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Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations

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  • Stan Hurn
  • J.Jeisman
  • K.A. Lindsay

Abstract

Maximum likelihood (ML) estimates of the parameters of stochastic differential equations (SDEs) are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed form expression for the transitional density of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This paper provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox-IngersollRoss and Ornstein-Uhlenbeck equations respectively.

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Bibliographic Info

Paper provided by School of Economics and Finance, Queensland University of Technology in its series Stan Hurn Discussion Papers with number 2006.

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Date of creation: 15 Jun 2006
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Handle: RePEc:qut:sthurn:2006

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Keywords: stochastic differential equations; parameter estimation; maximum likelihood; simulation; moments;

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Citations

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Cited by:
  1. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Teaching an old dog new tricks: Improved estimation of the parameters of SDEs by numerical solution of the Fokker-Planck equation," Stan Hurn Discussion Papers 2006-01, School of Economics and Finance, Queensland University of Technology.
  2. Ai[diaeresis]t-Sahalia, Yacine & Kimmel, Robert, 2007. "Maximum likelihood estimation of stochastic volatility models," Journal of Financial Economics, Elsevier, vol. 83(2), pages 413-452, February.
  3. A. Hurn & J. Jeisman & K. Lindsay, 2007. "Teaching an Old Dog New Tricks: Improved Estimation of the Parameters of Stochastic Differential Equations by Numerical Solution of the Fokker-Planck Equation," NCER Working Paper Series 9, National Centre for Econometric Research.

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