A Modified Sequential Monte Carlo Procedure for the Efficient Recursive Estimation of Extreme Quantiles

Abstract

Many applications in science involve finding estimates of unobserved variables from observed data, by combining model predictions with observations. The Sequential Monte Carlo (SMC) is a well-established technique for estimating the distribution of unobserved variables which are conditional on current observations. While the SMC is very successful at estimating the first central moments, estimating the extreme quantiles of a distribution via the current SMC methods is computationally very expensive. The purpose of this paper is to develop a new framework using probability distortion. We use an SMC with distorted weights in order to make computationally efficient inferences about tail probabilities of future interest rates using the Cox-Ingersoll-Ross (CIR) model, as well as with an observed yield curve. We show that the proposed method yields acceptable estimates about tail quantiles at a fraction of the computational cost of the full Monte Carlo.