Abstract
This chapter highlights several novel methods for portfolio optimization. One approach involves using more than one risk model and a systematic calibration procedure is described for incorporating more than one risk model in a portfolio construction strategy. The addition of a second risk model can lead to better overall performance than one risk model alone provided that the strategy is calibrated so that both risk models affect the optimal portfolio solution. In addition, the resulting portfolio is no more conservative than the portfolio obtained with one risk model alone. The second approach addresses the issue of generating multiple interesting solutions to the portfolio optimization problem. It borrows the concept of “elasticity” from Economics, and adapts it within the framework of portfolio optimization to evaluate the relative significance of various constraints in the strategy. By examining heatmaps of portfolio characteristics derived by perturbing constraints with commensurable elasticities, it offers insights into trade-offs associated with modifying constraint bounds. Not only do these techniques assist in enhancing our understanding of the terrain of optimal portfolios, they also offer the unique opportunity to visualize trade-offs associated with mathematically intractable metrics such as transfer coefficient. A carefully designed case study elucidates the practical utility of these techniques in generating multiple interesting solutions to portfolio optimization.
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