Browsing by Subject "bayesian forecasting"

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    Bayesian forecasting of Prepayment Rates for Individual Pools of Mortgages
    (2008) Popova, Ivillina; Popova, Elmira; George, Edward I.; Popova, Elmira
    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 of them there is no available (or impossible to gather) information. We implement this issue by creating a Bayesian mixture model and construct a Markov Chain Monte Carlo algorithm to estimate the parameters. We assess the model on a data set from the Bloomberg Database. Our results show that the burnout effect is a significant variable for explaining normal prepayment activities. This result does not hold when prepayment is triggered by non-pool dependent events. We show how to use the new model to compute prices for Mortgage Backed Securities. Monte Carlo simulation is the traditional method for obtaining such prices and the proposed model can be easily incorporated within simulation pricing framework. Prices for standard Pass-Throughs are obtained using simulation.
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    Dynamic Financial Index Models: Modeling Conditional Dependencies Via Graphs
    (2011) Wang, Hao; Reeson, Craig; Carvalho, Carlos M.; Carvalho, Carlos M.
    We discuss the development and application of dynamic graphical models for multivariate financial time series in the context of Financial Index Models. The use of graphs generalizes the independence residual variation assumption of index models with a more complex yet still parsimonious alternative. Working with the dynamic matrix-variate graphical model framework, we develop general time-varying index models that are analytically tractable. In terms of methodology, we carefully explore strategies to deal with graph uncertainty and discuss the implementation of a novel computational tool to sequentially learn about the conditional independence relationships defining the model. Additionally, motivated by our applied context, we extend the DGM framework to accommodate random regressors. Finally, in a case study involving 100 stocks, we show that our proposed methodology is able to generate improvements in covariance forecasting and portfolio optimization problems.