IDEAS home Printed from https://ideas.repec.org/a/spr/jeicoo/v15y2020i3d10.1007_s11403-019-00259-0.html
   My bibliography  Save this article

Tradability, closeness to market prices, and expected profit: their measurement for a binomial model of options pricing in a heterogeneous market

Author

Listed:
  • Yossi Shvimer

    (Bar-Ilan University)

  • Avi Herbon

    (Bar-Ilan University)

Abstract

A reliable method of options pricing in real time would help various players, including hedgers and speculators, to make informed decisions. In this study, we develop an extensive simulation with multiple business environments, which includes the use of real data from the S&P 500 Index between the years 2010–2017 for the 30 days prior to expiration of the options. Forecasted tradability is computed based on the SH model: a theoretical model of real-time options pricing that takes into account players’ heterogeneity with regard to their willingness to accept offers proposed by the opposing player. The quality of the model is examined for the scenario in which the model players are speculators who act against the real market prices. We show that the equilibrium prices predicted by the SH model are close to the market prices (a deviation of up to approx. 3%) in an In-The-Money environment. Additionally, the tougher the players (i.e., the greater their level of unwillingness to accept a bid from the opposing player), the higher the average tradability. We also find that the level of willingness of the players has a greater effect on tradability than does option moneyness or the market trend.

Suggested Citation

  • Yossi Shvimer & Avi Herbon, 2020. "Tradability, closeness to market prices, and expected profit: their measurement for a binomial model of options pricing in a heterogeneous market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(3), pages 737-762, July.
    • Handle: RePEc:spr:jeicoo:v:15:y:2020:i:3:d:10.1007_s11403-019-00259-0
    DOI: 10.1007/s11403-019-00259-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11403-019-00259-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11403-019-00259-0?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. León, à ngel & Mencía, Javier & Sentana, Enrique, 2009. "Parametric Properties of Semi-Nonparametric Distributions, with Applications to Option Valuation," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 176-192.
    2. U. Çetin & R. Jarrow & P. Protter & M. Warachka, 2008. "Pricing Options in an Extended Black Scholes Economy with Illiquidity: Theory and Empirical Evidence," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 9, pages 185-221, World Scientific Publishing Co. Pte. Ltd..
    3. Zhenxi Chen & Thomas Lux, 2018. "Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 711-744, October.
    4. James S. Doran & Andy Fodor & Kevin Krieger, 2010. "Option Market Efficiency and Analyst Recommendations," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(5-6), pages 560-590.
    5. Rosenberg, Joshua V. & Engle, Robert F., 2002. "Empirical pricing kernels," Journal of Financial Economics, Elsevier, vol. 64(3), pages 341-372, June.
    6. repec:bla:jfinan:v:59:y:2004:i:2:p:711-753 is not listed on IDEAS
    7. Xiaoyan Ni, Sophie & Pearson, Neil D. & Poteshman, Allen M., 2005. "Stock price clustering on option expiration dates," Journal of Financial Economics, Elsevier, vol. 78(1), pages 49-87, October.
    8. Robin K. Chou & San‐Lin Chung & Yu‐Jen Hsiao & Yaw‐Huei Wang, 2011. "The impact of liquidity on option prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(12), pages 1116-1141, December.
    9. James S. Doran & Andy Fodor & Kevin Krieger, 2010. "Option Market Efficiency and Analyst Recommendations," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(5‐6), pages 560-590, June.
    10. Young-Hye Cho & Robert F. Engle, 1999. "Time-Varying Betas and Asymmetric Effect of News: Empirical Analysis of Blue Chip Stocks," NBER Working Papers 7330, National Bureau of Economic Research, Inc.
    11. Marco Avellaneda & Michael Lipkin, 2003. "A market-induced mechanism for stock pinning," Quantitative Finance, Taylor & Francis Journals, vol. 3(6), pages 417-425.
    12. 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.
    13. Olivier Compte & Philippe Jehiel, 2004. "The Wait-and-See Option in Ascending Price Auctions," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 494-503, 04/05.
    14. Fedenia, Mark & Grammatikos, Theoharry, 1992. "Options Trading and the Bid-Ask Spread of the Underlying Stocks," The Journal of Business, University of Chicago Press, vol. 65(3), pages 335-351, July.
    15. Golez, Benjamin & Jackwerth, Jens Carsten, 2012. "Pinning in the S&P 500 futures," Journal of Financial Economics, Elsevier, vol. 106(3), pages 566-585.
    16. Jang, H. & Lee, J., 2019. "Machine learning versus econometric jump models in predictability and domain adaptability of index options," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 74-86.
    17. Andrea Buraschi & Alexei Jiltsov, 2006. "Model Uncertainty and Option Markets with Heterogeneous Beliefs," Journal of Finance, American Finance Association, vol. 61(6), pages 2841-2897, December.
    18. Charilaos E. Linaras & George Skiadopoulos, 2005. "Implied Volatility Trees And Pricing Performance: Evidence From The S&P 100 Options," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(08), pages 1085-1106.
    19. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    Full references (including those not matched with items on IDEAS)

    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. Fabozzi, Frank J. & Paletta, Tommaso & Stanescu, Silvia & Tunaru, Radu, 2016. "An improved method for pricing and hedging long dated American options," European Journal of Operational Research, Elsevier, vol. 254(2), pages 656-666.
    2. Bogdan Batrinca & Christian W. Hesse & Philip C. Treleaven, 2020. "Expiration day effects on European trading volumes," Empirical Economics, Springer, vol. 58(4), pages 1603-1638, April.
    3. Lakshithe Wagalath, 2016. "Feedback effects and endogenous risk in financial markets," Finance, Presses universitaires de Grenoble, vol. 37(2), pages 39-74.
    4. Stivers, Chris & Sun, Licheng, 2013. "Returns and option activity over the option-expiration week for S&P 100 stocks," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4226-4240.
    5. Brøgger, Søren Bundgaard, 2022. "Dynamic risk management and asset comovement," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 60-77.
    6. Moriggia, V. & Muzzioli, S. & Torricelli, C., 2009. "On the no-arbitrage condition in option implied trees," European Journal of Operational Research, Elsevier, vol. 193(1), pages 212-221, February.
    7. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    8. Sheridan Titman & Chishen Wei. Wei & Bin Zhao, 2021. "Corporate Actions and the Manipulation of Retail Investors in China: An Analysis of Stock Splits," NBER Working Papers 29212, National Bureau of Economic Research, Inc.
    9. Peter Christoffersen & Ruslan Goyenko & Kris Jacobs & Mehdi Karoui, 2018. "Illiquidity Premia in the Equity Options Market," The Review of Financial Studies, Society for Financial Studies, vol. 31(3), pages 811-851.
    10. Lin, Zih-Ying & Chang, Chuang-Chang & Wang, Yaw-Huei, 2018. "The impacts of asymmetric information and short sales on the illiquidity risk premium in the stock option market," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 152-165.
    11. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    12. Choy, Siu Kai & Wei, Jason, 2020. "Liquidity risk and expected option returns," Journal of Banking & Finance, Elsevier, vol. 111(C).
    13. Leippold, Markus & Schärer, Steven, 2017. "Discrete-time option pricing with stochastic liquidity," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 1-16.
    14. Anders Merrild Posselt, 2022. "Dynamics in the VIX complex," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1665-1687, September.
    15. Elyas Elyasiani & Silvia Muzzioli & Alessio Ruggieri, 2016. "Forecasting and pricing powers of option-implied tree models: Tranquil and volatile market conditions," Department of Economics 0099, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    16. Benzoni, Luca & Collin-Dufresne, Pierre & Goldstein, Robert S., 2011. "Explaining asset pricing puzzles associated with the 1987 market crash," Journal of Financial Economics, Elsevier, vol. 101(3), pages 552-573, September.
    17. Blasco, N. & Corredor, P. & Satrústegui, N., 2023. "Is there an expiration effect in the bitcoin market?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 647-663.
    18. Marc Jeannin & Giulia Iori & David Samuel, 2008. "Modeling stock pinning," Quantitative Finance, Taylor & Francis Journals, vol. 8(8), pages 823-831.
    19. Ramachandran, Lakshmi Shankar & Tayal, Jitendra, 2021. "Mispricing, short-sale constraints, and the cross-section of option returns," Journal of Financial Economics, Elsevier, vol. 141(1), pages 297-321.
    20. Lei Shi, 2010. "Portfolio Analysis and Equilibrium Asset Pricing with Heterogeneous Beliefs," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 9, July-Dece.

    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:spr:jeicoo:v:15:y:2020:i:3:d:10.1007_s11403-019-00259-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.