基于最小二乘法的长周期实物期权精确估值迭代模拟算法

提出了一种基于最小二乘法的长周期实物期权精确估值迭代模拟算法,并通过一个商用通信卫星在轨服务投资决策的算例对该算法的实现进行了说明.算法将一个需要一次进行大量运算的问题转变为一个需要进行多次运算但每次运算的计算量相对较小的问题,能够很好地解决在缺乏并行计算的条件下大量模拟运算所面临的计算资源瓶颈问题,不仅能够得到较为精确的实物期权价值的点估计值和区间估计值,也便于推导最优的投资策略.     

美式期权隐含波动率校准问题的研究

研究的是美式期权的隐含波动率校准问题.首先提出一个正则化的最小二乘方法,在对其惩罚问题研究后找到最小二乘问题的最优条件,并给出美式期权波动率校准问题的算法.最后,通过数值算例说明了方法的有效性.     

不确定的时间范围下含期权的最优投资决策

不同于以往研究的含期权的最优投资消费决策,研究了不确定的时间范围下含期权的最优投资决策,运用动态规划原理和随机分析的方法,解决对应的最优控制问题,最优策略可通过对应的HJB方程得到,并显式地得到了HARA效用下的最优投资策略及最优财富过程.     

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.     

GARCH模型下基于偏最小二乘的欧式股指期权定价——来自香港恒生指数期权市场的证据

目前,股指期权呼之欲出,在这种形势下,本文对股指期权定价问题进行了研究。本文首先在GARCH模型的基础上导出期权定价估值公式,其次,在GARCH欧式股指期权定价模型的基础上,融入偏最小二乘技术,给出最终的欧式股指期权的偏最小二乘定价方法。最后,对香港恒指期权进行参数估计和GARCH建模,运用新的定价方法进行期权定价。研究发现,对最终期权价格影响最大的是GARCH模型的估计值;另外整个大盘的活跃程度、投资者情绪也有不可忽视的影响。这个结论为中国顺利发展指数期权市场提供了坚实有力的定价依据。     

基于实物期权定价方法的林业碳汇项目投资决策仿真研究

文章针对林业碳汇项目投资决策的复杂性、动态性和不确定性过程,利用林业—碳汇共同经营决策模型计算林业碳汇项目在投资期内的期望价值,采用实物期权定价方法对不同阶段不同策略下的林业碳汇项目价值进行评估,同时提出了多主体仿真建模方法,利用NetLogo仿真软件对林业碳汇项目投资决策过程进行动态模拟。仿真系统中涉及到的主体有林地、CO₂和投资者,投资者主要是作为观察者的身份,在不同阶段会做出不同的投资策略。模拟仿真三种不同状态下投资者的决策变化:一是传统林业投资动态模拟,不包含碳汇和期权因素动态模拟;二是引入碳汇市场后的林业投资动态模拟;三是引入碳汇市场和期权后林业投资动态模拟。NetLogo仿真分析结果表明引入碳汇市场可以提高投资者的收益并改变投资者的经营策略,同时引入期权,不仅增加了投资者的积极性而进行扩张投资,还可以更好地发挥林木碳汇功能,体现林业的生态价值及经济价值。     

基于贝叶斯MCMC算法的美式期权定价

鉴于美式期权的定价具有后向迭代搜索特征,本文结合Longstaff和Schwartz提出的美式期权定价的最小二乘模拟方法,研究基于马尔科夫链蒙特卡洛算法对回归方程系数的估计,实现对美式期权的双重模拟定价.通过对无红利美式看跌股票期权定价进行大量实证模拟,从期权价值定价误差等方面同著名的最小二乘蒙特卡洛模拟方法进行对比分析,结果表明基于MCMC回归算法给出的美式期权定价具有更高的精确度.模拟实证结果表明本文提出的对美式期权定价方法具有较好的可行性、有效性与广泛的适用性.该方法的不足之处就是类似于一般的蒙特卡洛方法,会使得求解的计算量有所加大.     

美式障碍期权定价的总体最小二乘拟蒙特卡罗模拟方法

障碍期权的价格依赖于其标的资产的价格路径,实际市场中标的资产的价格变化存在跳跃现象。本文在跳跃扩散模型下使用总体最小二乘拟蒙特卡罗方法(TLSFM)对美式障碍期权定价问题进行了研究。TLSFM使用随机化的Faure序列并结合总体最小二乘回归方法,改进了Longstaff等提出的最小二乘蒙特卡罗模拟方法(LSM)。通过基于TLSFM与LSM和改进的三叉树方法的美式障碍期权定价结果的比较分析,说明了基于TLSFM的美式障碍期权定价具有结果稳定,时效性更强的优势。     

实物期权方法在自主创新项目投资决策中的应用

传统的投资决策方法由于蕴含着不确定性和可逆转性的假设使其不适应于高风险、高收益并存的自主创新项目投资决策.将实物期权思想融入自主创新项目投资决策方法,考虑了项目由于柔性经营的期权价值,能更准确地反映项目的价值,从而提高投资决策的科学性和合理性.从实物期权理论的基本原理出发,通过具体实例对比说明实物期权方法应用于自主创新项目投资决策的优势.     

基于LSMC模型的煤炭资源价值定价研究

煤炭资源价值定价可以抽象为一种美式期权定价问题.最小二乘蒙特卡洛模拟(LSMC)方法是解决美式期权定价问题的一个有效途径.详尽地分析了Cortazar等人的基于资源价格、利率和便利收益随机变动的三因素定价模型,利用向量Ito定理提出了三因素模型中价格、利率和便利收益变量的递推公式.对LSMC方法原理进行了细致的阐述,总结出实现LSMC方法的完整过程,并在Matlab环境下编制了LSMC算法实现程序,进行算例计算.算例结果表明,LSMC方法用于资源定价是有效可靠的.研究为煤炭资源价值定价提供了一个完整具有可操作性的工具.     

An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We show that increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R&D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that the magnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty.     

投资项目的期权评价与最优投资规则

本文介绍了不确定环境下的投资项目的期权评价方法和最优投资规则，研究了单期项目和连续投资项目的投资决策问题，探讨了实物期权评价方法与传统的净现值评价方法中最优投资规则的差异，并对影响最优投资规则的差异因素进行了敏感性分析，得出了直观而有实用价值的结论。     

不确定竞争市场投资决策

本文针对不确定的竞争市场 ,分析现在作一个数量为 I的不可逆投资 ,产生一个生产容量 k,以在将来不确定竞争市场中比潜在进入的竞争对手具有某种占先优势这样一个投资机会的策略投资行为和机会的价值。用博奕论方法分析和给出了基于现在投资可获得将来增长期权价值的决策方法。     

Decision-dependent probabilities in stochastic programs with recourse

Stochastic programming with recourse usually assumes uncertainty to be exogenous. Our work presents modelling and application of decision-dependent uncertainty in mathematical programming including a taxonomy of stochastic programming recourse models with decision-dependent uncertainty. The work includes several ways of incorporating direct or indirect manipulation of underlying probability distributions through decision variables in two-stage stochastic programming problems. Two-stage models are formulated where prior probabilities are distorted through an affine transformation or combined using a convex combination of several probability distributions. Additionally, we present models where the parameters of the probability distribution are first-stage decision variables. The probability distributions are either incorporated in the model using the exact expression or by using a rational approximation. Test instances for each formulation are solved with a commercial solver, BARON, using selective branching.     

Computational strategies for non-convex multistage MINLP models with decision-dependent uncertainty and gradual uncertainty resolution

In many planning problems under uncertainty the uncertainties are decision-dependent and resolve gradually depending on the decisions made. In this paper, we address a generic non-convex MINLP model for such planning problems where the uncertain parameters are assumed to follow discrete distributions and the decisions are made on a discrete time horizon. In order to account for the decision-dependent uncertainties and gradual uncertainty resolution, we propose a multistage stochastic programming model in which the non-anticipativity constraints in the model are not prespecified but change as a function of the decisions made. Furthermore, planning problems consist of several scenario subproblems where each subproblem is modeled as a nonconvex mixed-integer nonlinear program. We propose a solution strategy that combines global optimization and outer-approximation in order to optimize the planning decisions. We apply this generic problem structure and the proposed solution algorithm to several planning problems to illustrate the efficiency of the proposed method with respect to the method that uses only global optimization.     

Optimal investment under operational flexibility, risk aversion, and uncertainty

Traditional real options analysis addresses the problem of investment under uncertainty assuming a risk-neutral decision maker and complete markets. In reality, however, decision makers are often risk averse and markets are incomplete. We confirm that risk aversion lowers the probability of investment and demonstrate how this effect can be mitigated by incorporating operational flexibility in the form of embedded suspension and resumption options. Although such options facilitate investment, we find that the likelihood of investing is still lower compared to the risk-neutral case. Risk aversion also increases the likelihood that the project will be abandoned, although this effect is less pronounced. Finally, we illustrate the impact of risk aversion on the optimal suspension and resumption thresholds and the interaction among risk aversion, volatility, and optimal decision thresholds under complete operational flexibility.     

Investment in electric energy storage under uncertainty: a real options approach

In this paper we develop a real options approach to evaluate the profitability of investing in a battery bank. The approach determines the optimal investment timing under conditions of uncertain future revenues and investment cost. It includes time arbitrage of the spot price and profits by providing ancillary services. Current studies of battery banks are limited, because they do not consider the uncertainty and the possibility of operating in both markets at the same time. We confirm previous research in the sense that when a battery bank participates in the spot market alone, the revenues are not sufficient to cover the initial investment cost. However, under the condition that the battery bank also can receive revenues from the balancing market, both the net present value (NPV) and the real options value are positive. The real options value is higher than the NPV, confirming the value of flexible investment timing when both revenues and investment cost are uncertain.     

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.