Maximum likelihood estimation of partially observed diffusion models

Maximum likelihood estimation of partially observed diffusion models

This paper develops a maximum likelihood (ML) method to estimate partially observed diffusion models based on data sampled at discrete times. The method combines two techniques recently proposed in the literature in two separate steps. In the first step, the closed form approach of Aït-Sahalia (2008...

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Bibliographic Details
Main Authors: KLEPPE, Tore Selland, Jun YU, SKAUG, Hans J.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Closed-form approximation
Diffusion model
Efficient importance sampler
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/1797
https://ink.library.smu.edu.sg/context/soe_research/article/2796/viewcontent/P_ID_52648_Yu_JOE_2014_MaxLikelihoodEstPartiallyObservedDiffusionModels.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/soe_research/1797
https://ink.library.smu.edu.sg/context/soe_research/article/2796/viewcontent/P_ID_52648_Yu_JOE_2014_MaxLikelihoodEstPartiallyObservedDiffusionModels.pdf

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