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Abstract Details
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Activity Number:
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31
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301840 |
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Title:
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Maximum Penalized Quasi-Likelihood Estimation of the Diffusion Function
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Author(s):
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Jeff Hamrick*+ and Kostas Kardaras and Murad S. Taqqu and Yifei Huang
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Companies:
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Rhodes College and Boston University and Boston University and Boston University
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Address:
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2000 N. Parkway, Memphis, TN, 38112, United States
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Keywords:
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diffusion processes ;
diffusion function ;
kernel estimators ;
maximum penalized quasi-likelihood estimators ;
nonparametric statistics ;
finance
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Abstract:
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We develop a maximum penalized quasi-likelihood estimator for estimating in a non-parametric way the diffusion function of a diffusion process, as an alternative to more traditional kernel-based estimators. After developing a numerical scheme for computing the maximizer of the penalized maximum quasi-likelihood function, we study the asymptotic properties of our estimator by way of simulation. Under the assumption that overnight London Interbank Offered Rates (LIBOR); the USD/EUR, USD/GBP, JPY/USD, and EUR/USD nominal exchange rates; and 1-month, 3-month, and 30-year Treasury bond yields are generated by diffusion processes, we use our numerical scheme to estimate the diffusion function.
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