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Quadratic programming applications in finance using Excel

Author

Listed:
  • M Jackson

    (London Business School)

  • M D Staunton

    (London Business School)

Abstract

The paper describes two applications of quadratic programming in finance, one from the early years (Markowitz's efficient portfolios with minimum risk) and the other a more recent innovation (Sharpe's style analysis which estimates an implied asset allocation for an investment fund). We show how, in the presence of inequality constraints, Excel's Solver can be used to find the optimal weights in both quadratic programming applications. We also implement a direct analytic solution for generating the efficient frontier when there are no inequality constraints using the matrix functions in Excel. Both applications use only a small number of asset classes and require repeated use of the minimisation task. We show how Visual Basic for Applications (Microsoft's macro language for Excel) can be used to program such tasks, confirming that techniques that were the preserve of dedicated software only a few years ago can now be easily replicated using Excel to solve real problems.

Suggested Citation

  • M Jackson & M D Staunton, 1999. "Quadratic programming applications in finance using Excel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1256-1266, December.
    • Handle: RePEc:pal:jorsoc:v:50:y:1999:i:12:d:10.1057_palgrave.jors.2600839
    DOI: 10.1057/palgrave.jors.2600839
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    Cited by:

    1. Wang, Yuanrong & Aste, Tomaso, 2023. "Dynamic portfolio optimization with inverse covariance clustering," LSE Research Online Documents on Economics 117701, London School of Economics and Political Science, LSE Library.
    2. Naqvi, Bushra & Rizvi, Syed Kumail Abbas & Hasnaoui, Amir & Shao, Xuefeng, 2022. "Going beyond sustainability: The diversification benefits of green energy financial products," Energy Economics, Elsevier, vol. 111(C).
    3. Yuanrong Wang & Tomaso Aste, 2021. "Dynamic Portfolio Optimization with Inverse Covariance Clustering," Papers 2112.15499, arXiv.org, revised Jan 2022.
    4. Mike G. Tsionas & Dionisis Philippas & Constantin Zopounidis, 2023. "Exploring Uncertainty, Sensitivity and Robust Solutions in Mathematical Programming Through Bayesian Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 205-227, June.

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