基于Agent计算金融的计算机仿真研究综述

传统的金融学研究方法为"由上及下",此类方法倚重于总体把握,而忽视个体行为,特别是缺乏个体以及个体与环境之间的互动.基于Agent的计算金融是一种新的研究金融市场行为的工具.首先阐述了金融市场本身的复杂性和传统金融学存在的一些困惑.在这基础上提出了基于Agent的计算金融方法.其次,概要介绍了目前基于Agent的人工金融市场的主要设计方法,并且提出了在设计过程中要注意的一些问题.最后,分析了这种方法相对于传统金融学研究方法的优点和存在的问题,并进一步提出了该领域的未来研究方向.     

连续时间动态复制定理的推广与证明

推广了由Bertsimas,KoganandLo (1997) [1] 提出的非完全市场中的连续时间动态复制定理, 然后, 我们运用随机最优控制理论证明了这个定理.     

一类含有随机和模糊参数的规划模型

提出一类模糊机会约束的随机期望值规划模型,该模型同时含有随机和模糊参数.对改进的“报童问题”进行的分析,说明了模型的合理性.运用随机模拟与模糊模拟相结合的技术,给出了求解该规划模型的遗传算法.并对改进的“报童问题”进行了数值求解,同时给出了其它数值例子,进一步说明了所给出模型的适用性和有效性.     

Verification Theorem Of Stochastic Optimal Control With Mixed Delay And Applications To Finance

This paper focuses on a general model of a controlled stochastic differential equation with mixed delay in the state variable. Based on the Itô formula, stochastic analysis, convex analysis, and inequality technique, we obtain a semi‐coupled forward‐backward stochastic differential equation with mixed delay and mixed initial‐terminal conditions and prove that such forward‐backward system admits a unique adapted solution. The verification theorem for an optimal control of a system with mixed delay is established. The obtained results generalize and improve some recent results, and they are more easily verified and applied in practice. As an application, we conclude with finding explicitly the optimal consumption rate from the wealth process of a person given by a stochastic differential equation with mixed delay which fit into our general model.     

随机共振数值仿真的快速算法

根据信噪比曲线可以判断一个非线性系统是否发生随机共振,同时也可以得到非线性系统对噪声的输出性能。然而,由于要进行系综平均,即使要得到最简单的双稳态系统的信噪比曲线也需要很长时间的运算。经过对双稳态系统产生随机共振过程的仔细分析,以龙格—库塔法为基础,利用适当的数值计算方法,可以快速地求解双稳态系统的输出。     

Minorant Methods of Stochastic Global Optimization

Generalizations of the branch and bound method and of the Piyavskii method for solution of stochastic global optimization problems are considered. These methods employ the concept of a tangent minorant of an objective function as a source of global information about the function. Calculus of tangent minorants is developed.__________Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 56–70, March–April 2005.     

A Genetic Algorithm Approach to Solving Stochastic Job-shop Scheduling Problems

This paper proposes a method for solving stochastic job-shop scheduling problems based on a genetic algorithm. The genetic algorithm was expanded for stochastic programming. In this expansion, the fitness function is regarded as representing fluctuations that may occur under stochastic circumstances specified by the distribution functions of stochastic variables. In this study, the Roulette strategy is adopted for selecting the optimum solution in terms of the expected value. Within this algorithm, it is expected that the individual that appears most frequently must give the optimum solution. The effectiveness of this approach is confimed by applying it to stochastic job-shop scheduling problems. I compare the approximately optimum solutions found by this approach with the truly or approximately optimum solutions obtained by other conventional methods, and discuss the performance and effectiveness of this approach.     

多车种随机需求车辆路由问题的遗传算法

该文给出了多种类型车辆随机节点需求车辆路由问题(VRP)的遗传算法。将各个节点的需求作为一个随机数,同一条路径上车辆允许路由失败(routingfailure)的次数作为一个可控制的参数,对于节点需求服从给定的已知分布的VRP问题,给出了一些更简单实用的递推公式,并在此基础上通过巧妙地设计交配规则,得到遗传算法,并经实例运算对比,效果很好。     

Computing Equilibria in General Equilibrium Models via Interior-point Methods

In this paper we study new computational methods to find equilibria in generalequilibrium models. We first survey the algorithms to compute equilibria thatcan be found in the literature on computational economics and we indicate howthese algorithms can be improved from the computational point of view. We alsoprovide alternative algorithms that are able to compute the equilibria in anefficient manner even for large-scale models, based on interior-point methods.We illustrate the proposed methods with some examples taken from theliterature on general equilibrium models.     

随机规划ICC(β)模型的混合智能算法

讨论了当随机规划的随机变量服从离散分布时,合成机会约束模型(ICC(β))中约束集合的结构;利用适合全局搜索的遗传算法以及局部搜索能力很强的爬山算法,结合逐次增加约束方法,给出了计算该模型的混合智能算法;实例验证了该算法的有效性。该算法也可作为含连续随机变量的(ICC(β))的逼近算法。