کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5089428 | 1375592 | 2013 | 16 صفحه PDF | دانلود رایگان |
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.
⺠Parametric VaR based model (approximate model) is proposed for nonlinear portfolio optimization. ⺠It is shown that the approximate model can be translated into tractable second-order cone programming problem. ⺠It is shown that the proposed model can approximate the original one reasonably. ⺠Numerical simulation and empirical test show the efficiency of the proposed approach.
Journal: Journal of Banking & Finance - Volume 37, Issue 6, June 2013, Pages 2124-2139