一类专利权价值的动态实物期权定价方法

本文在同时考虑投资成本,投资时间以及现金流的不确定性的情况下,构建一类专利权价值的动态实物期权定价模型。考虑到投资中途可能失败的情况,在专利期权中特别加入放弃期权,并运用Monte Carlo模拟方法建立含放弃期权的专利期权的数值定价模型,然后将该模型用于一类IT企业的专利权投资案例的实证分析中。实证结果表明:基于不考虑投资成功与否的传统的实物期权定价模型的专利权定价评估比起含放弃期权的专利期权定价有存在低估专利期权价值的风险。     

基于实物期权的药物研发项目投资决策分析

本文结合药物开发投资项目的利润流不确定性和专利保护期限的不确定性,运用实物期权法,对药物开发项目进行分析,由或有债权分析方法推导出项目投资阈值,并做出相应典型数值分析.     

不确定性、实物期权与企业价值—基于简约Schwartz-Moon模型的分析

将实物期权理论引入传统现金流量折现法的应用框架,在不确定性条件下运用实物期权定价法来评估企业价值,提出了简约的Schwartz-Moon(2001)实物期权定价模型,并在此基础上运用蒙特卡罗模拟方法来计算了百度公司的价值.     

基于模糊集合论的实物期权定价方法

实物期权的定价在风险投资决策过程中具有重要意义.传统的实物期权定价方法忽略标的资产价值和投资成本的模糊性,从而可能导致错误的投资决策.本文主要研究了具有模糊标的的资产价值和投资成本情形时的实物期权定价模型.文中将这些模糊因素分别视为模糊数和模糊变量,然后运用模糊集合论,结合B-S期权定价理论,对实物期权进行定价,得到了基于模糊集合论的实物期权定价模型.     

技术创新与增长期权定价

本采用了净现值(NPV)和实物期权定价方法对一个实际的MMDS的发射放大器项目进行了定价，并对两种方法定价的结果进行了分析和对比。由于实物期权方法定价的结果包含项目中的增长期权和放弃期权的价值，因而实物期权方法定价的结果比NPV方法定价的结果更合理和更高。     

不确定性投资决策中的期权博弈分析

本文应用期权博弈理论方法分析了存在竞争条件下的不确定性投资决策问题.建立了一个对称双寡头模型,用实物期权方法计算了模型中的领先者、跟随者和同时投资者的价值函数和投资临界点.     

基于实物期权的基础设施项目融资中政府担保价值研究

通过政府担保吸引国内外财团、公司、企业以及个人等非政府投资主体投资基础设施项目是解决我国基础设施建设财政投入不足的有效途径。本文在国内外基础设施项目融资理论基础上，运用实物期权方法引入多变量模型对政府担保进行定价研究，通过实例分析了实物期权模型的有效性，最后给出了政府投资主体在吸引非政府投资主体时政府担保行为的影响因素和对应策略。     

房地产开发的最优时间和最优强度

房地产投资具有许多不确定性,对房地产投资进行评估尤为重要.利用实物期权理论,对房地产投资进行建模分析,确定出最优开发时间和最优开发强度;最后根据模型推导出来的结论进行数值分析.     

基本资产不可交易的实物期权定价方法研究

实物期权定价面临的一个主要问题是其基本资产不可交易问题，在这种情况下，通常的解决办法是在市场中寻找一个与该基本资产最为相关的可交易资产，利用可交易资产的价格信息来对特定实物期权进行定价和风险对冲。本应用随机动态规划法，确定实物期权的最优风险对冲策略所满足的偏微分方程。利用无套利原理，同时还可以得到实物期权的近似市场定价。     

调节作用下投资策略的评价

本文用实物期权的方法评价调节作用下的投资策略 ,建立期权定价模型。模型中项目价值遵循均值返回过程 ,项目价值的路径采用数值模拟分析 ,文中运用动态规划方法推出期权定价公式 ,通过数值解法求解和分析数值结果 ,得出调节作用对投资的影响。     

Modeling, inference and optimization of regulatory networks based on time series data

In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by various kinds of optimization techniques, ranging from linear, mixed-integer, spline, semi-infinite and robust optimization to conic, e.g., semi-definite programming. We present different kinds of uncertainties and a new time-discretization technique, address aspects of data preprocessing and of stability, related aspects from game theory and financial mathematics, we work out structural frontiers and discuss chances for future research and OR application in our real world.     

Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties

This paper considers the relationship of the major uncertainties of a project by using proposed approach. This approach by using rotary algorithm intellectualized the classic Monte Carlo simulation. This will help utility function to come closer to reality so that decision making and risk analysis would be done based on the real and possible modes, providing better conditions for decision making. Analyzing and investigating uncertainties are done in the risk management frame work. Because opportunities and threats are not separated, Monte Carlo simulation analysis is implemented as an integrated tool to reach the project goals, analyzing and investigating a variety of uncertainty permutations simultaneously. This method is a powerful tool for investigating the effects of all uncertainties’ occurrence, so it has noticeable benefits such as simultaneous consideration of uncertainties and the capability of representing several dimensions of utility function. In spite of these benefits, not considering the type and level of relationships, some permutations of uncertainties will occur that are not possible in real world. This would divert the utility function from reality. A simple example is used to illustrate the application of the model in practice.     

Supplier quality improvement: The value of information under uncertainty

We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability.     

海外区域技术平台投资的时机选择模型及实证研究

基于后发企业海外区域技术平台(RTP)投资中“成熟技术产品推广”和“新技术产品开发”两个阶段,本文构建了两阶段实物期权模型。进一步,利用中国制造业对外直接投资(OFDI)的上市公司样本,考察了RTP投资时机选择的决定因素及影响效果。研究发现,第一阶段外生不确定性越低、第二阶段内生不确定性越高、新技术产品项目的增长期权越大,企业越倾向于尽早地RTP投资于海外市场;在两阶段内外生不确定性的不同条件下,RTP投资时机相应地对企业创新增长产生了显著的差异影响。     

Stochastic and minimum regret formulations for transmission network expansion planning under uncertainties

After deregulation of the Power sector, uncertainty has increased considerably. Vertically integrated utilities were unbundled into independent generation, transmission and distribution companies. Transmission network expansion planning (TNEP) is now performed independent from generation planning. In this environment TNEP must include uncertainties of the generation sector as well as its own. Uncertainty in generation costs affecting optimal dispatch and uncertainty in demand loads are captured through composite scenarios. Probabilities are assigned to different scenarios. The effects of these uncertainties are transferred to the objective function in terms of total costs, which include: generation (dispatch), transmission expansion and load curtailment costs. Two formulations are presented: stochastic and minimum regret. The stochastic formulation seeks a design with minimum expected cost. The minimum regret formulation seeks a design with robust performance in terms of variance of the operational costs. Results for a test problem and a potential application to a real system are presented.     

Socially responsible merchant operations: Comparison of shutdown-averse CVaR and anticipated regret policies

Commodity and energy production assets are managed as real options on market uncertainties. Social impacts of plant shutdowns incentivize balancing asset value with shutdown probability. We propose new shutdown-averse policies based on the popular dynamic conditional value-at-risk (CVaR). We analytically and numerically compare these policies to known shutdown-averse policies based on anticipated regret (AR). Our findings support the use of AR over CVaR to embed shutdown-aversion and the consideration of hybrid policies that are asymptotically time-consistent but easily interpretable.     

Evaluating pharmaceutical R&D under technical and economic uncertainty

This study sets up a compound option approach for evaluating pharmaceutical R&D investment projects in the presence of technical and economic uncertainties. Technical uncertainty is modeled as a Poisson jump that allows for failure and thus abandonment of the drug development. Economic uncertainty is modeled as a standard diffusion process which incorporates both up-and downward shocks. Practical application of this method is emphasized through a case analysis. We show that both uncertainties have a positive impact on the R&D option value. Moreover, from the sensitivity analysis, we find that the sensitivity of the option with respect to economic uncertainty and market introduction cost decreases when technical uncertainty increases.     

多层气藏中气体流动问题的新模型及其应用

本文针对气井产量与井筒集是变数时,建立了多层气藏内真实气体渗流问题的新模型,求出了三种典型外边界条件下各储层压力分布精确解,作为特例,又得到了均质气藏内压力分布的精确解并给出了在气田开发中的应用.     

An approach to the valuation and decision of ERP investment projects based on real options

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach.