Abstract
This paper presents results of multi-stage numerical optimisation of medium sized financial portfolios using High Performance Computing. Linear and non-linear constraints are applied and the optimisation process is designed to handle multiple markets in multiple countries, producing a series of alternative scenarios dependent upon a number of risk-rcturn requirements. The portfolio is allowed to contain instruments for which stochastic or historic data (or some combination) is available. Comparative performance is discussed for both real and artificial data sets and extrapolation to very large datasets will be presented. The comparative benefits of the deployment of large scale High Performance Computing in this class of problem are made.
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© 1998 Springer-Verlag Berlin Heidelberg
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Hodgson, G.S., Dzwig, P., Liddell, H.M., Parkinson, D. (1998). Application of HPC to medium-size stochastic systems with non-linear constraints in finance. In: Sloot, P., Bubak, M., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1998. Lecture Notes in Computer Science, vol 1401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037168
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DOI: https://doi.org/10.1007/BFb0037168
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