资产组合鲁棒优化模型及应用研究

本文在对资产组合鲁棒优化理论归纳总结的基础上,根据我国实际情况,考虑未来经济因素的不确定性,建立了相应的资产组合鲁棒优化模型。对基金公司的投资决策、银行卡网络资金分配、VaR约束下的资产组合选择等实际问题进行了研究。针对每一个具体问题,调整和改进了模型的目标函数和约束条件,用相应的不确定集描述有关的未来不确定经济因素,得到了鲁棒优化结果,使得资产组合决策兼具可行性和最优性。     

鲁棒均值-CVaR投资组合模型及实证:基于安全准则的视角

考虑证券市场的不确定性,将资产的收益率看成区间随机变量。利用鲁棒优化方法,构建鲁棒均值-CVaR投资组合模型。采用对偶理论,将鲁棒均值-CVaR投资组合模型转换为线性规划问题,降低了模型的求解难度,有助于计算大规模的资产组合。进一步地,考虑投资者的安全性需求,在模型中引入最大违反概率,控制模型的保守程度,并直观反映投资者的安全性要求。采用实证的方法,研究模型的有效性。结果表明:鲁棒均值-CVaR投资组合模型具有较好的稳健性,且满足投资者的安全性要求,在实际的投资决策中具有可行性。     

金融风险的最坏VaR方法与投资组合优化

在股价及其走势均不确定的情况下,采用最坏VaR方法,对投资的潜在损失进行最保守的度量,并得到其等价的优化形式为一个二阶锥优化问题.接着考虑相应的投资组合优化问题:如何选择合适的头寸,使得当股票组合的期望收益达到给定水平的情况下,风险最低,即最坏VaR值最小,最后对模型进行实证分析.     

一类分布鲁棒优化问题的线性化方法及其应用

本文考虑一类特殊的极大极小化问题,即分布鲁棒优化问题.这类优化方法是不同于随机规划和鲁棒优化的一类方法,在这类问题中,不确定变量的概率分布往往是不能精确得知的,只知道概率分布所满足的一些条件,比如一次信息、二次信息以及支撑集合信息等.如此分布鲁棒优化问题便是寻求在所有满足条件的分布中找寻满足最坏可能分布的解.一般情况下,这类优化问题的求解都是NP难的.本文考虑一类简单的情形,即考虑不确定变量的概率分布只满足一次信息、支撑集合信息以及仿射一次信息,通过应用半无限规划问题的对偶性,本文指出这类分布鲁棒优化问题等价于线性规划问题,从而原分布鲁棒优化问题可以应用现成的求解线性规划的方法进行求解.为验证方法的有效性,本文将新方法应用于解决不确定条件下含有交易费用的利率管理问题.     

二阶随机占优约束下考虑偏度的投资组合模型

在DentchevaRuszczynski(2006)模型的基础上,考虑偏度对构建投资组合的影响,建立了二阶随机占优约束下最大化组合收益率偏度的投资组合优化模型,并应用分段线性近似方法将模型转化为一个非线性混合整数规划问题.利用中国股票市场的历史数据对所建模型进行了实证分析,结果表明,所建新模型比均值-方差-偏度模型和市场指数具有更稳健的表现.     

考虑交易费用的二阶随机占优投资组合风险控制模型

本文通过引入交易费用函数,建立了一个更符合实际的带有二阶随机占优约束的投资组合风险控制模型.该模型不需要对投资者的效用函数和风险资产收益的分布作任何假设,就可以确保风险厌恶投资者所做的选择都会随机占优于一个基准值,从而可以规避高风险投资.针对优化模型的求解,设计了一种光滑化样本平均值近似罚函数方法,理论上证明了光滑化罚问题与原问题的等价性.数值结果验证了模型和算法的有效性.     

考虑决策者心理行为的证券投资组合决策方法研究

在现实的证券投资组合决策中,决策者的心理行为是不可忽视的重要因素。本文针对考虑决策者心理行为的证券投资组合问题,给出了一种基于累积前景理论和心理账户的决策分析方法。首先,依据累积前景理论,将决策者对不同市场状态下的预期收益率作为参考点,计算各备选证券收益率相对于参照点的收益和损失,并计算不同市场状态下针对所有备选证券的综合前景价值;然后,依据决策者的心理账户,即以证券投资组合的收益总体综合前景价值最大为目标、以投资期末总财富阈值以及满足财富约束的概率不小于决策者设定的概率阈值为约束,构建了具有概率约束条件的证券投资组合优化模型,通过将概率约束转化为线性约束并求解优化模型,可得到最优的证券投资组合方案。最后,通过一个算例对本文提出方法的可行性和有效性进行了验证。研究结果表明,本文提出的方法能够较好地解决考虑决策者心理行为的证券投资组合问题。     

不确定信息下分式半无限优化问题的近似最优性刻画

该文研究了一类带不确定参数的多目标分式半无限优化问题。首先借助鲁棒优化方法,引入该不确定多目标分式优化问题的鲁棒对应优化模型,并借助Dinkelbach方法,将该鲁棒对应优化模型转化为一般的多目标优化问题。随后借助一种标量化方法,建立了该优化问题的标量化问题,并刻画了它们的解之间的关系。最后借助一类鲁棒型次微分约束规格,建立了该不确定多目标分式优化问题拟近似有效解的鲁棒最优性条件。     

考虑投资者风险态度的随机模糊投资组合模型及实证

考虑投资者面临证券市场随机和模糊的双重不确定性,把证券收益率视为随机模糊变量。在前景理论下考虑投资者的风险态度,建立不同的随机模糊收益率、期望收益隶属度函数和目标权重,构建考虑投资者风险态度的随机模糊投资组合模型。采用实证方法把市场分为下降和上升两个阶段,研究不同风险态度投资者的投资组合差异及模型表现。结果表明:投资者的风险态度会影响投资组合的结构;考虑投资者风险态度的随机模糊投资组合模型,能够满足不同风险态度投资者对投资收益和风险的差异需求,且在实际投资决策中具有可行性。     

不确定信息多目标线性优化的鲁棒方法

研究不确定信息的多目标线性优化问题,其数据不能精确给出但是属于一个给定的集合.首先,采用鲁棒方法把该问题转化为一个确定的多目标优化问题.然后,给出此问题解存在的充分条件.最后,通过实例验证了用鲁棒方法解决不确定信息的多目标线性优化问题的有效性.     

Optimal enough?

An alleged weakness of heuristic optimisation methods is the stochastic character of their solutions: instead of finding the truly optimal solution, they only provide a stochastic approximation of this optimum. In this paper we look into a particular application, portfolio optimisation. We demonstrate that the randomness of the ‘optimal’ solution obtained from the algorithm can be made so small that for all practical purposes it can be neglected. More importantly, we look at the relevance of the remaining uncertainty in the out-of-sample period. The relationship between in-sample fit and out-of-sample performance is not monotonous, but still, we observe that up to a point better solutions in-sample lead to better solutions out-of-sample. Beyond this point there is no more cause for improving the solution any further: any in-sample improvement leads out-of-sample only to financially meaningless improvements and unpredictable changes (noise) in performance.     

基于动态参照点的鲁棒投资组合模型及其拓展

鲁棒投资组合模型是一种适用于收益不确定条件下寻求最优决策的方法。首先考虑投资者对底线的重视,根据当收益触及底线时,激进者和保守者在参照点上的不同变化情况,建立动态参照点模型。接着,一方面将动态参照点作为划分获益和损失的界限值,改进现有的Worst-case Omega(WOmega)模型。另一方面结合投资者对下侧风险更为厌恶的特点,以动态参照点作为下侧风险的基准,改进现有的Relative Robust Portfolio Optimization(RRPO)模型。实证研究中,对于WOmega类模型,结果表明激进行为模型在样本内表现较好,而保守行为模型在样本外表现较好。对于RRPO类模型,结果显示激进行为的收益表现良好,保守行为对标准差及最大损失值的控制较好。随着约束的放松,所有模型的收益都能得到可观提升。     

The opportunity cost of mean–variance choice under estimation risk

Mean–variance portfolio choice is often criticized as sub-optimal in the more general expected utility framework. It is argued that the expected utility framework takes into consideration higher moments ignored by mean variance analysis. A body of research suggests that mean–variance choice, though arguably sub-optimal, provides very close-to-expected utility maximizing portfolios and their expected utilities, basing its evaluation on in-sample analysis where mean–variance choice is sub-optimal by definition. In order to clarify this existing research, this study provides a framework that allows comparing in-sample and out-of-sample performance of the mean variance portfolios against expected utility maximizing portfolios. Our in-sample results confirm the results of earlier studies. On the other hand, our out-of-sample results show that the expected utility model performs worse. The out-of-sample inferiority of the expected utility model is more pronounced for preferences and constraints under which in-sample mean variance approximations are weakest. We argue that, in addition to its elegance and simplicity, the mean–variance model extracts more information from sample data because it uses the covariance matrix of returns. The expected utility model may reach its optimal solution without using information from the covariance matrix.     

Robust ranking and portfolio optimization

The portfolio optimization problem has attracted researchers from many disciplines to resolve the issue of poor out-of-sample performance due to estimation errors in the expected returns. A practical method for portfolio construction is to use assets’ ordering information, expressed in the form of preferences over the stocks, instead of the exact expected returns. Due to the fact that the ranking itself is often described with uncertainty, we introduce a generic robust ranking model and apply it to portfolio optimization. In this problem, there are n objects whose ranking is in a discrete uncertainty set. We want to find a weight vector that maximizes some generic objective function for the worst realization of the ranking. This robust ranking problem is a mixed integer minimax problem and is very difficult to solve in general. To solve this robust ranking problem, we apply the constraint generation method, where constraints are efficiently generated by solving a network flow problem. For empirical tests, we use post-earnings-announcement drifts to obtain ranking uncertainty sets for the stocks in the DJIA index. We demonstrate that our robust portfolios produce smaller risk compared to their non-robust counterparts.     

On the effectiveness of scenario generation techniques in single-period portfolio optimization

In single-period portfolio selection problems the expected value of both the risk measure and the portfolio return have to be estimated. Historical data realizations, used as equally probable scenarios, are frequently used to this aim. Several other parametric and non-parametric methods can be applied. When dealing with scenario generation techniques practitioners are mainly concerned on how reliable and effective such methods are when embedded into portfolio selection models. In this paper we survey different techniques to generate scenarios for the rates of return. We also compare the techniques by providing in-sample and out-of-sample analysis of the portfolios obtained by using these techniques to generate the rates of return. Evidence on the computational burden required by the different techniques is also provided. As reference model we use the Worst Conditional Expectation model with transaction costs. Extensive computational results based on different historical data sets from London Stock Exchange Market (FTSE) are presented and some interesting financial conclusions are drawn.     

Robust portfolio optimization with derivative insurance guarantees

Robust portfolio optimization aims to maximize the worst-case portfolio return given that the asset returns are allowed to vary within a prescribed uncertainty set. If the uncertainty set is not too large, the resulting portfolio performs well under normal market conditions. However, its performance may substantially degrade in the presence of market crashes, that is, if the asset returns materialize far outside of the uncertainty set. We propose a novel robust optimization model for designing portfolios that include European-style options. This model trades off weak and strong guarantees on the worst-case portfolio return. The weak guarantee applies as long as the asset returns are realized within the prescribed uncertainty set, while the strong guarantee applies for all possible asset returns. The resulting model constitutes a convex second-order cone program, which is amenable to efficient numerical solution procedures. We evaluate the model using simulated and empirical backtests and analyze the impact of the insurance guarantees on the portfolio performance.     

Portfolio selection with a minimax measure in safety constraint

In this paper, we attempt to design a portfolio optimization model for investors who desire to minimize the variation around the mean return and at the same time wish to achieve better return than the worst possible return realization at every time point in a single period portfolio investment. The portfolio is to be selected from the risky assets in the equity market. Since the minimax portfolio optimization model provides us with the portfolio that maximizes (minimizes) the worst return (worst loss) realization in the investment horizon period, in order to safeguard the interest of investors, the optimal value of the minimax optimization model is used to design a constraint in the mean-absolute semideviation model. This constraint can be viewed as a safety strategy adopted by an investor. Thus, our proposed bi-objective linear programming model involves mean return as a reward and mean-absolute semideviation as a risk in the objective function and minimax as a safety constraint, which enables a trade off between return and risk with a fixed safety value. The efficient frontier of the model is generated using the augmented -constraint method on the GAMS software. We simultaneously solve the ratio optimization problem which maximizes the ratio of mean return over mean-absolute semideviation with same minimax value in the safety constraint. Subsequently, we choose two portfolios on the above generated efficient frontier such that the risk from one of them is less and the mean return from other portfolio is more than the respective quantities of the optimal portfolio from the ratio optimization model. Extensive computational results and in-sample and out-of-sample analysis are provided to compare the financial performance of the optimal portfolios selected by our proposed model with that of the optimal portfolios from the existing minimax and mean-absolute semideviation portfolio optimization models on real data from S&P CNX Nifty index.     

Short sales in Log-robust portfolio management

This paper extends the Log-robust portfolio management approach to the case with short sales, i.e., the case where the manager can sell shares he does not yet own. We model the continuously compounded rates of return, which have been established in the literature as the true drivers of uncertainty, as uncertain parameters belonging to polyhedral uncertainty sets, and maximize the worst-case portfolio wealth over that set in a one-period setting. The degree of the manager’s aversion to ambiguity is incorporated through a single, intuitive parameter, which determines the size of the uncertainty set. The presence of short-selling requires the development of problem-specific techniques, because the optimization problem is not convex. In the case where assets are independent, we show that the robust optimization problem can be solved exactly as a series of linear programming problems; as a result, the approach remains tractable for large numbers of assets. We also provide insights into the structure of the optimal solution. In the case of correlated assets, we develop and test a heuristic where correlation is maintained only between assets invested in. In computational experiments, the proposed approach exhibits superior performance to that of the traditional robust approach.     

Robustness of optimal portfolios under risk and stochastic dominance constraints

Solutions of portfolio optimization problems are often influenced by a model misspecification or by errors due to approximation, estimation and incomplete information. The obtained results, recommendations for the risk and portfolio manager, should be then carefully analyzed. We shall deal with output analysis and stress testing with respect to uncertainty or perturbations of input data for static risk constrained portfolio optimization problems by means of the contamination technique. Dependence of the set of feasible solutions on the probability distribution rules out the straightforward construction of convexity-based global contamination bounds. Results obtained in our paper [Dupa?ová, J., & Kopa, M. (2012). Robustness in stochastic programs with risk constraints. Annals of Operations Research, 200, 55–74.] were derived for the risk and second order stochastic dominance constraints under suitable smoothness and/or convexity assumptions that are fulfilled, e.g. for the Markowitz mean–variance model. In this paper we relax these assumptions having in mind the first order stochastic dominance and probabilistic risk constraints. Local bounds for problems of a special structure are obtained. Under suitable conditions on the structure of the problem and for discrete distributions we shall exploit the contamination technique to derive a new robust first order stochastic dominance portfolio efficiency test.     

Robust portfolio choice with CVaR and VaR under distribution and mean return ambiguity

We consider the problem of optimal portfolio choice using the Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) measures for a market consisting of n risky assets and a riskless asset and where short positions are allowed. When the distribution of returns of risky assets is unknown but the mean return vector and variance/covariance matrix of the risky assets are fixed, we derive the distributionally robust portfolio rules. Then, we address uncertainty (ambiguity) in the mean return vector in addition to distribution ambiguity, and derive the optimal portfolio rules when the uncertainty in the return vector is modeled via an ellipsoidal uncertainty set. In the presence of a riskless asset, the robust CVaR and VaR measures, coupled with a minimum mean return constraint, yield simple, mean-variance efficient optimal portfolio rules. In a market without the riskless asset, we obtain a closed-form portfolio rule that generalizes earlier results, without a minimum mean return restriction.