Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance

Peter C. B. Phillips () and Jun Yu ()

No 1597, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

Abstract: This paper overviews maximum likelihood and Gaussian methods of estimating continuous time models used in finance. Since the exact likelihood can be constructed only in special cases, much attention has been devoted to the development of methods designed to approximate the likelihood. These approaches range from crude Euler-type approximations and higher order stochastic Taylor series expansions to more complex polynomial-based expansions and infill approximations to the likelihood based on a continuous time data record. The methods are discussed, their properties are outlined and their relative finite sample performance compared in a simulation experiment with the nonlinear CIR diffusion model, which is popular in empirical finance. Bias correction methods are also considered and particular attention is given to jackknife and indirect inference estimators. The latter retains the good asymptotic properties of ML estimation while removing finite sample bias. This method demonstrates superior performance in finite samples.

Keywords: Maximum likelihood; Transition density; Discrete sampling; Continuous record; Realized volatility; Bias reduction; Jackknife; Indirect inference (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst and nep-sea
Date: Written
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