City Research Online

Abstract

In this paper we present a new method for simulating integrals of stochastic processes. We focus on the nontrivial case of time integrals, conditional on the state variable levels at the endpoints of a time interval, through a moment-based probability distribution construction. We present different classes of models with important uses in finance, medicine, epidemiology, climatology, bioeconomics and physics. The method is generally applicable in well-posed moment problem settings. We study its convergence, point out its advantages through a series of numerical experiments and compare its performance against existing schemes.

Publication Type:	Article
Additional Information:	Please note the following regarding the use and licencing of the software: The software is available without any warranty. No liability for any possible damages applies. Although we will welcome the reports of software faults, we do not guarantee any actions as a result to such reports.
Publisher Keywords:	Stochastic volatility; linear and nonlinear reducible models; Pearson curves; moments; simulation
Subjects:	H Social Sciences > HF Commerce > HF5601 Accounting H Social Sciences > HG Finance Q Science > QA Mathematics
Departments:	Bayes Business School > Actuarial Science & Insurance Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

Export

Downloads