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Optimising Credit Portfolio Using a Quadratic Nonlinear Projection Method

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  • Boguk Kim
  • Chulwoo Han
  • Frank Chongwoo Park

Abstract

A novel optimisation framework through quadratic nonlinear projection is introduced for credit portfolio when the portfolio risk is measured by Conditional Value-at-Risk (CVaR). The whole optimisation procedure to search toward the optimal portfolio state is conducted by a series of single-step optimisations under the local constraints described in the multi-dimensional constraint parameter space as functions of the total amount of portfolio adjustment. Each single-step optimisation is approximated by the first-order variation of the weight increments with respect to the total amount of portfolio adjustment and is solved in the form of locally exact formula formulated in the general Lagrange multiplier method. Our method can deal with optimisation for general nonlinear objective functions, such as the return-to-risk ratio maximisation or the diversification index, as well as the risk minimisation or the return maximisation.

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  • Boguk Kim & Chulwoo Han & Frank Chongwoo Park, 2014. "Optimising Credit Portfolio Using a Quadratic Nonlinear Projection Method," Papers 1411.2525, arXiv.org, revised Jul 2016.
    • Handle: RePEc:arx:papers:1411.2525
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    References listed on IDEAS

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    1. Renata Mansini & Włodzimierz Ogryczak & M. Speranza, 2007. "Conditional value at risk and related linear programming models for portfolio optimization," Annals of Operations Research, Springer, vol. 152(1), pages 227-256, July.
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