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Nonlinear portfolio selection using approximate parametric Value-at-Risk
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DOI：10.1016/j.jbankfin.2013.01.036.png)
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As the skewed return distribution is a prominent feature in nonlinear portfolio selection problems which involve derivative assets with nonlinear payoff structures, Value-at-Risk (VaR) is particularly suitable to serve as a risk measure in nonlinear portfolio selection. Unfortunately, the nonlinear portfolio selection formulation using VaR risk measure is in general a computationally intractable optimization problem. We investigate in this paper nonlinear portfolio selection models using approximate parametric Value-at-Risk. More specifically, we use first-order and second-order approximations of VaR for constructing portfolio selection models, and show that the portfolio selection models based on Delta-only, Delta-Gamma-normal and worst-case Delta-Gamma VaR approximations can be reformulated as second-order cone programs, which are polynomially solvable using interior-point methods. Our simulation and empirical results suggest that the model using Delta-Gamma-normal VaR approximation performs the best in terms of a balance between approximation accuracy and computational efficiency. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Portfolio selection
Value-at-Risk
European option
Delta-Gamma approximation
Second-order cone programming
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IF:
3.8
论文数:
6.4K
被引数:
2.4W
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