| Investment Knowledge |
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Prepayment Neural Net A model of mortgage prepayment rates based on the neural net approach is proposed. The model for insured, closed, five-year term mortgages has been developed. Mortgage prepayment rate is affected by a large number of economic, social and demographic factors and is to a significant degree a random variable. We have identified six principal determinants of the prepayment rate and built a neural network that uses these determinants as input parameters. Given these inputs the model is supposed to predict the corresponding prepayment rate. The prepayment rate is considered a random variable whose distribution function depends on the input parameters. The neural net model predicts the mean of this distribution given the inputs. Since many of the input parameters are themselves random variables, a more precise statement is that the model estimates the expectation of the prepayment rate conditional on the fixed set of parameters. Three kinds of test have been carried out here. One, a qualitative test, compares the goodness of the fit of the prepayment model to the training data set with that to the testing data set. The second test compares the performance of the neural net model on the testing data set with that of a linear regression model (a semi-quantitative test). The third, also a qualitative test, generates scenarios of continuously rising pvpb (falling interest rates) of falling pvpb (rising interest rates). Prepayment function (1) is supposed to be used as a part of other models, such as a model for valuing mortgages under stochastic interest rates. These bigger models may require a certain degree of smoothness of the prepayment function. Technically, function (1) is analytic, i.e. infinitely smooth. It is possible, however, that in certain domains of its argument space the function exhibits relatively steep gradients, which under discrete sampling may appear as discontinuities.
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