不完全市场下基于对数效用的动态投资组合

应用鞅方法研究不完全市场下的动态投资组合优化问题。首先,通过降低布朗运动的维数将不完全金融市场转化为完全金融市场,并在转化后的完全金融市场里应用鞅方法研究对数效用函数下的动态投资组合问题,得到了最优投资策略的显示表达式。然后,根据转化后的完全金融市场与原不完全金融市场之间的参数关系,得到原不完全金融市场下的最优投资策略。算例分析比较了不完全金融市场与转化后的完全金融市场下最优投资策略的变化趋势,并与幂效用、指数效用下最优投资策略的变化趋势做了比较。     

It模型金融市场的完备性

本文研究了标的资产价格服从连续时间It过程模型的金融市场的完备性问题。在允许有摩擦的金融市场中,当可允许投资策略取值于一个非空凸闭集时,给出了金融市场在内蕴完备条件下广义不完备的充分必要条件.     

It(o)模型金融市场的完备性

本文研究了标的资产价格服从连续时间Ito过程模型的金融市场的完备性问题．在允许有摩擦的金融市场中，当可允许投资策略取值于一个非空凸闭集时，给出了金融市场在内蕴完备条件下广义不完备的充分必要条件．     

具有交易费用和马氏调制的均值-方差组合选择

研究了均值-方差准则下,最优投资组合选择问题.投资者为了增加财富它可以在金融市场上投资.金融市场由一个无风险资产和n个带跳的风险资产组成,并假设金融市场具有马氏调制,买卖风险资产时,考虑交易费用.目标是,在终值财富的均值等于d的限制下,使终值财富的方差最小,即均值-方差组合选择问题.应用随机控制的理论解决该问题,获得了最优的投资策略和有效边界.     

最优消费投资的动态经济模型研究(Ⅰ)

本文研究了金融市场上投资者消费效用优化的随机控制问题．设金融市场上有一个局部无风险的资产和ｄ个风险资产,其价格服从连续的Ｉｔｏ模型．在效用折扣过程为有限分段函数情形下,得出了关于目前财富反馈形式的最优消费投资公式．     

概率准则在投资模型中的应用研究

本文在Black-Scholes金融市场设置下，基于概率准则，研究连续时间金融市场最优动态资产组合的选择问题，导出了最优解的显式表达式，对结论给出了金融学解释，所得结果可以方便地应用于投资决策与管理实践中。     

单时期证券市场的最优投资组合

考虑了单时期金融市场模型的最优投资组合问题,并对一般的效用函数,给出了最优投资组合问题有解的一个充分条件.     

基于ICA-SV模型的金融市场协同波动溢出分析及实证研究

对于动态投资组合与风险管理来说,测定波动溢出效应是非常重要的.已有的文献证明SV模型比GARCH模型能够更好地刻画金融市场的波动,使用SV模型研究两个金融市场间波动溢出的文献并不多见,而使用SV模型研究多个金融市场对一个金融市场协同波动溢出的文献则更为少见.本文以独立成分表示金融市场波动的协同指标,提出了独立成分SV模型(ICA-SV),并研究了多个金融市场对一个金融市场的协同波动溢出,实证结果验证了ICA-SV模型在分析金融市场协同波动溢出是可行的.     

最优消费投资的动态经济模型研究（I）

本文研究了金融市场上投资者消费效用优化的随机控制问题。设金融市场上有一个局部无风险的资产和ｄ个风险资产,其价格服从连续的Ｉｔｏ模型。在效用折扣过程为有限分段函数情形下,得出了关于目前财富反馈形式的最优消费投资公式。     

具有随机工资的养老金最优投资问题

在三种目标函数下, 研究了具有随机工资的养老金最优投资问题. 第一种是均值-方差准则, 第二种基于效用的随机微分博弈, 第三种基于均值-方差准则的随机微分博弈. 随机微分博弈问题中博弈的双方为养老金计划投资者和金融市场, 金融市场是博弈的虚拟手. 应用线性二次控制理论求得了三种目标函数下的最优策略和值函数的显式解.     

On a Mathematical Comparison between Hierarchy and Network with a Classification of Coordination Structures

This paper proposes a mathematical model to compare a network organization with a hierarchical organization. In order to formulate the model, we define a three-dimensional framework of the coordination structure of a network and of other typical coordination structures. In the framework, we can define a network structure by contrasting it with a hierarchy, in terms of the distribution of decision making, which is one of the main features of information processing. Based on this definition, we have developed a mathematical model for evaluating coordination structures. Using this model, we can derive two boundary conditions among the coordination structures with respect to the optimal coordination structure. The boundary conditions help us to understand why an organization changes its coordination structure from a hierarchy to a network and what factors cause this change. They enable us, for example, to find points of structural change where the optimal coordination structure shifts from a hierarchy to a hierarchy with delegation or from a hierarchy with delegation to a network, when the nature of the task changes from routine to non-routine. In conclusion, our framework and model may provide a basis for discussing the processes that occur when coordination structures change between a hierarchy and a network.     

Analytical existence of solutions to a system of nonlinear equations with application

We study the existence of analytical solutions to a system of nonlinear equations under constraints linked to the analysis of a road safety measure without computing second derivatives. We formally demonstrate this existence of solutions by applying a matrix inversion principle through Schur complement to a subsystem of equations derived from the main system. The analytical results thus obtained are used to construct a simple iterative procedure to look for optimal solutions as well as an initial solution adapted to data of each case study. We illustrate our results with simulated accident data obtained from the setting-up of a road safety measure. The numerical solutions thus obtained are then compared to those given through a classic Newton-Raphson type approach directly applied to the main system.     

Improving the computational efficiency of metric-based spares algorithms

We propose a new heuristic algorithm to improve the computational efficiency of the general class of Multi-Echelon Technique for Recoverable Item Control (METRIC) problems. The objective of a METRIC-based decision problem is to systematically determine the location and quantity of spares that either maximizes the operational availability of a system subject to a budget constraint or minimizes its cost subject to an operational availability target. This type of sparing analysis has proven essential when analyzing the sustainment policies of large-scale, complex repairable systems such as those prevalent in the defense and aerospace industries. Additionally, the frequency of these sparing studies has recently increased as the adoption of performance-based logistics (PBL) has increased. PBL represents a class of business strategies that converts the recurring cost associated with maintenance, repair, and overhaul (MRO) into cost avoidance streams. Central to a PBL contract is a requirement to perform a business case analysis (BCA) and central to a BCA is the frequent need to use METRIC-based approaches to evaluate how a supplier and customer will engage in a performance based logistics arrangement where spares decisions are critical. Due to the size and frequency of the problem there exists a need to improve the efficiency of the computationally intensive METRIC-based solutions. We develop and validate a practical algorithm for improving the computational efficiency of a METRIC-based approach. The accuracy and effectiveness of the proposed algorithm are analyzed through a numerical study. The algorithm shows a 94% improvement in computational efficiency while maintaining 99.9% accuracy.     

A multidimensional tropical optimization problem with a non-linear objective function and linear constraints

We examine a multidimensional optimization problem in the tropical mathematics setting. The problem involves the minimization of a non-linear function defined on a finite-dimensional semimodule over an idempotent semifield subject to linear inequality constraints. We start with an overview of known tropical optimization problems with linear and non-linear objective functions. A short introduction to tropical algebra is provided to offer a formal framework for solving the problem under study. As a preliminary result, a solution to a linear inequality with an arbitrary matrix is presented. We describe an example optimization problem drawn from project scheduling and then offer a general representation of the problem. To solve the problem, we introduce an additional variable and reduce the problem to the solving of a linear inequality, in which the variable plays the role of a parameter. A necessary and sufficient condition for the inequality to hold is used to evaluate the parameter, whereas the solution to the inequality is considered a solution to the problem. Based on this approach, a complete direct solution in a compact vector form is derived for the optimization problem under fairly general conditions. Numerical and graphical examples for two-dimensional problems are given to illustrate the obtained results.     

Robust absolute single machine makespan scheduling-location problem on trees

We investigate a robust single machine scheduling-location problem with uncertainty in edge lengths. Jobs are located at the vertices of a given tree. Given a location for a single machine, the jobs travel to the location and are processed there sequentially. The goal is to find a location of the machine and simultaneously a sequence to minimize the makespan value in the worst-case. We use the concept of gamma-robustness to model uncertainty. Our main result is a polynomial time algorithm.     

Integrated Shipment Dispatching and Packing Problems: a Case Study

In this paper we examine a consolidation and dispatching problem motivated by a multinational chemical company which has to decide routinely the best way of delivering a set of orders to its customers over a multi-day planning horizon. Every day the decision to be made includes order consolidation, vehicle dispatching as well as load packing into the vehicles. We develop a heuristic based on a cutting plane framework, in which a simplified Integer Linear Program (ILP) is solved to optimality. Since the ILP solution may correspond to a infeasible loading plan, a feasibility check is performed through a tailored heuristic for a three-dimensional bin packing problem with side constraints. If this test fails, a cut able to remove the infeasible solution is generated and added to the simplified ILP. Then the procedure is iterated. Computational results show that our procedure allows achieving remarkable cost savings.      

A priori orienteering with time windows and stochastic wait times at customers

In the pharmaceutical industry, sales representatives visit doctors to inform them of their products and encourage them to become an active prescriber. On a daily basis, pharmaceutical sales representatives must decide which doctors to visit and the order to visit them. This situation motivates a problem we more generally refer to as a stochastic orienteering problem with time windows (SOPTW), in which a time window is associated with each customer and an uncertain wait time at a customer results from a queue of competing sales representatives. We develop a priori routes with the objective of maximizing expected sales. We operationalize the sales representative’s execution of the a priori route with relevant recourse actions and derive an analytical formula to compute the expected sales from an a priori tour. We tailor a variable neighborhood search heuristic to solve the problem. We demonstrate the value of modeling uncertainty by comparing the solutions to our model to solutions of a deterministic version using expected values of the associated random variables. We also compute an empirical upper bound on our solutions by solving deterministic instances corresponding to perfect information.     

A Study on Abnormality in Variational and Optimal Control Problems

In this paper, a variational problem is considered with differential equality constraints over a variable interval. It is stressed that the abnormality is a local character of the admissible set; consequently, a definition of regularity related to the constraints characterizing the admissible set is given. Then, for the local minimum necessary conditions, a compact form equivalent to the well-known Euler equation and transversality condition is given. By exploiting this result and the previous definition of regularity, it is proved that nonregularity is a necessary and sufficient condition for an admissible solution to be an abnormal extremal. Then, a necessary and sufficient condition is given for an abnormal extremal to be weakly abnormal. The analysis of the abnormality is completed by considering the particular case of affine constraints over a fixed interval: in this case, the abnormality turns out to have a global character, so that it is possible to define an abnormal problem or a normal problem. The last section is devoted to the study of an optimal control problem characterized by differential constraints corresponding to the dynamics of a controlled process. The above general results are particularized to this problem, yielding a necessary and sufficient condition for an admissible solution to be an abnormal extremal. From this, a previously known result is recovered concerning the linearized system controllability as a sufficient condition to exclude the abnormality.     

Method of successive projections for finding a common point of sets in metric spaces

Many problems in applied mathematics can be abstracted into finding a common point of a finite collection of sets. If all the sets are closed and convex in a Hilbert space, the method of successive projections (MOSP) has been shown to converge to a solution point, i.e., a point in the intersection of the sets. These assumptions are however not suitable for a broad class of problems. In this paper, we generalize the MOSP to collections of approximately compact sets in metric spaces. We first define a sequence of successive projections (SOSP) in such a context and then proceed to establish conditions for the convergence of a SOSP to a solution point. Finally, we demonstrate an application of the method to digital signal restoration.