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Divergent estimation error in portfolio optimization and in linear regression

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  • Imre Kondor
  • Istvan Varga-Haszonits

Abstract

The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.

Suggested Citation

  • Imre Kondor & Istvan Varga-Haszonits, 2007. "Divergent estimation error in portfolio optimization and in linear regression," Papers 0710.1855, arXiv.org.
    • Handle: RePEc:arx:papers:0710.1855
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    Cited by:

    1. Varga-Haszonits, Istvan & Caccioli, Fabio & Kondor, Imre, 2016. "Replica approach to mean-variance portfolio optimization," LSE Research Online Documents on Economics 68955, London School of Economics and Political Science, LSE Library.
    2. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.

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