IDEAS home Printed from https://ideas.repec.org/p/fip/fednsr/706.html
   My bibliography  Save this paper

Option-implied term structures

Author

Listed:
  • Erik Vogt

Abstract

This paper proposes a nonparametric sieve regression framework for pricing the term structure of option spanning portfolios. The framework delivers closed-form, nonparametric option pricing and hedging formulas through basis function expansions that grow with the sample size. Novel confidence intervals quantify term structure estimation uncertainty. The framework is applied to estimating the term structure of variance risk premia and finds that a short-run component dominates market excess return predictability. This finding is inconsistent with existing asset pricing models that seek to explain the variance risk premium?s predictive content.

Suggested Citation

  • Erik Vogt, 2014. "Option-implied term structures," Staff Reports 706, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:706
    as

    Download full text from publisher

    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr706.pdf?la=en
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark Britten‐Jones & Anthony Neuberger, 2000. "Option Prices, Implied Price Processes, and Stochastic Volatility," Journal of Finance, American Finance Association, vol. 55(2), pages 839-866, April.
    2. 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.
    3. 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.
    4. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    5. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. "Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    6. Dew-Becker, Ian & Giglio, Stefano & Le, Anh & Rodriguez, Marius, 2017. "The price of variance risk," Journal of Financial Economics, Elsevier, vol. 123(2), pages 225-250.
    7. Jennifer Conrad & Robert F. Dittmar & Eric Ghysels, 2013. "Ex Ante Skewness and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 68(1), pages 85-124, February.
    8. Tim Bollerslev & Viktor Todorov, 2011. "Tails, Fears, and Risk Premia," Journal of Finance, American Finance Association, vol. 66(6), pages 2165-2211, December.
    9. Chen, Xiaohong & Liao, Zhipeng & Sun, Yixiao, 2014. "Sieve inference on possibly misspecified semi-nonparametric time series models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 639-658.
    10. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    11. Torben G. Andersen & Nicola Fusari & Viktor Todorov, 2015. "Parametric Inference and Dynamic State Recovery From Option Panels," Econometrica, Econometric Society, vol. 83(3), pages 1081-1145, May.
    12. Peter Christoffersen & Mathieu Fournier & Kris Jacobs, 2018. "The Factor Structure in Equity Options," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 595-637.
    13. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    14. Xiaohong Chen & Xiaotong Shen, 1998. "Sieve Extremum Estimates for Weakly Dependent Data," Econometrica, Econometric Society, vol. 66(2), pages 289-314, March.
    15. Chang, Bo Young & Christoffersen, Peter & Jacobs, Kris, 2013. "Market skewness risk and the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 107(1), pages 46-68.
    16. 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.
    17. Bondarenko, Oleg, 2003. "Estimation of risk-neutral densities using positive convolution approximation," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 85-112.
    18. Breeden, Douglas T & Litzenberger, Robert H, 1978. "Prices of State-contingent Claims Implicit in Option Prices," The Journal of Business, University of Chicago Press, vol. 51(4), pages 621-651, October.
    19. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    20. Gurdip Bakshi & Nikunj Kapadia & Dilip Madan, 2003. "Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options," The Review of Financial Studies, Society for Financial Studies, vol. 16(1), pages 101-143.
    21. Bollerslev, Tim & Osterrieder, Daniela & Sizova, Natalia & Tauchen, George, 2013. "Risk and return: Long-run relations, fractional cointegration, and return predictability," Journal of Financial Economics, Elsevier, vol. 108(2), pages 409-424.
    22. Gurdip Bakshi & Dilip Madan, 2006. "A Theory of Volatility Spreads," Management Science, INFORMS, vol. 52(12), pages 1945-1956, December.
    23. Darrell Duffie & Jun Pan & Kenneth Singleton, 2000. "Transform Analysis and Asset Pricing for Affine Jump-Diffusions," Econometrica, Econometric Society, vol. 68(6), pages 1343-1376, November.
    24. Joost Driessen & Pascal J. Maenhout & Grigory Vilkov, 2009. "The Price of Correlation Risk: Evidence from Equity Options," Journal of Finance, American Finance Association, vol. 64(3), pages 1377-1406, June.
    25. Itamar Drechsler & Amir Yaron, 2011. "What's Vol Got to Do with It," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 1-45.
    26. Coppejans, Mark & Gallant, A. Ronald, 2002. "Cross-validated SNP density estimates," Journal of Econometrics, Elsevier, vol. 110(1), pages 27-65, September.
    27. 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.
    28. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.
    29. Peter Carr & Liuren Wu, 2009. "Variance Risk Premiums," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1311-1341, March.
    30. repec:bla:jfinan:v:53:y:1998:i:2:p:499-547 is not listed on IDEAS
    31. Banz, Rolf W & Miller, Merton H, 1978. "Prices for State-contingent Claims: Some Estimates and Applications," The Journal of Business, University of Chicago Press, vol. 51(4), pages 653-672, October.
    32. Bates, David S., 2000. "Post-'87 crash fears in the S&P 500 futures option market," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 181-238.
    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. Sang Byung Seo & Jessica A. Wachter, 2019. "Option Prices in a Model with Stochastic Disaster Risk," Management Science, INFORMS, vol. 65(8), pages 3449-3469, August.
    2. Baeho Kim & Da‐Hea Kim & Haehean Park, 2020. "Informed options trading on the implied volatility surface: A cross‐sectional approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 776-803, May.

    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. 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.
    2. Yabei Zhu & Xingguo Luo & Qi Xu, 2023. "Industry variance risk premium, cross‐industry correlation, and expected returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(1), pages 3-32, January.
    3. Sanjay K. Nawalkha & Xiaoyang Zhuo, 2020. "A Theory of Equivalent Expectation Measures for Contingent Claim Returns," Papers 2006.15312, arXiv.org, revised May 2022.
    4. Nawalkha, Sanjay K & Zhuo, Xiaoyang, 2020. "A Theory of Equivalent Expectation Measures for Expected Prices of Contingent Claims," OSF Preprints hsxtu, Center for Open Science.
    5. Bondarenko, Oleg, 2014. "Variance trading and market price of variance risk," Journal of Econometrics, Elsevier, vol. 180(1), pages 81-97.
    6. Hollstein, Fabian & Wese Simen, Chardin, 2020. "Variance risk: A bird’s eye view," Journal of Econometrics, Elsevier, vol. 215(2), pages 517-535.
    7. Konstantinidi, Eirini & Skiadopoulos, George, 2016. "How does the market variance risk premium vary over time? Evidence from S&P 500 variance swap investment returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 62-75.
    8. Konstantinidi, Eirini & Skiadopoulos, George, 2016. "How does the market variance risk premium vary over time? Evidence from S&P 500 variance swap investment returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 62-75.
    9. José Afonso Faias & Tiago Castel-Branco, 2018. "Out-Of-Sample Stock Return Prediction Using Higher-Order Moments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-27, September.
    10. Rombouts, Jeroen V.K. & Stentoft, Lars & Violante, Francesco, 2020. "Dynamics of variance risk premia: A new model for disentangling the price of risk," Journal of Econometrics, Elsevier, vol. 217(2), pages 312-334.
    11. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    12. Bardgett, Chris & Gourier, Elise & Leippold, Markus, 2019. "Inferring volatility dynamics and risk premia from the S&P 500 and VIX markets," Journal of Financial Economics, Elsevier, vol. 131(3), pages 593-618.
    13. Tai‐Yong Roh & Alireza Tourani‐Rad & Yahua Xu & Yang Zhao, 2021. "Volatility‐of‐volatility risk in the crude oil market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(2), pages 245-265, February.
    14. Bollerslev, Tim & Todorov, Viktor & Xu, Lai, 2015. "Tail risk premia and return predictability," Journal of Financial Economics, Elsevier, vol. 118(1), pages 113-134.
    15. Filipović, Damir & Gourier, Elise & Mancini, Loriano, 2016. "Quadratic variance swap models," Journal of Financial Economics, Elsevier, vol. 119(1), pages 44-68.
    16. Biao Guo & Hai Lin, 2020. "Volatility and jump risk in option returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(11), pages 1767-1792, November.
    17. Yue, Tian & Ruan, Xinfeng & Gehricke, Sebastian & Zhang, Jin E., 2023. "The volatility index and volatility risk premium in China," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 40-55.
    18. Peter Van Tassel & Erik Vogt, 2016. "Global variance term premia and intermediary risk appetite," Staff Reports 789, Federal Reserve Bank of New York.
    19. Aït-Sahalia, Yacine & Karaman, Mustafa & Mancini, Loriano, 2020. "The term structure of equity and variance risk premia," Journal of Econometrics, Elsevier, vol. 219(2), pages 204-230.
    20. González-Urteaga, Ana & Rubio, Gonzalo, 2016. "The cross-sectional variation of volatility risk premia," Journal of Financial Economics, Elsevier, vol. 119(2), pages 353-370.

    More about this item

    Keywords

    variance risk premium; term structures; options; return predictability; nonparametric regressions;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:fip:fednsr:706. 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: Gabriella Bucciarelli (email available below). General contact details of provider: https://edirc.repec.org/data/frbnyus.html .

    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.