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Maximum Likelihood Estimation of Stochastic Volatility Models

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  • Yacine Ait-Sahalia
  • Robert Kimmel

Abstract

We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine model of Heston (1993) and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10579.

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Date of creation: Jun 2004
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Publication status: published as Ait-Sahalia, Yacine and Robert Kimmel. "Maximum Likelihood Estimation of Stochastic Volatility Models." Journal of Financial Economics 83, 2 (February 2007): 413-52.
Handle: RePEc:nbr:nberwo:10579

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Cited by:
  1. Tim Bollerslev & Michael Gibson & Hao Zhou, 2004. "Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities," Finance and Economics Discussion Series 2004-56, Board of Governors of the Federal Reserve System (U.S.).
  2. Peter Christoffersen & Kris Jacobs & Karim Mimouni, 2007. "Models for S&P500 Dynamics: Evidence from Realized Volatility, Daily Returns, and Option Prices," CREATES Research Papers 2007-37, School of Economics and Management, University of Aarhus.
  3. Charles S. Bos, 2008. "Model-based Estimation of High Frequency Jump Diffusions with Microstructure Noise and Stochastic Volatility," Tinbergen Institute Discussion Papers 08-011/4, Tinbergen Institute.
  4. Arnaud Gloter, 2007. "Efficient estimation of drift parameters in stochastic volatility models," Finance and Stochastics, Springer, vol. 11(4), pages 495-519, October.

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