A Stochastic Volatility Model and Inference for the Term Structure of Interest

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dc.contributor.advisor A. Ronald Gallant, Committee Member en_US
dc.contributor.advisor Denis Pelletier, Committee Member en_US
dc.contributor.advisor William H. Swallow, Committee Member en_US
dc.contributor.advisor Peter Bloomfield, Committee Chair en_US
dc.contributor.advisor David Dickey, Committee Member en_US
dc.contributor.author Liu, Peng en_US
dc.date.accessioned 2010-04-02T18:49:03Z
dc.date.available 2010-04-02T18:49:03Z
dc.date.issued 2007-04-25 en_US
dc.identifier.other etd-03152007-213137 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/4210
dc.description.abstract This thesis builds a stochastic volatility model for the term structure of interest rates, which is also known as the dynamics of the yield curve. The main purpose of the model is to propose a parsimonious and plausible approach to capture some characteristics that conform to some empirical evidences and conventions. Eventually, the development reaches a class of multivariate stochastic volatility models, which is flexible, extensible, providing the existence of an inexpensive inference approach. The thesis points out some inconsistency among conventions and practice. First, yield curves and its related curves are conventionally smooth. But in the literature that these curves are modeled as random functions, the co-movement of points on the curve are usually assumed to be governed by some covariance structures that do not generate smooth random curves. Second, it is commonly agreed that the constant volatility is not a sound assumption, but stochastic volatilities have not been commonly considered in related studies. Regarding the above problems, we propose a multiplicative factor stochastic volatility model, which has a relatively simple structure. Though it is apparently simple, the inference is not, because of the presence of stochastic volatilities. We first study the sequential-Monte-Carlo-based maximum likelihood approach, which extends the perspectives of Gaussian linear state-space modeling. We propose a systematic procedure that guides the inference based on this approach. In addition, we also propose a saddlepoint approximation approach, which integrates out states. Then the state propagates by an exact Gaussian approximation. The approximation works reasonably well for univariate models. Moreover, it works even better for the multivariate model that we propose. Because we can enjoy the asymptotic property of the saddlepoint approximation. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject term structure of interest rates en_US
dc.subject multivariate stochastic volatility en_US
dc.subject yield curve model en_US
dc.subject interest rate dynamics en_US
dc.subject non-linear non-Gaussian State-Space Model en_US
dc.title A Stochastic Volatility Model and Inference for the Term Structure of Interest en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Statistics en_US


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