Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression
- Topics:
- Real Estate Portfolio Management
- Tags:
- California State University,
- Capital Structures,
- Finance,
- Financial Planning,
- Financial Services,
- Interest Rate,
- Investment,
- Mortgages,
- Valuation
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Overview: Developing a good prepayment model is a central task in the valuation of mortgages and mortgage-backed securities but conventional parametric models often have bad out-of-sample predictive ability. A likely explanation is the highly non-linear nature of the prepayment function. Non-parametric techniques are much better at detecting non-linearity and multivariate interaction. This article discusses how non-parametric kernel regression may be applied to loan level event histories to produce a better parametric model. By utilizing a parsimonious specification, a model can be produced that practitioners can use in valuation routines based on Monte Carlo interest rate simulation.
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Format: PDF | Size: 533KB | Date: Jan 2002 | Pages: 30
