Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models
AbstractContinuous-time stochastic volatility models are becoming increasingly popular in finance because of their flexibility in accommodating most stylized facts of financial time series. However, their estimation is difficult because the likelihood function does not have a closed-form expression. A characteristic function-based estimation method for non-Gaussian Ornstein-Uhlenbeck-based stochastic volatility models is proposed. Explicit expressions of the characteristic functions for various cases of interest are derived. The asymptotic properties of the estimators are analyzed and their small-sample performance is evaluated by means of a simulation experiment. Finally, two real-data applications show that the superposition of two Ornstein-Uhlenbeck processes gives a good approximation to the dependence structure of the process.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 55 (2011)
Issue (Month): 8 (August)
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Web page: http://www.elsevier.com/locate/csda
Ornstein-Uhlenbeck process Levy process Stochastic volatility Characteristic function estimation;
Other versions of this item:
- Emanuele Taufer & Nikolai Leonenko & Marco Bee, 2009. "Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models," DISA Working Papers 0907, Department of Computer and Management Sciences, University of Trento, Italy, revised 02 Dec 2009.
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