This discussion paper led to an article in <I>Statistica Neerlandica</I> (2003). Vol. 57, issue 4, pages 439-469.<P> The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this...</p></i>

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian...

Monte Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments subject to possible default. The accurate measurement of credit risk is often a rare-event simulation problem because default probabilities are low for highly rated...

This study explores the role of the credit securitisation process in managing the credit risk amount of the banking loan portfolio, when the bank originator retains a residual equitylike class as illiquid first loss position (FLP). An Importance Sampling Monte Carlo simulation model has been...