Minimum contrast estimation in diffusion processes

Věra Lánská

51 citations0/0 files storedJournal of Applied Probability

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Stochastic processes and financial applications Probability and Statistical Research Control Systems and Identification

Abstract

This paper is concerned with the asymptotic theory of estimates of an unknown parameter in continuous-time Markov processes, which are described by non-linear stochastic differential equations. The maximum likelihood estimate and the minimum contrast estimate are investigated. For these estimates strong consistency and asymptotic normality are proved. The unknown parameter is assumed to take its values either in an open or in a compact set of real numbers.

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1976Maximum likelihood estimation for continuous-time stochastic processes 1975Asymptotic likelihood theory for diffusion processes

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2008A two-stage realized volatility approach to estimation of diffusion processes with discrete data 2011Minimum contrast estimation in fractional Ornstein-Uhlenbeck process: Continuous and discrete sampling 2014ECONOMETRIC ANALYSIS OF CONTINUOUS TIME MODELS: A SURVEY OF PETER PHILLIPS’S WORK AND SOME NEW RESULTS

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