IDEAS home Printed from https://ideas.repec.org/a/eee/mateco/v47y2011i6p689-697.html
   My bibliography  Save this article

Measure preserving derivatives and the pricing kernel puzzle

Author

Listed:
  • Beare, Brendan K.

Abstract

Recent empirical studies have found evidence of nonmonotonicity in the pricing kernels for a variety of market indices. This phenomenon is known as the pricing kernel puzzle. The payoff distribution pricing model of Dybvig predicts that the payoff distribution of a direct investment of $1 in a market index may be replicated by investing less than $1 in some derivative written on that market index whenever the associated pricing kernel is nondecreasing. Using the Hardy–Littlewood rearrangement inequality, we obtain an explicit solution for the cheapest replicating derivative, which we refer to as the optimal measure preserving derivative. The optimal measure preserving derivative is the permutation appearing in Ryff’s decomposition of the pricing kernel with respect to the market payoff measure. We compute optimal measure preserving derivatives corresponding to the estimated physical and risk neutral distributions in the paper by Jackwerth (2000) that first brought attention to the pricing kernel puzzle.

Suggested Citation

  • Beare, Brendan K., 2011. "Measure preserving derivatives and the pricing kernel puzzle," Journal of Mathematical Economics, Elsevier, vol. 47(6), pages 689-697.
  • Handle: RePEc:eee:mateco:v:47:y:2011:i:6:p:689-697
    DOI: 10.1016/j.jmateco.2011.09.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304406811000863
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jmateco.2011.09.005?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. Marc Rieger, 2011. "Co-monotonicity of optimal investments and the design of structured financial products," Finance and Stochastics, Springer, vol. 15(1), pages 27-55, January.
    2. Ait-Sahalia, Yacine & Lo, Andrew W., 2000. "Nonparametric risk management and implied risk aversion," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 9-51.
    3. Dybvig, Philip H, 1988. "Distributional Analysis of Portfolio Choice," The Journal of Business, University of Chicago Press, vol. 61(3), pages 369-393, July.
    4. Fousseni Chabi-Yo & René Garcia & Eric Renault, 2008. "State Dependence Can Explain the Risk Aversion Puzzle," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 973-1011, April.
    5. Philip H. Dybvig, 1988. "Inefficient Dynamic Portfolio Strategies or How to Throw Away a Million Dollars in the Stock Market," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 67-88.
    6. Jouini, Elyes & Kallal, Hedi, 2001. "Efficient Trading Strategies in the Presence of Market Frictions," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 343-369.
    7. Giovanni BARONE-ADESI & Hakim DALL'O, 2010. "Is the Price Kernel Monotone?," Swiss Finance Institute Research Paper Series 10-03, Swiss Finance Institute, revised Apr 2010.
    8. Carlier, G. & Dana, R.-A., 2005. "Rearrangement inequalities in non-convex insurance models," Journal of Mathematical Economics, Elsevier, vol. 41(4-5), pages 483-503, August.
    9. Jackwerth, Jens Carsten & Rubinstein, Mark, 1996. "Recovering Probability Distributions from Option Prices," Journal of Finance, American Finance Association, vol. 51(5), pages 1611-1632, December.
    10. Rosenberg, Joshua V. & Engle, Robert F., 2002. "Empirical pricing kernels," Journal of Financial Economics, Elsevier, vol. 64(3), pages 341-372, June.
    11. repec:dau:papers:123456789/5389 is not listed on IDEAS
    12. Alexandre Ziegler, 2007. "Why Does Implied Risk Aversion Smile?," Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 859-904.
    13. Wolfgang Karl Härdle & Yarema Okhrin & Weining Wang, 2015. "Uniform Confidence Bands for Pricing Kernels," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 376-413.
    14. George M. Constantinides & Jens Carsten Jackwerth & Stylianos Perrakis, 2009. "Mispricing of S&P 500 Index Options," Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1247-1277.
    15. repec:ebl:ecbull:v:7:y:2003:i:5:p:1-9 is not listed on IDEAS
    16. Brendan K. Beare & Lawrence D. W. Schmidt, 2016. "An Empirical Test of Pricing Kernel Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 338-356, March.
    17. Elyès Jouini & Vincent Porte, 2007. "Efficient Trading Strategies," Working Papers halshs-00176616, HAL.
    18. Jackwerth, Jens Carsten, 2000. "Recovering Risk Aversion from Option Prices and Realized Returns," Review of Financial Studies, Society for Financial Studies, vol. 13(2), pages 433-451.
    19. Bakshi, Gurdip & Madan, Dilip & Panayotov, George, 2010. "Returns of claims on the upside and the viability of U-shaped pricing kernels," Journal of Financial Economics, Elsevier, vol. 97(1), pages 130-154, July.
    20. Yuri Golubev & Wolfgang Härdle & Roman Timonfeev, 2008. "Testing Monotonicity of Pricing Kernels," SFB 649 Discussion Papers SFB649DP2008-001, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    21. René Garcia & Richard Luger & Éric Renault, 2005. "Viewpoint: Option prices, preferences, and state variables," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 38(1), pages 1-27, February.
    22. Becker, Gary S, 1973. "A Theory of Marriage: Part I," Journal of Political Economy, University of Chicago Press, vol. 81(4), pages 813-846, July-Aug..
    23. Giovanni Barone Adesi & Robert F. Engle & Loriano Mancini, 2014. "A GARCH Option Pricing Model with Filtered Historical Simulation," Palgrave Macmillan Books, in: Giovanni Barone Adesi (ed.), Simulating Security Returns: A Filtered Historical Simulation Approach, chapter 4, pages 66-108, Palgrave Macmillan.
    24. Wolfgang Härdle & Volker Krätschmer & Rouslan Moro, 2009. "A Microeconomic Explanation of the EPK Paradox," SFB 649 Discussion Papers SFB649DP2009-010, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    25. repec:dau:papers:123456789/4721 is not listed on IDEAS
    26. Kai Detlefsen & Wolfgang Härdle & Rouslan Moro, 2007. "Empirical Pricing Kernels and Investor Preferences," SFB 649 Discussion Papers SFB649DP2007-017, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    27. Thorsten HENS & Christian REICHLIN, 2010. "Three Solutions to the Pricing Kernel Puzzle," Swiss Finance Institute Research Paper Series 10-14, Swiss Finance Institute.
    28. Ludovic Renou & Guillaume Carlier, 2003. "Existence and monotonicity of optimal debt contracts in costly state verification models," Economics Bulletin, AccessEcon, vol. 7(5), pages 1-9.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Denis Belomestny & Wolfgang Karl Härdle & Ekaterina Krymova, 2017. "Sieve Estimation Of The Minimal Entropy Martingale Marginal Density With Application To Pricing Kernel Estimation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(06), pages 1-21, September.
    2. Carole Bernard & Jit Seng Chen & Steven Vanduffel, 2014. "Optimal portfolios under worst-case scenarios," Quantitative Finance, Taylor & Francis Journals, vol. 14(4), pages 657-671, April.
    3. Beare, Brendan K. & Moon, Jong-Myun, 2012. "Testing the concavity of an ordinaldominance curve," University of California at San Diego, Economics Working Paper Series qt6qg1f8ms, Department of Economics, UC San Diego.
    4. Barone-Adesi, Giovanni & Fusari, Nicola & Mira, Antonietta & Sala, Carlo, 2020. "Option market trading activity and the estimation of the pricing kernel: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 216(2), pages 430-449.
    5. Brendan K. Beare & Lawrence D. W. Schmidt, 2016. "An Empirical Test of Pricing Kernel Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 338-356, March.
    6. George M. Constantinides & Michal Czerwonko & Stylianos Perrakis, 2020. "Mispriced index option portfolios," Financial Management, Financial Management Association International, vol. 49(2), pages 297-330, June.
    7. Brendan K. Beare & Juwon Seo, 2022. "Stochastic arbitrage with market index options," Papers 2207.00949, arXiv.org, revised Jul 2022.
    8. Ricardo Crisóstomo, 2021. "Estimating real‐world probabilities: A forward‐looking behavioral framework," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1797-1823, November.
    9. Brendan K. Beare, 2023. "Optimal measure preserving derivatives revisited," Mathematical Finance, Wiley Blackwell, vol. 33(2), pages 370-388, April.
    10. Thierry Post & Iňaki Rodríguez Longarela, 2021. "Risk Arbitrage Opportunities for Stock Index Options," Operations Research, INFORMS, vol. 69(1), pages 100-113, January.
    11. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    12. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
    13. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle in forward looking data," Review of Derivatives Research, Springer, vol. 21(3), pages 253-276, October.
    14. Ricardo Crisóstomo, 2021. "Estimación de probabilidades representativas del mundo real: importancia de los sesgos conductuales," CNMV Documentos de Trabajo CNMV Documentos de Trabaj, CNMV- Comisión Nacional del Mercado de Valores - Departamento de Estudios y Estadísticas.
    15. Denis Belomestny & Shujie Ma & Wolfgang Karl Härdle, 2015. "Pricing Kernel Modeling," SFB 649 Discussion Papers SFB649DP2015-001, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    16. Stylianos Perrakis, 2022. "From innovation to obfuscation: continuous time finance fifty years later," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(3), pages 369-401, September.

    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. Brendan K. Beare & Lawrence D. W. Schmidt, 2016. "An Empirical Test of Pricing Kernel Monotonicity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 338-356, March.
    2. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
    3. Maria Grith & Wolfgang Karl Härdle & Volker Krätschmer, 2013. "Reference Dependent Preferences and the EPK Puzzle," SFB 649 Discussion Papers SFB649DP2013-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Stylianos Perrakis, 2022. "From innovation to obfuscation: continuous time finance fifty years later," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(3), pages 369-401, September.
    5. Audrino, Francesco & Meier, Pirmin, 2012. "Empirical pricing kernel estimation using a functional gradient descent algorithm based on splines," Economics Working Paper Series 1210, University of St. Gallen, School of Economics and Political Science.
    6. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle in forward looking data," Review of Derivatives Research, Springer, vol. 21(3), pages 253-276, October.
    7. Xinyu WU & Senchun REN & Hailin ZHOU, 2017. "Empirical Pricing Kernels: Evidence from the Hong Kong Stock Market," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(4), pages 263-278.
    8. Brendan K. Beare, 2023. "Optimal measure preserving derivatives revisited," Mathematical Finance, Wiley Blackwell, vol. 33(2), pages 370-388, April.
    9. Peter Reinhard Hansen & Chen Tong, 2022. "Option Pricing with Time-Varying Volatility Risk Aversion," Papers 2204.06943, arXiv.org, revised Oct 2022.
    10. Brendan K. Beare & Juwon Seo, 2022. "Stochastic arbitrage with market index options," Papers 2207.00949, arXiv.org, revised Jul 2022.
    11. Dietmar P. J. Leisen, 2017. "The shape of small sample biases in pricing kernel estimations," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 943-958, June.
    12. Barone-Adesi, Giovanni & Fusari, Nicola & Mira, Antonietta & Sala, Carlo, 2020. "Option market trading activity and the estimation of the pricing kernel: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 216(2), pages 430-449.
    13. David Backus & Mikhail Chernov & Ian Martin, 2011. "Disasters Implied by Equity Index Options," Journal of Finance, American Finance Association, vol. 66(6), pages 1969-2012, December.
    14. Polkovnichenko, Valery & Zhao, Feng, 2013. "Probability weighting functions implied in options prices," Journal of Financial Economics, Elsevier, vol. 107(3), pages 580-609.
    15. Wolfgang Härdle & Volker Krätschmer & Rouslan Moro, 2009. "A Microeconomic Explanation of the EPK Paradox," SFB 649 Discussion Papers SFB649DP2009-010, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    16. Liao, Wen Ju & Sung, Hao-Chang, 2020. "Implied risk aversion and pricing kernel in the FTSE 100 index," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    17. George M. Constantinides & Michal Czerwonko & Stylianos Perrakis, 2020. "Mispriced index option portfolios," Financial Management, Financial Management Association International, vol. 49(2), pages 297-330, June.
    18. Jiao, Yuhan & Liu, Qiang & Guo, Shuxin, 2021. "Pricing kernel monotonicity and term structure: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 123(C).
    19. Yuri Golubev & Wolfgang Härdle & Roman Timofeev, 2014. "Testing monotonicity of pricing kernels," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(4), pages 305-326, October.
    20. Almeida, Caio & Freire, Gustavo, 2022. "Pricing of index options in incomplete markets," Journal of Financial Economics, Elsevier, vol. 144(1), pages 174-205.

    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:eee:mateco:v:47:y:2011:i:6:p:689-697. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jmateco .

    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.