Computing survival probabilities based on stochastic differential models

We develop a new numerical method to compute survival probabilities based on stochastic differential models, a matter of great importance in several areas of science, such as finance, biology, medicine and geophysics. This novel approach is based on polynomial differential quadrature, which is combi...

Full description

Saved in:
Bibliographic Details
Published in:Journal of computational and applied mathematics 2015-03, Vol.277, p.127-137
Main Authors: Andreoli, Alessandro, Ballestra, Luca Vincenzo, Pacelli, Graziella
Format: Article
Language:English
Subjects:
Differential quadrature
PDQ
Reduced-form model
Ruin probability
Structural model
Survival probability
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We develop a new numerical method to compute survival probabilities based on stochastic differential models, a matter of great importance in several areas of science, such as finance, biology, medicine and geophysics. This novel approach is based on polynomial differential quadrature, which is combined with a high-order time discretization scheme. Numerical experiments are presented showing that the proposed method performs extremely well and is more efficient than the approaches recently developed in Costabile et al. (2013) and Guarin et al. (2011). •We propose a very efficient method to compute survival probabilities.•We combine polynomial differential quadrature with high-order time-stepping.•We consider a reduced-form model and a structural model that arise from finance and insurance.•The method is model independent and could also be extended to other stochastic processes.•Numerical comparison with other recent approaches is provided.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2014.08.030