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Parametric Inference and Dynamic State Recovery from Option Panels

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  • Torben G. Andersen
  • Nicola Fusari
  • Viktor Todorov

Abstract

We develop a new parametric estimation procedure for option panels observed with error which relies on asymptotic approximations assuming an ever increasing set of observed option prices in the moneyness- maturity (cross-sectional) dimension, but with a fixed time span. We develop consistent estimators of the parameter vector and the dynamic realization of the state vector that governs the option price dynamics. The estimators converge stably to a mixed-Gaussian law and we develop feasible estimators for the limiting variance. We provide semiparametric tests for the option price dynamics based on the distance between the spot volatility extracted from the options and the one obtained nonparametrically from high-frequency data on the underlying asset. We further construct new formal tests of the model fit for specific regions of the volatility surface and for the stability of the risk-neutral dynamics over a given period of time. A large-scale Monte Carlo study indicates the inference procedures work well for empirically realistic specifications and sample sizes. In an empirical application to S&P 500 index options we extend the popular double-jump stochastic volatility model to allow for time-varying jump risk premia and a flexible relation between risk premia and the level of risk. Both extensions lead to an improved characterization of observed option prices.

Suggested Citation

  • Torben G. Andersen & Nicola Fusari & Viktor Todorov, 2012. "Parametric Inference and Dynamic State Recovery from Option Panels," NBER Working Papers 18046, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18046
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jarno Talponen, 2018. "Matching distributions: Recovery of implied physical densities from option prices," Papers 1803.03996, arXiv.org.
    2. Jarrow, Robert & Kwok, Simon Sai Man, 2015. "Specification tests of calibrated option pricing models," Journal of Econometrics, Elsevier, vol. 189(2), pages 397-414.
    3. repec:eee:econom:v:203:y:2018:i:2:p:256-266 is not listed on IDEAS
    4. Vogt, Erik, 2014. "Option-implied term structures," Staff Reports 706, Federal Reserve Bank of New York, revised 01 Jan 2016.
    5. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
    6. Bryan Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2016. "Too-Systemic-to-Fail: What Option Markets Imply about Sector-Wide Government Guarantees," American Economic Review, American Economic Association, vol. 106(6), pages 1278-1319, June.
    7. Calvet, Laurent E. & Fearnley, Marcus & Fisher, Adlai J. & Leippold, Markus, 2015. "What is beneath the surface? Option pricing with multifrequency latent states," Journal of Econometrics, Elsevier, vol. 187(2), pages 498-511.
    8. Torben G. Andersen & Nicola Fusari & Viktor Todorov & Rasmus T. Varneskov, 1001. "Option Panels in Pure-Jump Settings," CREATES Research Papers 2018-04, Department of Economics and Business Economics, Aarhus University.
    9. Andrea Barletta & Paolo Santucci de Magistris & Francesco Violante, 0404. "A Non-Structural Investigation of VIX Risk Neutral Density," CREATES Research Papers 2017-15, Department of Economics and Business Economics, Aarhus University.
    10. Sirio Aramonte & Mohammad Jahan-Parvar & Samuel Rosen & John W. Schindler, 2017. "Firm-Specific Risk-Neutral Distributions : The Role of CDS Spreads," International Finance Discussion Papers 1212, Board of Governors of the Federal Reserve System (U.S.).

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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