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- Title
Simulated Likelihood Approximations for Stochastic Volatility Models.
- Authors
Sørensen, Helle
- Abstract
Abstract. This paper deals with parametric inference for continuous-time stochastic volatility models observed at discrete points in time. We consider approximate maximum likelihood estimation: for the k th-order approximation, we pretend that the observations form a k th-order Markov chain, find the corresponding approximate log-likelihood function, and maximize it with respect to θ . The approximate log-likelihood function is not known analytically, but can easily be calculated by simulation. For each k , the method yields consistent and asymptotically normal estimators. Simulations from a model based on the Cox–Ingersoll–Ross model are used for illustration.
- Subjects
STOCHASTIC processes; STOCHASTIC approximation; MATHEMATICAL models; MARKOV processes
- Publication
Scandinavian Journal of Statistics, 2003, Vol 30, Issue 2, p257
- ISSN
0303-6898
- Publication type
Article
- DOI
10.1111/1467-9469.00330