Optimal portfolios: new variations of an old theme

We survey some recent developments in the area of continuous-time portfolio optimization. These will include the use of options and of defaultable assets as investment classes and the presentation of a worst-case investment approach that takes the possibility of stock market crashes into account.      

Competence-driven project portfolio selection,scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.     

A mixed integer programming model for multistage mean–variance post-tax optimization

In this paper, we introduce a mixed integer stochastic programming approach to mean–variance post-tax portfolio management. This approach takes into account of risk in a multistage setting and allows general withdrawals from original capital. The uncertainty on asset returns is specified as a scenario tree. The risk across scenarios is addressed using the probabilistic approach of classical stochastic programming. The tax rules are used with stochastic linear and mixed integer quadratic programming models to compute an overall tax and return-risk efficient multistage portfolio. The incorporation of the risk term in the model provides robustness and leads to diversification over wrappers and assets within each wrapper. General withdrawals and risk aversion have an impact on the distribution of assets among wrappers. Computational results are presented using a study with different scenario trees in order to show the performance of these models.     

Theory of optimal consumption and portfolio selection under a Capital-at-Risk (CaR) and a Value-at-Risk (VaR) constraint

The solution to the optimal portfolio selection and consumptionrule subject to Capital-at-Risk and Value-at-Risk constraintsis derived via the use of stochastic dynamic programming.     

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.     

个人住房抵押贷款模式及应用

提出了等比还贷，等差还贷，基于等额的灵活还贷和混合还贷4种模式，举例分析并指出了各模式适合的客户类型，最后对贷款利率调整时各模式的有关变化作了进一步的探讨，各种还贷模式可根据具体情况在实践中推广应用。     

基于利率市场化的银行贷款竞争导向定价策略研究

本文着重对两个银行之间竞争导向定价策略博弈行为进行了分析，认为在利率管制条件下，银行难以运用利率杠杆在信贷市场进行有效竞争；利率市场化之后，监管者不再对利率进行约束，银行信贷竞争的广度和深度都会随之加大。因此价格竞争将是银行信贷市场竞争的常态。     

LQ理论在参数随机的证券投资组合套期保值中的应用

在连续时间情形、不考虑交易费用、市场无摩擦假设,以及套期保值准则等条件下,考察了参数随机的证券投资组合中加入未定权益类衍生品形成的最优动态投资策略(u*(t)),并给出了该投资组合的最优模型所对应的黎卡提(Riccati)方程的解的存在性证明.     

Portfolio Selection Problem with Minimax Type Risk Function

The investor's preference in risk estimation of portfolio selection problems is important as it influences investment strategies. In this paper a minimax risk criterion is considered. Specifically, the investor aims to restrict the standard deviation for each of the available stocks. The corresponding portfolio optimization problem is formulated as a linear program. Hence it can be implemented easily. A capital asset pricing model between the market portfolio and each individual return for this model is established using nonsmooth optimization methods. Some numerical examples are given to illustrate our approach for the risk estimation.     

中国股市基本面价值加权投资组合研究

基本面价值加权投资组合是近年来在美国兴起的新型投资理论和技术.其基础是噪声市场假说和价格回归价值。本文回顾了基本面投资组合的相关文献,解释了其建立的理论基础和模型.本文还根据平滑市值权重技术,构建了基于中国股票市场的基本面投资组合,并和传统的市值加权投资组合进行比较。本文的主要结论是,中国市场1992年到2002年和美国市场类似呈现平稳价格噪声,基本面投资组合总体优于市值加权组合.股权分置改革初期市场低落,市场价格过度低估基本面价值,市值组合占优势。2005年下半年来股市呈现一定泡沫特征,市场价格对基本面价值有过度高估趋势,间接地证明了相关部门对股市过热的警告和监管是合理的.