Unified Moment-Based Modeling of Integrated Stochastic Processes

Ioannis Kyriakou (), Riccardo Brignone () and Gianluca Fusai ()
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Ioannis Kyriakou: Faculty of Actuarial Science & Insurance, Bayes Business School (formerly Cass), City, University of London, London EC1Y 8TZ, United Kingdom
Riccardo Brignone: Department of Quantitative Finance, Faculty of Economics and Behavioural Science, University of Freiburg, 79098 Freiburg im Breisgau, Germany
Gianluca Fusai: Dipartimento di Studi per l’Economia e l’Impresa, Università del Piemonte Orientale, 28100 Novara, Italy; Faculty of Finance, Bayes Business School (formerly Cass), City, University of London, London EC1Y 8TZ, United Kingdom

Operations Research, 2024, vol. 72, issue 4, 1630-1653

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.

Keywords: Simulation; stochastic volatility; linear and nonlinear reducible models; Pearson curves; moments; simulation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:4:p:1630-1653

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