委托代理框架下实物期权最优投资策略研究

在非对称信息条件下，讨论实物期权定价及其投资优化问题。根据委托代理理论，提出实物期权投资者和经营者的价值模型，在实物期权经营者对于项目价值信息隐匿条件下，应用极大值原理推导出实物期权最优投资和转移价值的解，指出投资与项目价值、转移价值与项目价值间的关系，分析实物期权价值模型各参数对投资决策的影响作用。     

不确定环境下项目评估的期权决策树研究

在不确定环境下，传统的净现值(NPV)项目评估方法由于忽略决策的灵活性而低估了项目的价值。针对这种不足,根据期权特性提出期权决策树评估方法．即把实物期权的思想嵌入到决策树中，从而使评估包括了项目投资的不确定性价值。期权决策树分析方法采用了BS期权定价公式以及McDonald和Siegel关于是否继续生产的期权定价公式，计算出了不同决策方案的价值。并进一步讨论了不确定性的价值大小以及对决策的影响。示例证明：期权决策树方法比传统的净现值方法更具有科学性、准确性和实用性。     

一类地产期权的微分方程定价模型及其计算

该文以北京西奥中心写字楼为例,分析"以租待售"型房地产营销工具具有的分期付款期权特性,运用Δ-对冲技巧和Ito引理,构造了美式分期付款地产期权的微分方程定价模型,并确定了定价模型中各个变量的内涵,包括标的资产价格、波动率、期限和执行价等。针对北京西奥中心写字楼的具体市场数据,应用有限差分策略进行数值计算,得到了相应的期权价值。     

一类地产期权的微分方程定价模型及其计算

该文以北京西奥中心写字楼为例,分析“以租待售”型房地产营销工具具有的分期付款期权特性,运用Δ-对冲技巧和Ito引理,构造了美式分期付款地产期权的微分方程定价模型,并确定了定价模型中各个变量的内涵,包括标的资产价格、波动率、期限和执行价等。针对北京西奥中心写字楼的具体市场数据,应用有限差分策略进行数值计算,得到了相应的期权价值。     

Excel计算欧式看涨期权的价格二叉树

欧式看涨期权是基于标的股票的衍生证券,可以利用股票的二叉树模型通过链锁法求解欧式看涨期权的初始价格,本文主要采用excel通过股票的二叉树计算欧式看涨期权的初始价格,并且可以把其中的参数设为变量,表单建立后只需修改参数值就可以重新计算欧式看涨期权的价格,因此具有很大的实用性。     

基于期权思想的产业投资结构的控制与优化

运用期权的观点和方法研究产业投资问题具有重要的现实意义，在衡量投资机会价值的基础上，提出一和中产业增量投资期权决策思路，基于期权的观点建立了与项目投资决策类似的产业投资决策期权模型，提出一种控制与优化产业投资结构的新思路，具有一定的实际应用价值。     

求解非线性期权定价模型的自适应小波同伦摄动技术

Leland 模型是在考虑交易费用的情况下,对 Black - Scholes 模型进行修改得到的非线性期权定价模型. 本文针对 Leland 模型,提出了一种求解非线性动力学模型的自适应多尺度小波同伦摄动法. 该方法首先利用插值小波理论构造了用于逼近连续函数的多尺度小波插值算子,利用该算子可以将非线性期权定价模型方程自适应离散为非线性常微分方程组; 然后将用于求解非线性常微分方程组的同伦摄动技术和小波变换的动态过程相结合,构造了求解 Leland 模型的自适应数值求解方法. 数值模拟结果验证了该方法在数值精度和计算效率方面的优越性.     

R&D项目进展评估及决策选择模型

在分析R＆D项目技术和市场不确定性分布特征的基础上,提出多步骤四项式期权定价模型,用于R＆D项目进展评估.探讨了当出现不可预见信息时,对采取的多个管理决策的选择问题.实例分析表明,多步骤四项式模型能灵敏地监测项目的单因素变化引起的项目投资价值的变化,对管理的灵活性和不确定性能进行量化处理,在很大程度上提高了R＆D中期投资决策的准确性.     

基于合作创新网络的复杂装备协同研发绩效分析

由于分析制造业协同研发绩效时考虑的影响因素较为单一,导致分析精准度较低,提出基于合作创新网络的复杂装备协同研发绩效分析方法.采用合作创新网络构建复杂装备协同研发绩效机制模型,在此基础上建立复杂装备协同研发绩效评价指标体系.通过二叉树模型在MaxDEA软件中实现复杂装备协同研发绩效的自动评价,根据评价结果实现对复杂装备协...     

阶段性投资最优规模问题的实物期权方法

提出一种两阶段投资最优规模的实物期权方法。借助随机过程刻画3种典型投资风格下的决策触发时间以及决策所依赖的执行概率，利用扩张型实物期权估算或有投资决策的柔性价值，建立项目综合价值函数模型，以回报率为目标对投资最优规模进行数值求解。算例表明，选择最佳投资规模可避免回报不足和资金浪费。     

Real‐time waiting‐price trading interval in a heterogeneous options market: a Bernoulli distribution

Options pricing remains an open research question that is challenging for both theoreticians and practitioners. Unlike many classical binomial models that assume a “representative agent,” the model suggested herein considers two players who are heterogeneous with respect to their estimations of the distribution of the underlying asset price on expiration day, and with respect to their levels of willingness to make a transaction (eagerness level). A two‐player binomial model is developed to find the real‐time optimal option price in two stages. First, we determine a primary feasible pricing domain. We then find a narrower feasible domain, termed the “waiting‐price trading interval,” meaning the region within which the players may either wait for better offers (due to a change in market conditions or player beliefs), or make an immediate transaction. The suggested model is formulated by a nonlinear optimization problem and the optimal price is shown to be unique. We demonstrate that the counter player's eagerness level has a significant effect on the proposed optimal option price. Using empirical analysis, several known lattice‐based models for option pricing, such as CRR and Tian, are compared with the current model (herein, S‐H) in which the price offered by the model player takes into account the subjective beliefs of the opposing market player. The comparison shows significant advantages to the S‐H model in terms of the expected profit on expiration day.     

期权定价多元树的计算

树形结构法是期权定价的基本方法之一,其中二元树形法目前得到了广泛的应用．本文的主题是多元树形结构法,文中对其算法及构造进行了讨论,同时,还找出了二个新的五元树形结构模型和一个七元树形结构模型．与二元树形结构法相比,多元树形结构法能有更快的收敛速度和更高的计算精度,然而期权的内在收益函数(payoff function)的不光滑性会降低树形结构法的计算效率．本文讨论了克服这一缺陷的方法,最后文中对在定价计算中的效率问题进行了实证研究．     

Agent-Based Approach to Option Pricing Anomalies

Psychological studies on decision making under uncertainty, which have been inspired by Kahneman and Tversky's study, have attracted considerable interest in financial research as key factors to solve anomalies that cannot be explained by the traditional models. Recently, we proposed an agent-based prospect theoretical model and demonstrated that the loss-aversion feature of investors is capable of explaining a large number of financial stylized facts. This paper aims to extend the previous work to the field of option pricing. Two important anomalies in the field-the implied volatility smile and the skewness premium-will be analyzed. This paper can be considered as an attempt to integrate the behavioral financial theory and the option pricing theory by using the agent-based approach.     

Consumption Utility-Based Pricing and Timing of the Option to Invest with Partial Information

This paper extends real options theory to consider the situation where the mean appreciation rate of the value of an irreversible investment project is not observable and governed by an Ornstein–Uhlenbeck process. Our main purpose is to analyze the impact of the uncertainty of the mean appreciation rate on the pricing and investment timing of the option to invest under incomplete markets with partial information. We assume that an investor aims to maximize expected discounted utility of lifetime consumption. Based on consumption utility indifference pricing method, stochastic control and filtering theory, we obtain under CARA utility the implied values and the optimal investment thresholds of the option to invest, which are determined by a semi-closed-form solution to a free-boundary partial differential equation (PDE) problem. The solution is independent of the utility time-discount rate. We provide numerical results by finite difference methods and compare the results with those under a fully observable case. Numerical calculations show that partial information leads to a significant loss of the implied value of the option to invest. This loss, called implied information value, IIV increases quickly with the uncertainty of the mean appreciation rate. A high volatility of project values might decrease the IIV, as well as the implied value of the option.     

Improving option price forecasts with neural networks and support vector regressions

Options are important financial derivatives that allow investors to control their investment risks in the securities market. Determining the theoretical price for an option, or option pricing, is regarded as one of the most important issues in financial research; a number of parametric and nonparametric option pricing approaches have been presented. While the objective of option pricing is to find the current fair price, for decision making, in contrast, the forecasting activity has to accurately predict the future option price without advance knowledge of the underlying asset price. In this paper, a simple and effective nonparametric method of forecasting option prices based on neural networks (NNs) and support vector regressions (SVRs) is presented. We first modified the improved conventional option pricing methods, allowing them to forecast the option prices. Second, we employed the NNs and SVRs to further decrease the forecasting errors of the parametric methods. Since the conventional methods mimic the trends of movement of the real option prices, using these methods in a first stage allows the NNs and SVRs to concentrate their power in nonlinear curve approximation to further reduce the forecasting errors in a second stage. Finally, extensive experimental studies with data from the Hong Kong option market demonstrated the ability of NNs and SVRs to improve forecast accuracy.     

变精度粗糙集模型在决策树构造中的应用

本文在应用变精度粗糙集模型构造决策树的研究基础上,提出了具有置信度规则的决策树的构造方法。该方法是对决策树生成方法的一个改进,所构造的决策树具有更强的实用性以及更高的可理解性。本文还针对两个甚至两个以上属性的分类质量量度相等的特殊情形,给出了如何选择较优的属性作为结点的方法。与传统的ID3算法相比,该方法所构造的决策树不仅结构简单,而且更加实用,利于理解。     

Monetary pricing of software development risks: A method and empirical illustration

The ability to price (monetize) software development risks can benefit various aspects of software development decision-making. This paper presents a risk pricing method that estimates two parameters for every individual risk factor: extra cost incurred per unit exposure, and project sensitivity, to that factor. Since variability is a widely used measure of risk in finance and decision sciences, the method derives risk pricing parameters by relating variability in risk factors to variability in project cost. This approach rests on the fact that a parametric cost estimator predicts project cost by adjusting the “nominal” cost of a project based on the expected values of risk factors (cost drivers), but the actual project cost often deviates from prediction because the actual values of risk factors normally deviate from expectations. In addition, to illustrate the viability of the method, the paper applies the method empirically with COCOMO data, to approximate risk pricing parameters for four risk factors (Personnel Capability, Process Maturity, Technology Platform, and Application Task). Importantly, though, the method could work equally well with data recorded based on other parametric cost estimators. The paper also discusses several areas that can benefit from benchmark risk pricing parameters of the kind we obtain.