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Parallelization and Performance of Portfolio Choice Models

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  • A. Abdelkhalek, A. Bilas and A. Michaelides

Abstract

We show how applications in computational economics can take advantage of modern parallel architectures to reduce the computation time in a wide array of models that have been, to date, computationally intractable. The specific application comes from solving a portfolio choice model over the lifecycle in the presence of undiversifiable labor income risk, borrowing and short sale constraints. We provide an efficient parallel implementation and introduce a new benchmark for parallel computer architectures from an emerging and important class of applications. We conclude that emerging applications in this area of computational economics exhibit adequate parallelism to achieve, after a number of optimization steps, almost linear speedup for system sizes up to 64 processors on today's hardware shared memory multiprocessors.

Suggested Citation

  • A. Abdelkhalek, A. Bilas and A. Michaelides, 2001. "Parallelization and Performance of Portfolio Choice Models," Computing in Economics and Finance 2001 114, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:114
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    Cited by:

    1. Jurgen A. Doornik & Neil Shephard & David F. Hendry, 2004. "Parallel Computation in Econometrics: A Simplified Approach," Economics Papers 2004-W16, Economics Group, Nuffield College, University of Oxford.
    2. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
    3. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 151-182, August.
    4. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.
    5. Yongyang Cai & Kenneth Judd & Greg Thain & Stephen Wright, 2015. "Solving Dynamic Programming Problems on a Computational Grid," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 261-284, February.

    More about this item

    Keywords

    Parallel Programming; Portfolio Choice;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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