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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 that the inference procedures work well for empirically realistic model 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 risk premia of extreme events, i.e., jumps, as well as a more flexible relation between the risk premia and the level of risk. We show that both extensions provide a significantly improved characterization, both statistically and economically, of observed option prices.

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File URL: http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd12-266.pdf
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Bibliographic Info

Paper provided by Institute of Economic Research, Hitotsubashi University in its series Global COE Hi-Stat Discussion Paper Series with number gd12-266.

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Date of creation: Dec 2012
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Handle: RePEc:hst:ghsdps:gd12-266

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Keywords: Option Pricing; Inference; Risk Premia; Jumps; Latent State Vector; Stochastic Volatility; Specification Testing; Stable Convergence;

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  2. Patrick GAGLIARDINI & Christian GOURIEROUX & Eric RENAULT, 2004. "Efficient Derivative Pricing By The Extended Method of Moments," Swiss Finance Institute Research Paper Series 10-07, Swiss Finance Institute, revised Oct 2009.
  3. Todorov, Viktor & Tauchen, George, 2011. "Volatility Jumps," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 356-371.
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  10. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
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  17. 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.
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  21. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
  22. Jean Jacod & Viktor Todorov, 2010. "Do price and volatility jump together?," Papers 1010.4990, arXiv.org.
  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.
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  25. Pastorello, Sergio & Patilea, Valentin & Renault, Eric, 2003. "Iterative and Recursive Estimation in Structural Nonadaptive Models: Rejoinder," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 503-09, October.
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