Minimax hedging strategy

We present several variants of a robust risk management strategy based on minimax for the writer of a European call option on a stock and show that it performs at least as well as the standard hedging strategy, delta hedging. When using the minimax strategy, the hedger specifies a worst case scenario in terms of the price of the underlying stock. The minimax strategy recommends the number of shares in the underlying stock the hedger should hold in order to minimize the hedging error against the worst case occurring. The minimax hedging error may correspond to an extreme point of the price range being considered or to a mid-range solution. Simulation and empirical results suggest that the minimax strategy is particularly powerful for hedging the risk of writing an option when the price of the underlying stock is both highly volatile and crosses over the exercise frequently.     

CREWS-SAVRE: Scenarios for Acquiring and Validating Requirements

This paper reports research into semi-automatic generation of scenarios for validating software-intensive system requirements. The research was undertaken as part of the ESPRIT IV 21903 CREWS long-term research project. The paper presents the underlying theoretical models of domain knowledge, computational mechanisms and user-driven dialogues needed for scenario generation. It describes how CREWS draws on theoretical results from the ESPRIT III 6353 NATURE basic research action, that is object system models which are abstractions of the fundamental features of different categories of problem domain. CREWS uses these models to generate normal course scenarios, then draws on theoretical and empirical research from cognitive science, human-computer interaction, collaborative systems and software engineering to generate alternative courses for these scenarios. The paper describes a computational mechanism for deriving use cases from object system models, simple rules to link actions in a use case, taxonomies of classes of exceptions which give rise to alternative courses in scenarios, and a computational mechanism for generation of multiple scenarios from a use case specification.     

自动驾驶安全关键场景生成技术综述

安全关键场景生成是自动驾驶的重要方向,在自动驾驶测试、汽车安全性评估和汽车安全标准构建等领域都有着很高的应用价值,是关系自动驾驶应用落地的关键。现有研究缺乏重点围绕安全关键场景生成技术的综述,因此本文对安全关键场景生成技术进行了系统性综述。首先,分析了安全关键场景生成技术的综述相关研究;其次,对安全关键场景生成模型进行了对比分析;再次,分类总结了基于聚类、贝叶斯网络和对抗网络的安全关键场景生成方法的进展;最后,对安全关键场景生成方法研究趋势进行了展望。     

Local Search Techniques for Constrained Portfolio Selection Problems

We consider the problem of selecting a portfolio of assets that provides theinvestor a suitable balance of expected return and risk. With respect to theseminal mean-variance model of Markowitz, we consider additionalconstraints on the cardinality of the portfolio and on the quantity ofindividual shares. Such constraints better capture the real-world tradingsystem, but make the problem more difficult to be solved with exact methods.We explore the use of local search techniques, mainly tabu search, for theportfolio selection problem. We compare the combine previous work on portfolioselection that makes use of the local search approach and we propose newalgorithms that combine different neighborhood relations. In addition, we showhow the use of randomization and of a simple form of adaptiveness simplifiesthe setting of a large number of critical parameters. Finally, we show how ourtechniques perform on public benchmarks.     

Robust multiperiod portfolio management in the presence of transaction costs

We study the viability of different robust optimization approaches to multiperiod portfolio selection. Robust optimization models treat future asset returns as uncertain coefficients in an optimization problem, and map the level of risk aversion of the investor to the level of tolerance of the total error in asset return forecasts. We suggest robust optimization formulations of the multiperiod portfolio optimization problem that are linear and computationally efficient. The linearity of the optimization problems is an advantage when complex additional requirements need to be imposed on the portfolio structure, e.g., limitations on positions in certain assets or tax constraints. We compare the performance of our robust formulations to the performance of the traditional single period mean-variance formulation frequently employed in the financial industry.     

基于Web服务的可视化作战想定生成系统设计与实现

设计一个基于Web服务的可视化作战想定生成系统,该系统实现了与具体仿真系统的松散耦合,能够跨越C4ISR系统、作战仿真应用、想定生成系统等仿真系统底层数据结构的异构性,快速重用分布仿真系统的想定资源,提高想定开发效率,并使仿真系统之间的互操作更加灵活。     

工作流生成中的一种流程模式知识优化方法

基于模式的工作流生成利用规划技术和业务知识实现了工作流自动创建。流程模式知识的优劣直接影响到工作流生成结果的正确性。提出了一个针对流程模式的知识优化方法。在人工建模的基础上,针对流程模式知识构建场景分类器。利用流程规划运行的历史数据,通过机器学习对流程模式进行调整与优化,保证了工作流创建的正确性。     

Digital Portfolio Theory

The Modern Portfolio Theory of Markowitz maximized portfolio expected return subject to holding total portfolio variance below a selected level. Digital Portfolio Theory is an extension of Modern Portfolio Theory, with the added dimension of memory. Digital Portfolio Theory decomposes the portfolio variance into independent components using the signal processing decomposition of variance. The risk or variance of each security's return process is represented by multiple periodic components. These periodic variance components are further decomposed into systematic and unsystematic parts relative to a reference index. The Digital Portfolio Theory model maximizes portfolio expected return subject to a set of linear constraints that control systematic, unsystematic, calendar and non-calendar variance. The paper formulates a single period, digital signal processing, portfolio selection model using cross-covariance constraints to describe covariance and autocorrelation characteristics. Expected calendar effects can be optimally arbitraged by controlling the memory or autocorrelation characteristics of the efficient portfolios. The Digital Portfolio Theory optimization model is compared to the Modern Portfolio Theory model and is used to find efficient portfolios with zero calendar risk for selected periods.     

基于微粒群算法的单阶段投资组合优化

投资组合优化问题是NP难解问题,通常方法很难达到全局最优,文章对微粒群算法在投资组合优化中的具体应用进行了研究,在具体应用过程中为了提高算法的收敛性和稳定性对算法进行了改进,通过多次具体的真实历史数据的验证和试验结果分析。结果表明在解决单阶段投资组合优化问题中,微粒群算法是一种高效的、可靠的优化算法,具有一定的实用价值。     

Explanation-Based Scenario Generation for Reactive System Models

Reactive systems control many useful and complex real-world devices. Tool-supported specification modeling helps software engineers design such systems correctly. One such tool, a scenario generator, constructs an input event sequence for the spec model that reaches a state satisfying given criteria. It can uncover counterexamples to desired safety properties, explain feature interactions in concrete terms to requirements analysts, and even provide online help to end users learning how to use a system. However, while exhaustive search algorithms such as model checkers work in limited cases, the problem is highly intractable for the functionally rich models that correspond naturally to complex systems engineers wish to design. This paper describes a novel heuristic approach to the problem that is applicable to a large class of infinite state reactive systems. The key idea is to piece together scenarios that achieve subgoals into a single scenario achieving the conjunction of the subgoals. The scenarios are mined from a library captured independently during requirements acquisition. Explanation-based generalization then abstracts them so they may be coinstantiated and interleaved. The approach is implemented, and I present the results of applying the tool to 63 scenario generation problems arising from a case study of telephony feature validation.     

基于UML的生成场景测试用例研究

使用UML生成场景测试用例,有利于测试者设计测试用例。使用UML的类图、状态图和顺序图来说明DHCP实例的场景测试用例的生成方法,讨论基于UML顺序图生成场景测试用例的方法,从找出场景到生成测试用例,分析与每一个场景相关的环境条件并将它与方法序列、输入、输出合理组合作为覆盖该场景的测试用例。结果表明,该方法生成的测试用例数量少,减少了测试工作量和测试用例的重复生成。     

On the use of optimization models for portfolio selection: A review and some computational results

Portfolio theory deals with the question of how to allocate resources among several competing alternatives (stocks, bonds), many of which have an unknown outcome. In this paper we provide an overview of different portfolio models with emphasis on the corresponding optimization problems. For the classical Markowitz mean-variance model we present computational results, applying a dual algorithm for constrained optimization.     

求解均值-CVaR投资组合模型的改进粒子群算法

针对粒子群算法易跳过全局极值,且只能求解连续性问题的缺点,提出离散复形法局部搜索的思想,来有效提高粒子群算法在离散型问题中的搜索性能。针对粒子群算法易陷入局部极小的缺点,引入自适应粒子迁徙操作保证粒子的多样性,有效避免陷入局部收敛。对采用CVaR度量风险、构建有交易费用和限制证券比例的均值-CVaR投资组合模型进行仿真实验,实验结果验证了算法的有效性。将改进的粒子群算法应用到求解均值-CVaR模型的投资组合问题,与其他算法相比,该方法精度更高、性能更稳定。     

基于CVaR的供电商多市场购电组合研究

电力市场环境下,供电商在日前现货、远期合同、期权、可中断负荷等多个市场购电时面临着收益与风险的权衡。引入条件风险价值(CVaR)作为市场风险的度量因子,建立了以最小化损失为目标的供电商多市场购电决策模型,分析了期权和可中断负荷对购电组合的影响。算例结果表明期权和可中断负荷能有效地降低供电商的购电损失,期权价格和可中断负荷补偿价格对购电组合策略有显著的影响。     

用非线性存储方案设计SIMD计算机的一般方法

非线性存储方案能在处理单元数等于存储体数的情况下,使ＳＩＭＤ机实现多种访存模式无冲突,提高其整体性能,文中提出一种用线性存储方案设计ＳＩＭＤ 一般方法,在存储方案给定的前提下,针对有限的模板集设计出同时满足存储器访问无冲突和互联网的并行结构,首先,用布尔向量空间表示模板,并指出模板与ＬＣ置换的对应关系,在此基础上,提出设计局部地址生成逻辑和增强的间接二进制Ｎ方体网络的方法,由于板集中任意的访存方式