Invited Session Tue.1.H 3027

Tuesday, 10:30 - 12:00 h, Room: H 3027

Cluster 7: Finance & economics [...]

Portfolio optimization


Chair: John Birge



Tuesday, 10:30 - 10:55 h, Room: H 3027, Talk 1

Sergio Ortobelli Lozza
On the impact of some distributional factors in large scale portfolio problems

Coauthors: valeria caviezel, Tomás Tichý


In this paper, we examine the possibility to estimate the return distributions using a principal component analysis applied to different semidefinite positive correlation matrices. Using a recent classification of semidefinite positive correlation measures we are able to value the impact
of different distributional factors in the choices under uncertainty conditions. In particular we investigate the opportunity to reduce the complexity of large scale portfolio selection problems using some concordance measures. We first analyze the large scale static problem and then we discuss a first extension to the dynamic portfolio problem. Finally we propose an empirical application to the large scale portfolio problem.



Tuesday, 11:00 - 11:25 h, Room: H 3027, Talk 2

Jun-Ya Gotoh
Robust portfolio techniques for coherent risk minimization

Coauthors: Keita Shinozaki, Akiko Takeda


Coherent measures of risk have gained growing popularity in financial risk management during the first decade of this century. However, optimal solutions to their minimization are highly susceptible to estimation error of the risk measure because the estimate depends only on a portion of sampled scenarios. In this talk, by employing robust optimization modeling for minimizing coherent risk measures, we present a couple of ways for making the solution robust over a certain range of estimation errors. Specifically, we show that a worst-case coherent risk minimization leads to a penalized minimization of the empirical risk estimate. Besides, inspired by Konno, Waki and Yuuki (2002) we examine the use of factor model in coherent risk minimization. In general, the factor model-based coherent risk minimization along the lines of Goldfarb and Iyengar (2003) is shown to be intractable, and we present a global optimization algorithm for solving the intractable case. Numerical experiment shows that robust approaches achieve better out-of-sample performance than the empirical minimization and market benchmarks.



Tuesday, 11:30 - 11:55 h, Room: H 3027, Talk 3

Romy Shioda
Factor alignment problem in quantitative portfolio management

Coauthors: Anureet Saxena, Robert Stubbs


The underestimation of risk of optimized portfolios is a consistent criticism about risk models and optimization. Quantitative portfolio managers have historically used a variety of ad hoc techniques to overcome this issue in their investment processes. In this talk, we construct a theory explaining why risk models underestimate the risk of optimized portfolios. We show that the problem is not necessarily with a risk model, but is rather the interaction between alphas, constraints, and risk factors in the risk model. We develop an optimization technique that incorporates a dynamic Alpha Alignment Factor (AAF) into the factor risk model during the optimization process. Using actual portfolio manager backtests, we illustrate both how pervasive the underestimation problem can be and the effectiveness of the proposed AAF in correcting the bias of the risk estimates of optimized portfolios.


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