基于遗传算法的创业投资实物期权评价模型

传统创业投资评价定量方法主要包括内部收益率法、净现值法和回收期法等,这些分析方法为投资决策提供了量化的依据.但由于它们的一些假设忽略了投资环境的不确定性和管理灵活性等许多重要的现实影响因素而在实际应用中较难发挥作用,随着金融创新理论的发展,实物期权方法弥补了传统创业投资定量评价方法的局限性.但是,由于创业投资具有不确定性,复杂性,高风险性,阶段性等特点,实物期权法仍无法有效评价由于技术不确定性以及技术不确定性和市场不确定性之间相互作用对于创业投资评价的影响.因此,本文尝试采用遗传算法改进实物期权评价法,旨在提高创业投资评价的科学性和准确性.     

大型可租性投资项目的多阶段决策实物期权模型

对于大型可租性投资项目多阶段投资决策,传统的NPV法无法正确地评价其管理的柔性价值,本文引入实物期权理论,指出实物期权是对实物投资的选择权,对于多阶段的投资开发项目,当每一阶段完成后,即拥有了对后续阶段的投资期权.本文针对企业投资大型可租性项目的多阶段决策建立了相应的实物期权模型,并给出了每一阶段的期权价值以及最佳投资点的临界价格.     

实物期权定价模型在建筑项目投资决策中的应用

文章采用实物期权定价模型中的B-S和二叉树模型对建筑项目投资决策进行比较分析,通过案例,详细说明了实物期权在建筑项目投资决策中的应用方法,弥补了传统投资决策方法的缺陷和不足,使建筑项目投资决策更加科学合理.     

实物期权价值的蒙特卡洛模拟研究

一、项目投资的传统决策方法--净现值法(NPV) 传统的项目投资决策方法主要是指净现值法(NPV),其基本思路是通过对项目预期产生的各期净现金流的贴现值总和与起初投资成本之差即净现值的分析来决定投资与否.简单的说,当项目的净现值为正时应该投资,净现值为负时便拒绝投资.NPV有两个难以解决的问题:预期的现金流量难以把握和合理的贴现率难以确定.另外,NPV法还有一个很大的缺陷就是它忽略了项目投资过程中不确定性带来价值,往往低估投资项目的价值,致使经营者失去了很多好的投资机会.投资项目的实物期权评估方法将不确定性考虑到项目投资决策过程中,能够更加准确的评估项目的价值越来越受到理论界和实务界的关注.     

引入项目生命周期的实物期权评价方法

文章基于输配电建设工程项目具有投资专用性强、投资决策时间长、不确定高等特点,提出了一种引入项目生命周期的实物期权评价方法来评估输配电项目的投资价值,并与传统的DCF方法进行了比较分析,结果表明,采用分阶段的投资实物期权方法,可以更科学地测度输配电建设工程项目的价值.     

基于B-S公式的模糊实物期权研究

传统的实物期权分析将项目的收益和成本等参数仅仅看成确定的值或随机的变量,不能很充分的描述这些参数的性质.文章通过构造非线性三角模糊数,将其引入到连续时间实物期权的评估中以描述参数的不确定性,并在战略投资决策中得到合适的应用.     

基于实物期权的房地产项目投资决策模型

房地产项目投资具有投入高、周期长、风险大、回报高等特点,传统的投资决策方法已经不能为投资者提供科学的决策。文章将实物期权理论引用到房地产项目投资决策中,针对我国房地产投资的政策和市场特点,构建了适用于我国房地产项目投资决策的模型;并利用某房地产项目投资的数据,对该模型进行了检验。     

复合实物期权在石油开发项目投资决策中的应用

由于石油开发项目的投资具有不确定性、多阶段性、不可逆性以及投资战略的灵活性,决定了用传统的NPV法对其评价时存在先天不足,实物期权法的出现较好地弥补了此类不足.     

运用实物期权方法分析半导体产业的投资决策

一、引言 传统的投资决策方法忽视了大多数投资项目所具有的不可逆性和投资的可延迟性.所谓不可逆性就是说投资的初始成本至少部分是沉没的,当投资者改变主意时也不能完全收回投资的初始成本.在实际投资过程中还存在着投资时机的选择问题,即可以现在投资,也可以推迟投资以获取未来有关的更多信息.而20世纪70年代发展起来的实物期权理论为投资决策提供了新的研究思路.McDonald和Siegal(1986)基于实物期权方法分析了不确定条件下投资时机的选择问题.     

基于实物期权思想的能力配置决策方法

一、实物期权的基本原理 Amram and Kulatilaka(1999,p.5)对实物期权作如下简要定义:"期权是一项面向未来行动的权利而不是责任.期权的不确定性是其价值所在.许多能力配置相关的投资形成可能的后续发展机会.因此,能力配置相关的实物期权可以看作是现金流+期权".简言之,许多嵌于非金融资产和债务中的管理和操作灵活性可以被视为实物期权,它们代表着企业对于延迟不可撤销的决策,直至不确定性问题得到解决的管理能力.狭义地理解,实物期权方法是金融期权理论向实物(非金融)资产的延伸(Amram and Kulatilaka1999,p.6).学者们(Dixit and Pindyck,1994;Trigeorgis,1996;Amram and Kulatilaka,1999;Copeland and Antikarov,2001)在运用实物期权框架时,强调其在不确定条件下对于传统评估方法(如净现值法)的优势.     

B-S模型的扩展及其在石油开发中的应用

近年来，在石油勘探开发投资决策中，采用实物期权的方法进行评估取得了快速发展，但是随着国际油价的大幅走高，人民币汇率的坚挺，传统的B-S模型对实物期权的估价方法受到挑战。对多元伊藤过程进行研究，实现了期权估价的B-S模型的扩展，并且通过对开发勘探中随机变量的规范，引入国际油价和汇率两个变量，开发出一种新型的石油开发实物期权评价方法。     

Robust bootstrap densities for dynamic conditional correlations: implications for portfolio selection and Value-at-Risk

ABSTRACT

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of financial returns. Although the decisions depend on the forecasts covariance matrix little is known about effects of outliers on the uncertainty associated with these forecasts. In this paper we analyse these effects on the context of dynamic conditional correlation models when the uncertainty is measured using bootstrap methods. We also propose a bootstrap procedure to obtain forecast densities for return, volatilities, conditional correlation and Value-at-Risk that is robust to outliers. The results are illustrated with simulated and real data.     

Adaptive utility and trial aversion

Decision making with adaptive utility provides a generalisation to classical Bayesian decision theory, allowing the creation of a normative theory for decision selection when preferences are initially uncertain. In this paper we address some of the foundational issues of adaptive utility as seen from the perspective of a Bayesian statistician. The implications that such a generalisation has upon the traditional utility concepts of value of information and risk aversion are also explored, with a new concept of trial aversion introduced that is similar to risk aversion, but which concerns a decision maker's aversion to selecting decisions with high uncertainty over resulting utility.     

多重属性不确定性模型的分层拆解及应用

文章利用梯形模糊数构建多重属性不确定性模型,发现传统多重属性不确定性模型利用欧几里得距离判断决策正确程度,仅适用于二维复杂决策问题,对存在多种方案的复杂决策问题需进一步拆解与分层。运用最小化系数、向量距离公式等方法测度最优解与理想决策的差异值,通过比较差异值大小判断最优解的理想程度;同时基于满意度最大化理论确立缺失原始数据复杂决策问题的权重,为多重属性不确定性模型提供有效的运算数据。研究显示,多重属性不确定性模型排序方法具有较强的可操作性与适用性。     

Examples of option values in drug development

The option to stop a project is fundamental in drug development. The majority of drugs do not reach the market. Furthermore, many marketed drugs do not repay their development costs. It is therefore crucial to optimize the value of the option to stop. We formulate two examples of statistical models. One is based on success/failure in a series of trials; the other assumes that the commercial value evolves as a stochastic process as more information becomes available. These models are used to study a number of issues: the number and timing of decision points; value of information; speed of development; and order of trials. The results quantify the value of options. They show that early information that can change key decisions is most valuable. That is, we should nip bad projects in the bud. Modelling is also useful to analyse more complex decisions, for example, weighting the value of decision points against the cost of information or the speed of development. Copyright © 2003 John Wiley & Sons, Ltd.     

CEO特征与企业研发投入关系的实证研究

CEO作为企业投资决策的核心制定者,其自身特征无疑会对企业的研发活动产生重要影响。利用世界银行2005年对中国120个城市12 065家企业的调研数据,分析了CEO受教育水平、CEO任期、CEO自主权等三个因素对企业研发投入水平的影响,在控制了企业特征、行业等因素后,实证结果发现:(1)CEO受教育水平与企业研发投入水平显著正相关,即教育程度高的CEO更加重视研发活动;(2)CEO任期同企业研发投入水平显著正相关,即较长的任期有助于CEO着眼于长远目标,进而加大研发投入;(3)CEO自主权也同企业研发投入水平显著正相关,说明较高的CEO自主权有利于CEO通过努力实现自身人力资本价值,从而激励其加大研发的投入。     

Dealing with uncertainty: statistics for an aging population

"Uncertainty in statistics and demographic projections for aging and other policy purposes comes from four sources: differences in definitions, sampling error, nonsampling error, and scientific uncertainty. Some of these uncertainties can be reduced by proper planning and coordination, but most often decisions have to be made in the face of some remaining uncertainty. Although decision makers have a tendency to ignore uncertainty, doing so does not lead to good policy-making. Techniques for estimating and reporting on uncertainty include sampling theory, assessment of experts' subjective distributions, sensitivity analysis, and multiple independent estimates." The primary geographical focus is on the United States.     

基于云重心理论的信息价值测评研究

随着科学技术的迅猛发展和信息化进程的加速推进,信息作为一种促进经济和社会发展的资源已经越来越受到人们的重视。首先提出了信息价值的内涵,确立了信息价值测评的主要思想;其次,引入云重心理论的云发生器,用于计算信息价值评估模型的量化值,通过云重心评估模型,可以定量化的计算出信息价值的得分值,从而得到所求的信息价值;最后,以实际企业的调查数据为例,对企业信息价值进行了实际计算,得到了企业信息价值的具体量化值,以便为企业对信息进行决策时提供一种定量方法。     

Cointegration and Long-Run Asset Allocation

We show that economic restrictions of cointegration between asset cash flows and aggregate consumption have important implications for return dynamics and optimal portfolio rules, particularly at long investment horizons. When cash flows and consumption share a common stochastic trend (i.e., are cointegrated), temporary deviations between their levels forecast long-horizon dividend growth rates and returns, and consequently, alter the term profile of risks and expected returns. We show that the optimal asset allocation based on the error-correction vector autoregression (EC-VAR) specification can be quite different relative to a traditional VAR that ignores the cointegrating relation. Unlike the EC-VAR, the commonly used VAR approach to model expected returns focuses on short-run forecasts and can considerably miss on long-horizon return dynamics, and hence, the optimal portfolio mix in the presence of cointegration. We develop and implement methods to account for parameter uncertainty in the EC-VAR setup and highlight the importance of the error-correction channel for optimal portfolio decisions at various investment horizons.     

Bayesian validation assessment of multivariate computational models

Multivariate model validation is a complex decision-making problem involving comparison of multiple correlated quantities, based upon the available information and prior knowledge. This paper presents a Bayesian risk-based decision method for validation assessment of multivariate predictive models under uncertainty. A generalized likelihood ratio is derived as a quantitative validation metric based on Bayes’ theorem and Gaussian distribution assumption of errors between validation data and model prediction. The multivariate model is then assessed based on the comparison of the likelihood ratio with a Bayesian decision threshold, a function of the decision costs and prior of each hypothesis. The probability density function of the likelihood ratio is constructed using the statistics of multiple response quantities and Monte Carlo simulation. The proposed methodology is implemented in the validation of a transient heat conduction model, using a multivariate data set from experiments. The Bayesian methodology provides a quantitative approach to facilitate rational decisions in multivariate model assessment under uncertainty.