pacfin:v:90:y:2025:i:c:s0927538x24003974
    DOI: 10.1016/j.pacfin.2024.102645
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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    3. Sol Kim, 2009. "The performance of traders' rules in options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(11), pages 999-1020, November.
    4. Longstaff, Francis A, 1995. "Option Pricing and the Martingale Restriction," The Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 1091-1124.
    5. Da Dong & Qingfu Liu & Pingping Tao & Zhiliang Ying, 2021. "The pricing mechanism between ETF option and spot markets in China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1286-1300, August.
    6. Hansen, Jacob H. & Siggaard, Mathias V., 2024. "Double Machine Learning: Explaining the Post-Earnings Announcement Drift," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 59(3), pages 1003-1030, May.
    7. Engstrom, Malin, 2002. "Do Swedes smile? On implied volatility functions," Journal of Multinational Financial Management, Elsevier, vol. 12(4-5), pages 285-304.
    8. Jay Cao & Jacky Chen & John Hull, 2020. "A neural network approach to understanding implied volatility movements," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1405-1413, September.
    9. Niu, Jing & Ma, Chao & Wang, Yunpeng & Chang, Chun-Ping & Wang, Haijie, 2022. "The pricing of China stock index options based on monetary policy uncertainty," Journal of Asian Economics, Elsevier, vol. 81(C).
    10. Christoffersen, Peter & Jacobs, Kris, 2004. "The importance of the loss function in option valuation," Journal of Financial Economics, Elsevier, vol. 72(2), pages 291-318, May.
    11. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
    12. Fan, Qingqian & Feng, Sixian, 2022. "An empirical study on the characterization of implied volatility and pricing in the Chinese option market," Finance Research Letters, Elsevier, vol. 49(C).
    13. Bates, David S, 1996. "Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options," The Review of Financial Studies, Society for Financial Studies, vol. 9(1), pages 69-107.
    14. Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing Options and Computing Implied Volatilities using Neural Networks," Risks, MDPI, vol. 7(1), pages 1-22, February.
    15. Damiano Brigo & Xiaoshan Huang & Andrea Pallavicini & Haitz Saez de Ocariz Borde, 2021. "Interpretability in deep learning for finance: a case study for the Heston model," Papers 2104.09476, arXiv.org.
    16. 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.
    17. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    18. Ignacio Peña & Gonzalo Rubio & Gregorio Serna, 2001. "Smiles, Bid‐ask Spreads and Option Pricing," European Financial Management, European Financial Management Association, vol. 7(3), pages 351-374, September.
    19. Mark Rubinstein., 1994. "Implied Binomial Trees," Research Program in Finance Working Papers RPF-232, University of California at Berkeley.
    20. Scott, Louis O., 1987. "Option Pricing when the Variance Changes Randomly: Theory, Estimation, and an Application," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(4), pages 419-438, December.
    21. Pena, Ignacio & Rubio, Gonzalo & Serna, Gregorio, 1999. "Why do we smile? On the determinants of the implied volatility function," Journal of Banking & Finance, Elsevier, vol. 23(8), pages 1151-1179, August.
    22. Panayiotis Andreou & Chris Charalambous & Spiros Martzoukos, 2014. "Assessing the performance of symmetric and asymmetric implied volatility functions," Review of Quantitative Finance and Accounting, Springer, vol. 42(3), pages 373-397, April.
    23. Yue, Tian & Zhang, Jin E. & Tan, Eric K.M., 2020. "The Chinese equity index options market," Emerging Markets Review, Elsevier, vol. 45(C).
    24. Lara Marie Demajo & Vince Vella & Alexiei Dingli, 2020. "Explainable AI for Interpretable Credit Scoring," Papers 2012.03749, arXiv.org.
    25. Peter Christoffersen & Steven Heston & Kris Jacobs, 2009. "The Shape and Term Structure of the Index Option Smirk: Why Multifactor Stochastic Volatility Models Work So Well," Management Science, INFORMS, vol. 55(12), pages 1914-1932, December.
    26. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    27. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    28. Brandt, Michael W. & Wu, Tao, 2002. "Cross-sectional tests of deterministic volatility functions," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 525-550, December.
    29. Robert JARROW & Andrew RUDD, 2008. "Approximate Option Valuation For Arbitrary Stochastic Processes," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 1, pages 9-31, World Scientific Publishing Co. Pte. Ltd..
    30. Wiggins, James B., 1987. "Option values under stochastic volatility: Theory and empirical estimates," Journal of Financial Economics, Elsevier, vol. 19(2), pages 351-372, December.
    31. Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.
    32. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2022. "Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 288-303.
    33. Li, Pengshi & Xian, Aichuan & Lin, Yan, 2021. "What determines volatility smile in China?," Economic Modelling, Elsevier, vol. 96(C), pages 326-335.
    34. 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.
    35. repec:bla:jfinan:v:53:y:1998:i:6:p:2059-2106 is not listed on IDEAS
    36. Zhang, Huiming & Watada, Junzo, 2019. "An analysis of the arbitrage efficiency of the Chinese SSE 50ETF options market," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 474-489.
    37. Rubinstein, Mark, 1994. "Implied Binomial Trees," Journal of Finance, American Finance Association, vol. 49(3), pages 771-818, July.
    38. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    39. Guifang Liu & Weijun Xu, 2017. "Application of Heston’s Model to the Chinese Stock Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(8), pages 1749-1763, August.
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