Goal achieving probabilities of cone‐constrained mean‐variance portfolios

In this paper, we establish closed‐form formulas for key probabilistic properties of the cone‐constrained optimal mean‐variance strategy, in a continuous market model driven by a multidimensional Brownian motion and deterministic coefficients. In particular, we compute the probability to obtain to a point, during the investment horizon, where the accumulated wealth is large enough to be fully reinvested in the money market, and safely grow there to meet the investor's financial goal at terminal time. We conclude that the result of Li and Zhou [Ann. Appl. Prob., v.16, pp.1751–1763, (2006)] in the unconstrained case carries over when conic constraints are present: the former probability is lower bounded by 80% no matter the market coefficients, trading constraints, and investment goal. We also compute the expected terminal wealth given that the investor's goal is underachieved, for both the mean‐variance strategy and the aforementioned hybrid strategy where transfer to the money market occurs if it allows to safely achieve the goal. The former probabilities and expectations are also provided in the case where all risky assets held are liquidated if financial distress is encountered. These results provide investors with novel practical tools to support portfolio decision‐making and analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Mean–variance portfolio selection under a constant elasticity of variance model

This paper discusses a mean–variance portfolio selection problem under a constant elasticity of variance model. A backward stochastic Riccati equation is first considered. Then we relate the solution of the associated stochastic control problem to that of the backward stochastic Riccati equation. Finally, explicit expressions of the optimal portfolio strategy, the value function and the efficient frontier of the mean–variance problem are expressed in terms of the solution of the backward stochastic Riccati equation.     

A multi-period stochastic portfolio optimization model applied for an airline company in the EU ETS

This paper aims to set up and solve a multi-period stochastic portfolio optimization model from an airline company’s point of view, considering all the specific European Union Emissions Trading Scheme (EU ETS) regulatory, managerial and trading constraints (i.e. physical constraints). Our contribution to existing academic literature is multiple. As the first ever case, we apply this technique to the aviation sector, a newly included sector within the EU ETS. More than mainly incorporating physical and technical (‘engineering’) features and focusing on short-term planning issues, we particularly address financial features and focus on mid-term planning issues. Therefore, instead of using spot prices, we run Monte Carlo simulations of correlated geometric Brownian motions (GBM) for traded futures prices of various emission allowance types for different CO₂ delivery time periods. We thereby specifically refer to the existing exchange-traded emission allowance types EU Emission Allowance (EUA) and Certified Emission Reduction (CER). By implementing actually valid and real-world-oriented regulatory constraints for EU ETS, namely managerial and trading constraints, our model implies a real-life application. We also highlight the possibility of banking and borrowing of emission allowances between CO₂ compliance periods, which is a crucial regulatory feature of EU ETS.     

New reformulations for probabilistically constrained quadratic programs

The mixed integer quadratic programming (MIQP) reformulation by Zheng, Sun, Li, and Cui (2012) for probabilistically constrained quadratic programs (PCQP) recently published in EJOR significantly dominates the standard MIQP formulation ( and ) which has been widely adopted in the literature. Stimulated by the dimensionality problem which Zheng et al. (2012) acknowledge themselves for their reformulations, we study further the characteristics of PCQP and develop new MIQP reformulations for PCQP with fewer variables and constraints. The results from numerical tests demonstrate that our reformulations clearly outperform the state-of-the-art MIQP in Zheng et al. (2012).     

On Extending the LP Computable Risk Measures to Account Downside Risk

A mathematical model of portfolio optimization is usually quantified with mean-risk models offering a lucid form of two criteria with possible trade-off analysis. In the classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. Following Sharpe’s work on linear approximation to the mean-variance model, many attempts have been made to linearize the portfolio optimization problem. There were introduced several alternative risk measures which are computationally attractive as (for discrete random variables) they result in solving linear programming (LP) problems. Typical LP computable risk measures, like the mean absolute deviation (MAD) or the Gini’s mean absolute difference (GMD) are symmetric with respect to the below-mean and over-mean performances. The paper shows how the measures can be further combined to extend their modeling capabilities with respect to enhancement of the below-mean downside risk aversion. The relations of the below-mean downside stochastic dominance are formally introduced and the corresponding techniques to enhance risk measures are derived.The resulting mean-risk models generate efficient solutions with respect to second degree stochastic dominance, while at the same time preserving simplicity and LP computability of the original models. The models are tested on real-life historical data.The research was supported by the grant PBZ-KBN-016/P03/99 from The State Committee for Scientific Research.     

基于流动性风险的开放式基金投资组合分析

不同于通常的投资组合方法,本文在开放式基金的投资组合中加进流动性风险,得出最佳流动性短缺概率和在流动性不同的有价证券上的最佳投资量.     

证券组合投资的多目标区间数线性规划模型

本文提出了证券组合投资的多目标区间数线性规划模型，引入了收益——风险偏好参数和优化水平参数。投资者可以根据对风险的喜好程度和金融市场的客观情况，适当估计这两个参数，从而得到相应情况下的有效投资方案，使投资过程更具柔性，而且更接近于实际情况。     

Multiperiod Portfolio Optimization with Terminal Liability: Bounds for the Convex Case

This paper is concerned with an investor trading in multiple securities over many time periods in order to meet an outstanding liability at some future date. The investor is concerned with maximizing the expected profits from portfolio rebalancing under an initial wealth restriction to meet the future liabilities. We formulate the problem as a discrete-time stochastic optimization model and allow asset prices to have continuous probability distributions on compact domains. For the case of Markovian price uncertainty and convex terminal liability, we develop a simplicial approximation, under which bounds on the problem can be computed efficiently. Computations only require evaluating a dynamic programming recursion, which thus, allows its application to problems with a large number of trading periods. The bounds are tight in that they are exact in certain cases. Numerical results are given to demonstrate the computational efficiency of the procedure.     

具有优良资产投资的最优证券组合专门化研究

利用均值 -方差模型 ,研究了具有优良资产的证券组合问题 ,讨论了投资组合专门化的概念、条件和所需要的信息量 ,并给出了一种对投资组合专门化实际情况判断的方法 .     

最小风险证券组合的结构分析和迭代算法

本文分析了最小风险组合证券投资的结构特征，并提出了一种组合证券风险最小化的迭代算法，证明了其收敛性．该算法操作简单，且易于处理不允许卖空情况下的证券组合问题．