Estimation of the parameters of stochastic differential equations

Jeisman, Joseph Ian (2006) Estimation of the parameters of stochastic differential equations. PhD thesis, Queensland University of Technology.


Stochastic di®erential equations (SDEs) are central to much of modern finance theory and have been widely used to model the behaviour of key variables such as the instantaneous short-term interest rate, asset prices, asset returns and their volatility. The explanatory and/or predictive power of these models depends crucially on the particularisation of the model SDE(s) to real data through the choice of values for their

parameters. In econometrics, optimal parameter estimates are generally considered to be those that maximise the likelihood of the sample. In the context of the estimation of the parameters of SDEs, however, a closed-form expression for the likelihood function is rarely available and hence exact maximum-likelihood (EML) estimation is

usually infeasible. The key research problem examined in this thesis is the development

of generic, accurate and computationally feasible estimation procedures based on the ML principle, that can be implemented in the absence of a closed-form expression for the likelihood function. The overall recommendation to come out of the thesis is that an estimation procedure based on the finite-element solution of a reformulation of the Fokker-Planck equation in terms of the transitional cumulative distribution function(CDF) provides the best balance across all of the desired characteristics. The recommended approach involves the use of an interpolation technique proposed in this thesis which greatly reduces the required computational effort.

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ID Code: 16205
Item Type: QUT Thesis (PhD)
Supervisor: Hurn, Stan, Becker, Ralf, & Lindsay, Kenneth
Keywords: stochastic differential equations, parameter estimation, maximum likelihood, finite difference, finite element, cumulative distribution function, interpolation
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Department: Faculty of Business
Institution: Queensland University of Technology
Copyright Owner: Copyright Joseph Ian Jeisman
Deposited On: 03 Dec 2008 03:58
Last Modified: 21 Jun 2017 14:40

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