A Linear Stochastic Programming Model for Optimal Leveraged Portfolio Selection

The literature of portfolio optimization is extensive and covers several important aspects of the asset allocation problem. However, previous works consider simplified linear borrowing cost functions that leads to suboptimal allocations. This paper aims at efficiently solving the leveraged portfolio selection problem with a thorough borrowing cost representation comprising a number lenders with different rates and credit limits. We propose a two-stage stochastic programming model for asset and debt allocation considering a CVaR-based risk constraint and a convex piecewise-linear borrowing cost function. We compare our model to its counterpart with the fixed borrowing rate approximation used in literature. Numerical results show our model significantly improves performance in terms of risk-return trade-off.     

A Dynamic Stochastic Model of Asset Pricing with Heterogeneous Beliefs

This paper presents a new stochastic asset pricing model in a context of bounded rationality, where beliefs about future prices are formed via an expectations updating rule characterized by a stochastic multiplicative random variable, working as an agent-based time dependent weight of the conditional expectation of the fundamental. The agent’s belief about future prices depends on his confidence in the forecasts made by other agents, measured by the distribution type of agents and by a confidence parameter. The resulting stochastic dynamical system is firstly analyzed in a deterministic setting, deriving conditions for uniqueness and stability of steady states and proving that, for high values of the confidence parameter, no complicated dynamics can be exhibited, hence the new component has a stabilizing effect on the qualitative dynamics. Differently, for small values of the confidence parameter, we prove the existence of a stability region in the parameters plane where the only possible dynamics is convergence to a steady state, while complexity is exhibited outside such region. Starting from the results obtained in the deterministic case, the model is then explored by reintroducing randomness. More specifically, we analyze the stability region in three directions: first of all, a robust estimate of the stability region’s measure is provided; second, a long run equilibrium relation between the parameters of the system is obtained; third, the persistence properties of the series describing the bifurcation curves is performed. We finally underline some economic implications.     

A Stochastic Optimization Model to Reduce Expected Post-Disaster Response Time Through Pre-Disaster Investment Decisions

This paper seeks to enhance network survivability under a disaster and reduce the expected post-disaster response time for transportation networks through pre-disaster investment decisions. The planning focuses on determining the links of the network to strengthen through investment under two types of uncertainties: the disaster characteristics, and the surviving network under each disaster. A bi-level stochastic optimization model is proposed for this problem, in which link investment decisions are made at the upper level to enhance the network survivability subject to a budget constraint such that the expected post-disaster response time is minimized at the lower level. A two-stage heuristic algorithm is proposed to obtain effective solutions efficiently. The numerical experiments indicate that the proposed heuristic algorithm converges to a fixed point representing a feasible solution, within an acceptable tolerance level, of the bi-level stochastic optimization model which is an effective solution under disasters of moderate severity. Parametric and sensitivity analyses reinforce the need for a holistic approach that integrates multiple relevant considerations to determine the link investment decisions.     

A Dynamic and Flexible Berth Allocation Model with Stochastic Vessel Arrival Times

Networks and Spatial Economics - This study proposes a berth-flow network modeling approach to deal with the dynamic berth allocation problem (DBAP) with stochastic vessel arrival times. In this...     

Performance of a Hedged Stochastic Portfolio Model in the Presence of Extreme Events

Classical methods for computing the value-at-risk(VaR) do not account for the large price variationsobserved in financial markets. The historical methodis subject to event risk and may miss some fundamentalmarket evolution relevant to VaR; thevariance/covariance method tends to underestimate thedistribution tails and Monte Carlo simulation issubject to model risk. These methods are therebyusually completed with analyses derived fromcatastrophe scenarios.We propose a special case of the extreme-valueapproach for computing the value-at-risk of a stochasticmulticurrency portfolio when alternative hedgingstrategies are considered. This approach is able tocover market conditions ranging from the usual VaRenvironment to financial crises.We implement a multistage portfolio model with anexchange rate dynamic with stochastic volatility. Theparameters are estimated by GARCH-t models. Thesimulations are used to select multicurrencyportfolios whose exchange rate risk is hedged andrebalanced each ten days, accounting for VaR. Wecompare the performances of the two most classicalinstitutional options strategies – protective puts andcovered calls – to that of holding an unhedgedportfolio in presence of extreme events.     

基于随机Petri网的嵌入式软件能耗模型

传统的软件能耗测量方式是在软件设计完成后进行实际测量从而得到能耗值,不能在软件的设计初期形成一定的数据量为开发做出参考。为此,从体系结构层面对软件进行构件化处理,基于随机Petri网分别对构件、接口、连接件各模块进行建模与分析,构造出嵌入式软件能耗模型ESPN。给出寻找3种能耗路径的方法,根据能耗状态可达图对软件能耗进行预测并求得其确切值。通过实例化分析与设计验证了该模型的有效性。     

A Multiobjective Optimization Model for Dynamic Reconfiguration of Ring Topologies with Stochastic Load

In this paper we describe a heuristic procedure to generate solutions to a multiobjective stochastic, optimization problem for a dynamic telecommunications network. Generating Pareto optimal solutions can be difficult since the optimization problem is computationally challenging and moreover the network must be reconfigured in near real time, for example, to recover connectivity after a severe weather event. There are two main contributions of this paper. First, we show mathematically how a certain deterministic equivalent optimization problem can be solved instead of the stochastic one, thus facilitating computations. Second, we test our heuristic under a wide set of simulated conditions (e.g., atmospheric obscuration due to differing levels of cloud cover, different demand patterns) and show that it achieves near Pareto optimality in a short amount of time.     

动态电源管理的随机切换模型与策略优化

提出一种基于连续时间Markov决策过程的动态电源管理策略优化方法.通过建立动态电源管理系统的随机切换模型,将动态电源管理问题转化为带约束的策略优化问题,并给出一种基于矢量合成的策略梯度优化算法.随机切换模型对动态电源管理系统的描述精确,策略优化算法简便有效,既能离线计算,也适用于在线优化.仿真实验验证了该方法的有效性.     

动态电源管理的随机切换模型与在线优化

考虑系统参数未知情况下的动态电源管理问题,提出一种基于强化学习的在线策略优化算法. 通过建立事件驱动的随机切换分析模型,将动态电源管理问题转化为带约束的Markov 决策过程的策略优化问题. 利用此模型的动态结构特性,结合在线学习估计梯度与随机逼近改进策略,提出动态电源管理策略的在线优化算法．随机切换模型对电源管理系统的动态特性描述精确,在线优化算法自适应性强,运算量小,精度高,具有较高的实际应用价值．     

证券投资组合模型及其应用

证券投资组合问题广泛存在于金融、风险投资等多个领域。文中分别在全部投资和选择性投资两种场合下重点研究了证券投资组合方案选择问题。对于全部投资而言,文中建立起了多目标规划模型并在求解过程中采用理想点法将其转化为线性规划模型,利用LINGO软件求解出最佳投资比例。对于后者,文中通过引入一个服从0-1分布的参数,将其化为一般情况求解。通过一个实例以说明上述方法的应用,并对模型进行了改进与推广。     

平稳动态系统DBN结构学习模型设计

针对动态数据挖掘问题,提出了一种平稳系统连续变量动态贝叶斯网络(Dynamic Bayesian Networks,DBN)结构学习模型,用于智能体的自主优化;首先给出了平稳系统连续变量结构学习的基本思路及假设条件,讨论了平稳系统连续变量DBN结构学习的模型设计问题;其次,在诸多随机过程系统文献的基础上.提出了系统的BIC评分函数,在有限时间T内的情况给出三个定义,并设计了学习的基本框架;最后,设计了平稳系统连续变量的实验模型并进行了仿真,结果表明,该模型能正确地学习出所设计的DBN结构.     

Hierarchical Decomposition of Production and Capacity Investment Decisions in Stochastic Manufacturing Systems

This paper is concerned with hierarchical decisions regarding production and investment in capacity in manufacturing systems with production subject to breakdown and repair. The production capacity can be increased by investing continuously in new capacity which is available upon completion. The decision variables are the rates of production and investment in capacity. The investment rate is assumed to have an upper bound. If, as assumed, the rates of breakdown and repair of production equipment are much larger than the rate of discounting of costs, the given problem can be approximated by a simpler problem in which the stochastic production capacity is replaced by the average capacity. Asymptotically optimal controls for the given problem are constructed from nearly optimal controls of the limiting problem. In addition, we analyse the behaviour of the solution as the investment rate is allowed to become arbitrarily large.     

Dynamic Network Model of Managing Investment Portfolio under Random Stepwise Changes in Volatilities of Financial Assets

The problem of managing a portfolio of risk (ordinary shares) and no-risk (bank account, reliable bonds) investments was considered. The portfolio model was described in the state space by a system of stochastic differential (or difference) equations with random stepwise parameters. Management of the investment portfolio was formulated as a dynamic problem of tracking some reference portfolio with an investor-defined yield. An approach to determining the optimal management strategy with quadratic criterion-based feedback was proposed. Results of numerical modeling were presented.     

A Policy Improvement Method in Constrained Stochastic Dynamic Programming

This note presents a formal method of improving a given base-policy such that the performance of the resulting policy is no worse than that of the base-policy at all states in constrained stochastic dynamic programming. We consider finite horizon and discounted infinite horizon cases. The improvement method induces a policy iteration-type algorithm that converges to a local optimal policy.     

一类国际证券投资组合和消费选择的最优控制问题

首先运用经典动态规划方法,研究股票付息下国际证券市场中一类最优证券投资组合和消费选择问题,并利用投资学理论对投资者只投资两种证券情形的最优组合给出经济分析和解释.然后,运用非常简单和直接的方法对两种典型的效用函数给出最优解的显式形式,求解的技巧来自解决线性二次最优控制问题的配平方法.最后,给出一些数值计算例子来展示各模型参数对最优选择的影响.     

A Broad, Quantitative Model for Making Early Requirements Decisions

Although detailed information is typically scarce during a project's early phases, developers frequently need to make key decisions about trade-offs among quality requirements. Developers in many fields-including systems, hardware, and software engineering-routinely make such decisions on the basis of a shallow of the situation or on past experience, which might be irrelevant to the current a consequence, developers can get locked into what is ultimately an inferior design or pay a significant price to reverse such earlier decisions later in the process. By coarsely quantifying relevant factors, a risk-assessment model helps hardware and software engineers make trade-offs among quality requirements early in development.     

双随机软件可靠性模型的建模

本文在仔细分析了软件故障的物理过程及模型机理后，认为：由于软件故障过程的随机因素太复杂以致于用某一个传统随机过程不足以为软件故障过程建模．传统上采用的马尔可夫非齐次泊松过程模型或二项型式模型只能部分地表述软件的故障行为．若要全面地去表述软件的故障行为，就需要一个补偿随机过程．也就是说本文采用了两个随机函数来为软件故障行为建模．