Computation of the effects of uncertainty in volatility on option pricing and hedging

We quantify the effect of uncertainty in the volatility parameter σ on the Black–Scholes price of the European and American put. We apply probabilistic uncertainty analysis to the Black–Scholes model and compare the results with those of the Uncertain Volatility model. From historical data, we calibrate a probability distribution for the volatility. We then use Monte Carlo (MC) and a surrogate Polynomial Chaos (PC)/MC method to compute uncertainty bounds. The calibrated probability distribution is not one related to a standard orthogonal basis, so a basis is constructed numerically for the PC approximation. We show how to construct one stably from the probability distribution. We show that both methods give the same results, and quantify the relative speedup of the surrogate method. Finally, we investigate the effect of the parametric uncertainty, and show, for example, that the presence of uncertainty smoothes out the optimal exercise boundary of the American put.     

Novel passivity criteria for neural networks with discrete and unbounded distributed time-varying delays

Novel passivity criteria are presented for the passivity of a class of cellular neural networks with discrete and unbounded distributed time-varying delays. Two types of uncertainty are considered: one is time-varying structured uncertainty while the other is interval uncertainty. The Gu's discretized Lyapunov–Krasovskii functional method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz–Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for passivity of delayed neural networks. A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing passivity criteria.     

γ谱仪测量^109Cd的影响因素研究

不确定度分析是实现放射性活度测量质量保证的重要方面．本文对谱仪测量工作中影响加。Cd比活度的因素进行了研究与分析,为γ能谱分析工作及放射性活度测量中不确定度评定提供有益借鉴．     

基于贝叶斯理论的不确定度评定

传统的测量不确定评定是A类、B类评定,与这两类评定方法不同,提出了一种基于贝叶斯理论的测量不确定度评定方法.此方法综合了历史信息和当前样本信息,通过建立贝叶斯模型得出后验分布,从而进行测量不确定度评定.通过实例说明,贝叶斯评定比A类评定更为合理.     

Community discovery method with uncertainty measure of overlappingnodes based on topological potential

Community discovery of complex networks,esp.of social networks,has been a hotly debated topic in academic circles in recent years.Since actual networks usually contain some overlapping nodes that are d...     

Robust tracking control for micro machine tools with load uncertainties

The quality of the micro-mechanical machining outcome depends significantly on the tracking performance of the miniaturized linear motor drive precision stage. The tracking behavior of a direct drive design is prone to uncertainties such as model parameter variations and disturbances. Robust optimal tracking controller design for this kind of precision stages with mass and damping ratio uncertainties was researched. The mass and damping ratio uncertainties were modeled as the structured parametric uncertainty model. An identification method for obtaining the parametric uncertainties was developed by using unbiased least square technique. The instantaneous frequency bandwidth of the external disturbance signals was analyzed by using short time Fourier transform technique. A two loop tracking control strategy that combines the μ-synthesis and the disturbance observer (DOB) techniques was proposed. The μ-synthesis technique was used to design robust optimal controllers based on structured uncertainty models. By complementing the μ controller, the DOB was applied to further improving the disturbance rejection performance. To evaluate the positioning performance of the proposed control strategy, the comparative experiments were conducted on a prototype micro milling machine among four control schemes: the proposed two-loop tracking control, the single loop μ control, the PID control and the PID with DOB control. The disturbance rejection performances, the root mean square (RMS) tracking errors and the performance robustness of different control schemes were studied. The results reveal that the proposed control scheme has the best positioning performance. It reduces the maximal errors caused by disturbance forces such as friction force by 60% and the RMS errors by 63.4% compared with the PID control. Compared to PID with DOB control, it reduces the RMS errors by 29.6%.     

特大洪水演变过程及不确定性研究项目专题(三)——溃坝模型

研究小组通过现场溃坝试验和试验室溃坝试验收集到了大量的数据,并以此作为今后建模模拟研究工作的基础.研究小组对现有各模型进行了测试,对其模拟性能进行了比较.文章介绍了模拟研究得到的主要成果,并以此为用户提出建议,以期提高模型选择的水平和使用效果.     

边坡稳定分析方法综述及发展趋势研究

边坡稳定性问题是岩土工程中的一个重要研究内容。边坡破坏具有不同的模式,相对应便产生了多种分析方法。采用列表比较方法侧重列述了各分析方法的优缺点及工程适用范围,以便于工程实践应用的参考。总体上将稳定性分析方法分为定性分析、定量分析和不确定性分析三类,并在最后对边坡稳定性分析方法的发展趋势作了展望。     

不确定多状态时滞分布式系统的鲁棒H_∞控制

针对不确定多状态时滞分布式连续系统, 研究了其鲁棒H∞控制器的设计方法.通过引入Lyapunov函数, 利用线性矩阵不等式方法得到了自治系统的鲁棒渐近稳定的充分条件.基于该充分条件给出了闭环系统的鲁棒渐近稳定的充分条件, 并设计了系统的鲁棒H∞状态反馈控制器.设计的控制器保证了闭环系统的二次稳定, 同时使系统的H∞范数小于给定的衰减水平γ.仿真结果证明了该设计方法的有效性.     

最大熵不确定型决策模型与应用

现有不确定型决策方法中,各种状态的不确定性没有在决策方法中得到体现,且没有充分挖掘利用益损值信息,不同的决策准则往往得到不同的决策结果,不利于决策者做出正确决策.鉴于此,提出了基于信息不确定性的最大熵决策模型,通过构建状态分布的信息熵和决策结果离差信息的多准则目标函数,以状态概率分布为优化变量,采用加速遗传算法优化求解.防洪调度决策分析结果显示,最大熵决策模型可充分挖掘益损值信息,基于信息熵的不确定性准则使状态概率分布更加合理,决策结果可区分性优于其他决策方法.