Robust portfolio selection under norm uncertainty
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Abstract
In this paper, we consider the robust portfolio selection problem which has a data uncertainty described by the \((p,w)\)norm in the objective function. We show that the robust formulation of this problem is equivalent to a linear optimization problem. Moreover, we present some numerical results concerning our robust portfolio selection problem.
Keywords
portfolio selection \((p,w)\)norm uncertainty set linear optimization problemMSC
91G10 90C05 90C901 Introduction
Portfolio selection is the problem of allocating capital over a number of available assets in order to maximize the return on the investment while minimizing the risk. The first systematic approach to the problem of asset allocation under uncertainty is attributed to Markowitz [1]. An underlying assumption of Markowitz’s model is that precise estimates of \(\mu_{i}\) and \(\sigma_{i}\) have been obtained. Consequently, \(\mu_{i}\) and \(\sigma_{i}\) are treated as known constants; however, asset returns are variable. It is reasonable to conclude that a model which treats returns as known constants will produce a portfolio whose realized return is different from the optimal portfolio return given by the objective function value. In particular, when the realized asset returns are less than the estimates used to optimize the model, the realized portfolio return will be less than the optimal portfolio return given by the objective. Therefore, it is worthwhile exploring alternative frameworks, such as robust optimization, for application to the portfolio selection problem.
Although the distributions of asset returns are uncertain, in the robust optimization framework, we may assert that μ or σ, or both, belong to an uncertainty set, the bounds of which we can define. Most robust portfolio models describe asset returns by ellipsoidal uncertainty sets, based on the methodology of BenTal and Nemirovski [2, 3, 4] and El Ghaoui and Lebret [5], in which the user defined parameter Ω adjusts the guaranteed and achieved robustness of the portfolio. Previously, robustness has been evaluated based upon performance, particularly the worst case performance, then compared to the worst case performance of a nonrobust model such as the expected valuevariance model. In addition to the worst case performance, we suggest that it is also important to evaluate robustness based upon whether a model yields portfolios that achieve their guaranteed robustness in practice. In 2000, Lobo and Boyd [6] presented several different methods for modeling the uncertainty sets for the expected returns vector and covariance matrices, such as box or ellipsoidal sets. Each robust model was a semidefinite program solved via interior point methods. Their results focused on the performance of the solution method rather than on the robustness of the optimal portfolios. Goldfarb and Iyengar [7] defined asset returns by robust factor models in which the uncertainty was modeled by ellipsoidal sets. The robustness was evaluated based on performance, particularly in worst case scenarios, and compared to the expected valuevariance portfolio model. Results showed the worst case performance of the robust model was approximately 200% better than the nonrobust model; thus, they concluded that robust portfolios were more apt to withstand noisy data. For more about the robust portfolio selection problems with the ellipsoidal sets, we refer to [8, 9, 10, 11, 12, 13].
Recently, Bertsimas and Sim [14] proposed a different approach for robust linear optimization with polyhedral (as opposed to ellipsoidal) uncertainty sets. An attractive aspect of their method is that the new robust formulation is also a linear optimization problem. They also extended their methods to discrete optimization problems in a tractable way. In 2004, Bertsimas et al. [15] characterized the robust counterpart of a linear programming problem with an uncertainty set. They also showed that the approach of [14] follows from their conclusion by considering a norm, called the Dnorm, and its dual. Recently, Wang and Luo [16] considered the linear optimization problem which has a data uncertainty described by the \((p,w)\)norm. They showed that the \((p,w)\)norm includes the polyhedral norms \(L_{1}\), \(L_{\infty}\), and the Dnorm as special cases not only to make up for the disadvantages of the uncertain parameters of all possible values that will give the same weight, but also to consider the robust cost of the robust optimization model which is mentioned in [14, 15]. They also provided probabilistic guarantees on the feasibility of an optimal robust solution when the uncertain coefficients obey independent and identically distributed normal distributions.
Motivated and inspired by the work mentioned above, in this paper, we consider the robust portfolio selection problem with an uncertainty set described by the \((p,w)\)norm. We see that the robust formulation of this problem is a linear optimization problem. Moreover, we present some numerical results about our robust portfolio selection problem.
Here is the structure of this paper. In Section 2, we consider the robust portfolio selection problem which has data uncertainty described by \((p,w)\)norm in the objective function and we show that the robust formulation of this problem is equivalent to a linear optimization problem. In Section 3, we present some computational results on the performance of our robust portfolio selection problem. Section 4 concludes with a summary of this paper.
2 Robust portfolio selection
In this section, we discuss the formulation of the robust counterpart to the portfolio optimization problem. First of all, we consider the robust portfolio selection problem which has a data uncertainty described by the \((p,w)\)norm in the objective function. Then we show that the robust formulation of this problem is equivalent to a linear programming.
We denote by J the set of coefficients \(r_{i}\), \(i\in J\), that are subject to parameter uncertainty; i.e., \(\tilde{r}_{i}\), \(i\in J\) takes values according to a symmetric distribution with mean equal to the nominal value \(r_{i}\) in the interval \({}[ r_{i}\sigma_{i}, r_{i}+\sigma_{i}]\). For every i, we introduce a parameter p, which takes values in the interval \({}[0,J]\). The case is unlikely that all of the \(r_{i}\), \(i\in J\), will change, which is proposed by [17]. Our goal is to protect for the cases that up to \(\lceil p \rceil\) of these coefficients are allowed to change and take the worst case values at the same time. Next, we introduce the following definition of the \((p,w)\)norm.
Definition 2.1
(see [16])
Remark 2.1
(see [16])
 (1)
\(\y \_{p,w}\) is indeed a norm.
 (2)Ifthen the \((p,w)\)norm degenerates into Dnorm studied by Bertsimas et al. [15], i.e.,

\(y_{1}\ge y_{2}\ge\cdots\ge y_{n}\ge0\),

\(w_{1}=w_{2}=\cdots=w_{\lfloor p\rfloor}=1\), \(w_{\lceil p\rceil}=p\lfloor p\rfloor\), \(w_{i}\le w_{\lceil p\rceil}\), \(\lceil p\rceil< i\le n\),
$$ \y \_{p}=\max_{\{S\cup{t} S\subseteq J, S\le\lfloor p\rfloor ,t\in J\setminus S\}} \biggl\{ \sum _{j\in S} y_{j}+\bigl(p\lfloor p \rfloor\bigr) y_{t}\biggr\} . $$ 
 (3)
If \(w=(1,\ldots,1)^{T}\) and \(p=n\), then \((p,w)\)norm degenerates into \(L^{1}\) and one has \(\y\_{p,w}=\y\_{n,e}=\sum_{i=1}^{n} y_{i}\), \(i=1,\ldots,n\).
 (4)
If \(w=(1,\ldots,1)^{T}\) and \(p=1\), then \((p,w)\)norm degenerates into \(L^{\infty}\) and \(\y\_{p,w}=\y\_{1,e}=\maxy_{i}\), \(i=1,\ldots,n\).
We need the following proposition to reformulate (2.4) as a linear optimization problem.
Proposition 2.1
Proof
An optimal solution of problem (2.6) obviously consists of \(\lceil p \rceil\) variables at 1, which is equivalent to a subset \(\{SS\subseteq J,S=\lceil p\rceil\}\). The objective function of problem (2.6) converts to \(\sum_{j\in S}\sigma_{j} w_{j}x_{j}^{*}\), which is equivalent to problem (2.5). □
Next we will reformulate problem (2.4) as a linear optimization problem.
Theorem 2.1
Proof
3 Computational results
In this section, the experimental results show that our approach can get better riskadjusted returns than Bertsimas and Sim [14] with the same protection level, while the risk deviation is significantly smaller than Bertsimas and Sim [14] and our method can capture the balance between risks and benefits that is similar to the meanvariance model, and also it is more simple to get its linear structure.
We will use Matlab 2010 and Cplex 12.6 to solve this problem.
\(\pmb{(p,w)}\) Norm uncertainty approach
p  p exp. return  p min. return  p max. return  p dev 

0  1.2000  0.9115  1.4885  0.2885 
5  1.1847  1.1108  1.2586  0.0186 
10  1.1785  1.1298  1.2272  0.0123 
15  1.1739  1.1349  1.2130  0.0094 
20  1.1689  1.1409  1.1968  0.0063 
25  1.1671  1.1444  1.1898  0.0047 
30  1.1617  1.1435  1.1799  0.0036 
35  1.1582  1.1449  1.1715  0.0025 
40  1.1510  1.1501  1.1519  8.8294e − 004 
45  1.1513  1.1504  1.1523  9.2802e − 004 
Bertsimas and Sim approach
p  D exp. return  D min. return  D max. return  D dev 

0  1.2000  0.9115  1.4885  0.2885 
5  1.1844  1.0896  1.2793  0.0254 
10  1.1776  1.1075  1.2478  0.0192 
15  1.1716  1.1147  1.2285  0.0151 
20  1.1678  1.1165  1.2190  0.0126 
25  1.1678  1.1114  1.2241  0.0126 
30  1.1678  1.1063  1.2293  0.0126 
35  1.1678  1.1012  1.2344  0.0126 
40  1.1678  1.0960  1.2395  0.0126 
45  1.1503  1.1267  1.1740  0.0236 
Ellipsoidal method
θ  S exp. return  S min. return  S max. return  S dev 

0  1.2000  0.9104  1.4896  0.2896 
1  1.1836  0.9492  1.1481  0.0235 
\(\sqrt{150}\)  1.1503  1.1267  1.1740  0.0236 
4 Conclusions
In this paper, we consider the robust portfolio selection problem which has data uncertainty described by the \((p,w)\)norm not only to make up for the disadvantages of the uncertain parameters of all possible values that will give the same weight, but also to consider the robust cost of the robust optimization model which is mentioned in [14]. We see that the robust formulation of this problem is a linear optimization problem. Moreover, we present some numerical results concerning our robust portfolio selection problem.
Notes
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 11401484 and 41301488). Computations made in this paper were performed using Matlab 2010 and Cplex 12.6.
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