A stochastic approach to prepayment modeling

dc.contributor.advisorThompson, James R.
dc.creatorOverley, Mark S.
dc.date.accessioned2009-06-04T00:07:12Z
dc.date.available2009-06-04T00:07:12Z
dc.date.issued1996
dc.description.abstractA new type of prepayment model for use in the valuation of mortgage-backed securities is presented. The model is based on a simple axiomatic characterization of the prepayment decision by the individual in terms of a continuous time, discrete state stochastic process. One advantage of the stochastic approach compared to a traditional regression model is that information on the variability of prepayments is retained. This information is shown to have a significant effect on the value of mortgage-backed derivative securities. Furthermore, the model explains important path dependent properties of prepayments such as seasoning and burnout in a natural way, which improves fit accuracy for mean prepayment rates. This is demonstrated by comparing the stochastic mean to a nonlinear regression model based on time and mortgage rate information for generic Ginnie Mae collateral.
dc.format.extent146 p.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.callnoTHESIS STAT. 1996 OVERLEY
dc.identifier.citationOverley, Mark S.. "A stochastic approach to prepayment modeling." (1996) Diss., Rice University. <a href="https://hdl.handle.net/1911/17009">https://hdl.handle.net/1911/17009</a>.
dc.identifier.urihttps://hdl.handle.net/1911/17009
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectStatistics
dc.subjectEconomics
dc.subjectFinance
dc.titleA stochastic approach to prepayment modeling
dc.typeThesis
dc.type.materialText
thesis.degree.departmentStatistics
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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