Empirical Characteristic Function Estimation and Its Applications
This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood appro...
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2004
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sg-smu-ink.soe_research-13572018-05-30T04:02:47Z Empirical Characteristic Function Estimation and Its Applications YU, Jun This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering. 2004-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/358 https://ink.library.smu.edu.sg/context/soe_research/article/1357/viewcontent/SSRN_id553701.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Diffusion process; Poisson jump; Self-exciting; GMM; Jumpclustering Econometrics |
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Diffusion process; Poisson jump; Self-exciting; GMM; Jumpclustering Econometrics YU, Jun Empirical Characteristic Function Estimation and Its Applications |
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This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering. |
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YU, Jun |
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YU, Jun |
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YU, Jun |
title |
Empirical Characteristic Function Estimation and Its Applications |
title_short |
Empirical Characteristic Function Estimation and Its Applications |
title_full |
Empirical Characteristic Function Estimation and Its Applications |
title_fullStr |
Empirical Characteristic Function Estimation and Its Applications |
title_full_unstemmed |
Empirical Characteristic Function Estimation and Its Applications |
title_sort |
empirical characteristic function estimation and its applications |
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Institutional Knowledge at Singapore Management University |
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2004 |
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https://ink.library.smu.edu.sg/soe_research/358 https://ink.library.smu.edu.sg/context/soe_research/article/1357/viewcontent/SSRN_id553701.pdf |
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