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Activity Number:
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100
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 7, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306368 |
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Title:
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Bayesian Forecasting of Prepayment Rates for Individual Pools of Mortgages
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Author(s):
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Ivilina Popova*+ and Elmira Popova and Edward I. George
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Companies:
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Seattle University and The University of Texas at Austin and University of Pennsylvania
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Address:
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Department of Finance, Seattle, WA, 98122,
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Keywords:
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finance ; prepayment ; mixture ; Bayesian
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Abstract:
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This paper proposes a novel approach for modeling prepayment rates of individual pools of mortgages. The model incorporates the empirical evidence that prepayment is past-dependent via Bayesian methodology. There are many factors that influence the prepayment behavior, and, for many, there is no available information. We implement this issue by creating a Bayesian mixture model and constructing a Markov chain Monte Carlo algorithm to estimate the parameters. We assess the model on a dataset from the Bloomberg Database. Our results show the burnout effect is a significant variable for explaining normal prepayment activities. This result does not hold when prepayment is triggered by nonpool-dependent events.
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