金融计划简易概率网络模型

本文给出一种简易概率网络模型．某些多阶段金融计划问题可纳入该模型．金融计划的最优值是用其数学期望估算的，而计算其数学期望是相当困难的．本文给出一种临界流方法，并给出临界流存在的必要充分条件．     

一类随机多目标二次线性规划模型的交互式算法

针对线性约束条件下带有一个二次目标函数和多个线性目标函数的随机多目标决策问题，借助参考方向法和权重法对该决策问题的期望值模型进行标量化，获得了关于期望值模型的（恰当／弱）有效解的充要条件，引入Ａｃｈｉｅｖｅｍｅｎｔ函数建立了一类随机多目标二次线性规划模型的交互式计算方法．     

经典风险模型分红控制过程的最优停止问题

本文在带注资的经典风险模型的最优分红控制过程的基础上,进一步引入最优停止策略.目标是要找到最优的停止时刻,使得到该时刻为止,股东的折现分红与带有一定费用的折现注资二者之差的期望值最大化.通过建立值函数V(x)满足的HJB方程,我们找到了最优停止时刻τ~*.特别的,当索赔服从指数分布时,通过计算最终得到了值函数V(x)和最优停止时刻.τ~*的清晰表达式.     

带时变生产成本的易变质经济批量模型的最优策略分析

考虑了具有时变生产成本的易变质产品经济批量模型．有限计划期内,单位生产成本、生产率以及需求率假定为时间的连续函数,生产固定成本则具有遗忘效应现象．当不允许缺货时,建立了以总成本最小为目标的混合整数优化模型并证明了此问题最优解的相关性质．对于此问题的特殊情形,将成本函数中的离散型变量松弛为连续型变量,通过分析其最优解的存在性及唯一性,求解了此最优解,将其作为初始值设计了求取一般情形最优解的有效算法．最后通过算例验证了理论结果的有效性．     

煤车装卸系统的优化操作

本文对一煤炭装卸系统建立优化模型,目标函数是每年总费用的期望值。我们将问题归结为寻求一定的操作规则,以使总费用期望值近似达到最优。此外,我们为了达到最少总费用安排了一个日调度表,最后,讨论了模型的优缺点。     

对数效用分红支付问题

本文研究具有对数效用函数的风险灵敏保险公司的最优分红问题.首先建立分红支付问题的离散时间Markov决策过程模型(简称DTMDP),优化目标是最大化公司破产前分红现值的对数的期望值.在较弱的假设下,本文证明值函数满足最优方程.然后得到这个最优方程最大的最大点的若干性质.最后证明最大的最大点在每个时刻的映射值全体构成一个最优分红策略.     

基于模糊系数的投资组合优化模型及其实证分析

采用模糊数处理不确定性信息．以模糊期望收益率最大为目标函数,使总的风险不高于给定的模糊数,建立了一种新的模型．在给定的截集下,期望收益率转化为区间数,目标函数转化为对该区间数的下限求最大值．基于模糊数大小的概率比较,从而将模糊优化模型转化为不等式约束下的线性规划模型．利用Matlab编程可解得其最优解．最后通过实例分析...     

有风险控制的最优投资组合(英)

本文讨论有风险控制的最优投资组合问题并研究了倍率风险函数及临界风险的性质,最大最小风险的估计;给出了其倍率风险函数有严格解析形式的例子．     

基于产品合格率随机分布的投产量模型优化

当产品合格率随机分布时,建立起以投产综合损失费(欠产再投产费,过量产出费和不合格处理费)期望值为目标函数,投产量为决策变量的投产量模型。结合产品合格率随机分布不同(均匀分布和正态分布)推导求解了模型的最优解,得出了产量与投产量的等式关系,据此可有效地指导生产。最后通过数值实例分析验证了模型的有效性。     

CIMS缓冲区分配工件的阈值控制策略及其优化

本文对两个并行不同的工作站，在有限的缓冲区中，提出了一类分配工件的阈值控制策略．首先利用结构矩阵分析理论，在这类阈值控制策略下给出了这个CIMS可靠性模型的随机结构，得到了系统的稳态生产率和稳态可用度，推导了这类阈位控制策略的两类持续时间的分布，然后在保证系统稳态生产率最大的目标下，得到了这类阈值控制策略的PW－最优解和PT－最优解．     

Chance and necessity in sociological theory

This paper proposes a mathematical model of financial markets as networks. The model examines the effect of network structure on market behavior (price volatility and trading volume). In the model, investors are arrayed in various network configurations through which they gather information to make trading decisions. The basic network considered is a chain graph with two parameters, number of investors (n) and the length of time in which information is transmitted (k). Closed‐form expressions for price volatility and expected trading volume are provided. The model is generalized to more complex networks, focusing on the hub‐and‐spoke network. The network configurations analyzed do not represent the real (and unknown) communication network among investors, but predictions from the model are consistent with price and volume patterns observed in sociological and economic research on financial markets. The main result is that network structure alone influences price volatility and expected trading volume even though investors are homogeneous and the information introduced into the system is unbiased and random. This result suggests that the structure of the real communication network among investors may influence market behavior.     

Bayesian Fused Lasso Regression for Dynamic Binary Networks

We propose a multinomial logistic regression model for link prediction in a time series of directed binary networks. To account for the dynamic nature of the data, we employ a dynamic model for the model parameters that is strongly connected with the fused lasso penalty. In addition to promoting sparseness, this prior allows us to explore the presence of change points in the structure of the network. We introduce fast computational algorithms for estimation and prediction using both optimization and Bayesian approaches. The performance of the model is illustrated using simulated data and data from a financial trading network in the NYMEX natural gas futures market. Supplementary material containing the trading network dataset and code to implement the algorithms is available online.     

Bankruptcy prevention in multiperiod Markowitz optimization problem

A multiperiod version of the Markowitz optimization problem is considered by assuming that the financial market model is discrete with respect to time and the number of scenarios. Basing on the optimal strategy of securities trading, we find the upper bound of the expected final portfolio value which does not lead to bankruptcy.     

Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels

A great deal of research has been done on production planning and sourcing problems, most of which concern deterministic or stochastic demand and cost situations and single period systems. In this paper, we consider a new class of multi-period production planning and sourcing problem with credibility service levels, in which a manufacturer has a number of plants and subcontractors and has to meet the product demand according to the credibility service levels set by its customers. In the proposed problem, demands and costs are uncertain and assumed to be fuzzy variables with known possibility distributions. The objective of the problem is to minimize the total expected cost, including the expected value of the sum of the inventory holding and production cost in the planning horizon. Because the proposed problem is too complex to apply conventional optimization algorithms, we suggest an approximation approach (AA) to evaluate the objective function. After that, two algorithms are designed to solve the proposed production planning problem. The first is a PSO algorithm combining the AA, and the second is a hybrid PSO algorithm integrating the AA, neural network (NN) and PSO. Finally, one numerical example is provided to compare the effectiveness of the proposed two algorithms.     

Optimal Security Liquidation Algorithms

This paper develops trading strategies for liquidation of a financial security, which maximize the expected return. The problem is formulated as a stochastic programming problem that utilizes the scenario representation of possible returns. Two cases are considered, a case with no constraint on risk and a case when the risk of losses associated with trading strategy is constrained by Conditional Value-at-Risk (CVaR) measure. In the first case, two algorithms are proposed; one is based on linear programming techniques, and the other uses dynamic programming to solve the formulated stochastic program. The third proposed algorithm is obtained by adding the risk constraints to the linear program. The algorithms provide path-dependent strategies, i.e., the fraction of security sold depends upon price sample-path of the security up to the current moment. The performance of the considered approaches is tested using a set of historical sample-paths of prices.     

Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand

This paper proposes a short-term liner ship fleet planning problem by taking into account container transshipment and uncertain container shipment demand. Given a liner shipping service network comprising a number of ship routes, the problem is to determine the numbers and types of ships required in the fleet and assign each of these ships to a particular ship route to maximize the expected value of the total profit over a short-term planning horizon. These decisions have to be made prior to knowing the exact container shipment demand, which is affected by some unpredictable and uncontrollable factors. This paper thus formulates this realistic short-term planning problem as a two-stage stochastic integer programming model. A solution algorithm, integrating the sample average approximation with a dual decomposition and Lagrangian relaxation approach, is then proposed. Finally, a numerical example is used to evaluate the performance of the proposed model and solution algorithm.     

Strategic Long-Term Financial Risks: Single Risk Factors

The question of the measurement of strategic long-term financial risks is of considerable importance. Existing modelling instruments allow for a good measurement of market risks of trading books over relatively small time intervals. However, these approaches may have severe deficiencies if they are routinely applied to longer time periods. In this paper we give an overview on methodologies that can be used to model the evolution of risk factors over a one-year horizon. Different models are tested on financial time series data by performing backtesting on their expected shortfall predictions.     

夜盘交易对我国农产品期货市场影响的实证研究

随着我国农产品期货与国际市场的联动性进一步加强,为防止相关期货产品的隔夜风险和价格跳水问题,对部分农产品期货实行夜盘交易制度。为测度夜盘交易制度是否有益于农产品期货市场朝着稳定、理性的方向发展,本文采用了适合刻画金融序列波动性的GARCH族模型,实证检验得出GARCH、GARCH-M和EGARCH模型能够高度拟合农产品期货的价格序列并显著衡量夜盘交易对于我国农产品期货市场的影响。研究结论如下:第一、基于GRACH模型实证结果,夜盘交易制度变量的回归结果显著,该制度能减轻农产品期货的价格波动,且其影响是显著的;第二、EGARCH模型的回归结果同样显著,分别对比不同样本期的EGARCH模型实证结果可以得到,夜盘交易的开放减少了农产品期货市场的非对称性,使得市场趋向于理性的方向发展。     

Multiobjective expected value model for portfolio selection in fuzzy environment

In this paper, we propose a credibilistic framework for portfolio selection problem using an expected value multiobjective model with fuzzy parameters. We consider short term return, long term return, risk and liquidity as key financial criteria. A solution procedure comprising fuzzy goal programming and fuzzy simulation based real-coded genetic algorithm is developed to solve the model. The proposed solution approach is considered advantageous particularly for the cases where the fuzzy parameters of the problem may assume any general functional form. An empirical study is included to illustrate the usefulness of the proposed model and solution approach in real-world applications of portfolio selection.