Optimisation of stochastic programming by hidden Markov modelling based scenario generation

Sovan Mitra and Tong Ji

International Journal of Mathematics in Operational Research, 2010, vol. 2, issue 4, 436-455

Abstract: Stochastic programming models provide a powerful paradigm for decision-making under uncertainty. In these models, the uncertainties are represented by a discrete scenario tree and the quality of the solutions obtained is governed by the quality of the scenarios generated. We propose a new technique to generate scenarios based on Gaussian mixture hidden Markov modelling. We show that our approach captures important time-varying dynamics of stochastic processes (such as autoregression and jumps) as well as non-Gaussian distribution characteristics (such as skewness and kurtosis). Our scenario generation method enables richer robustness and scenario analysis through exploiting the tractable properties of Gaussian mixture distributions. We provide the computational implementation of our scenario generation method, which includes calibration. We demonstrate the benefits of our scenario generation method by conducting numerical experiments on the FTSE-100 index.

Keywords: Markov processes; risk assessment; risk management; optimisation; Gaussian mixture; time-varying dynamics; autoregression; jumps, skewness; kurtosis; robustness analysis; stochastic programming; scenario generation; scenario analysis; financial mathematics; modelling; FTSE-100. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=33439 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:2:y:2010:i:4:p:436-455

Access Statistics for this article

More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().