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RUcore Resource Object
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TitleSingular perturbation methods in credit derivative modeling
NameKoo, Jawon (author), Feehan, Paul (chair), Ocone, Daniel (internal member), Gundy, Richard (internal member), Chen, Ren-Raw (outside member), Rutgers University, Graduate School - New Brunswick,
Degree Date2010-01
Date Created2010
SubjectMathematics,
Credit derivatives--Mathematical models,
Stochastic processes
DescriptionThis thesis introduces the dynamical pricing model and
approximation method in pricing a "Collateralized Debt
Obligation" (CDO). For this purpose we use a two-dimensional, self-affecting Markov process of discrete-valued aggregate loss process and stochastic factor process in its intensity. We review several models for pricing of multi-name credit derivative
products and explain in detail a two-dimensional Markov intensity model proposed by Halperin and Arnsdorf.
Using the model by Halperin and Arnsdorf, we derive the Kolmogorov forward partial differential equation for the transition density function of the underlying two-dimensional Markov process. We use the singular perturbation method to obtain an approximate solution
to this partial differential equation in the case of a fast mean reverting stochastic intensity model. We perform an error analysis to determine the accuracy of our approximate solution.
approximation method in pricing a "Collateralized Debt
Obligation" (CDO). For this purpose we use a two-dimensional, self-affecting Markov process of discrete-valued aggregate loss process and stochastic factor process in its intensity. We review several models for pricing of multi-name credit derivative
products and explain in detail a two-dimensional Markov intensity model proposed by Halperin and Arnsdorf.
Using the model by Halperin and Arnsdorf, we derive the Kolmogorov forward partial differential equation for the transition density function of the underlying two-dimensional Markov process. We use the singular perturbation method to obtain an approximate solution
to this partial differential equation in the case of a fast mean reverting stochastic intensity model. We perform an error analysis to determine the accuracy of our approximate solution.
NotePh.D.
NoteIncludes bibliographical references (p. 78-79)
Noteby Jawon Koo
Genretheses
Persistent URLhttp://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052121
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.