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- W2127942700 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." @default.
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- W2127942700 date "2003-05-01" @default.
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- W2127942700 title "Simulated Likelihood Approximations for Stochastic Volatility Models" @default.
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- W2127942700 doi "https://doi.org/10.1111/1467-9469.00330" @default.
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