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Portfolio selection with divisible and indivisible assets: Mathematical algorithm and economic analysis

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  • Rafael Lazimy

Abstract

We extend the classic mean-variance framework to a broad class of investment decisions under risk where investors select optimal portfolios of risky assets that include perfectly divisible as well as perfectly indivisible assets. We develop an algorithm for solving the associated mixed-integer nonlinear program and report on the results of a computational study. We then study the mean-variance structure of the investment frontier facing an individual investor in the presence of investment opportunities in both risky divisible and indivisible assets. Finally, we analyze the economic implications of the presence of investment opportunities in risky indivisible assets on the investor’s investment strategy and on his risk evaluation. Copyright Springer Science+Business Media, LLC 2007

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  • Rafael Lazimy, 2007. "Portfolio selection with divisible and indivisible assets: Mathematical algorithm and economic analysis," Annals of Operations Research, Springer, vol. 152(1), pages 273-295, July.
  • Handle: RePEc:spr:annopr:v:152:y:2007:i:1:p:273-295:10.1007/s10479-006-0141-5
    DOI: 10.1007/s10479-006-0141-5
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    1. Roger G. Ibbotson & Laurence B. Siegel, 1984. "Real Estate Returns: A Comparison with Other Investments," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 12(3), pages 219-242, September.
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    6. Merton, Robert C., 1972. "An Analytic Derivation of the Efficient Portfolio Frontier," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(4), pages 1851-1872, September.
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    Cited by:

    1. Marcello D'Amato & Christian Di Pietro & Marco M. Sorge, 2023. "Left and Right: A Tale of Two Tails of the Wealth Distribution," CSEF Working Papers 691, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    2. Rafael Lazimy, 2013. "Interactive Polyhedral Outer Approximation (IPOA) strategy for general multiobjective optimization problems," Annals of Operations Research, Springer, vol. 210(1), pages 73-99, November.

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