Calibration to American options: numerical investigation of the de-Americanization method

American options are the reference instruments for the model calibration of a large and important class of single stocks. For this task, a fast and accurate pricing algorithm is indispensable. The literature mainly discusses pricing methods for American options that are based on Monte Carlo, tree and partial differential equation methods. We present an alternative approach that has become popular under the name de-Americanization in the financial industry. The method is easy to implement and enjoys fast run-times (compared to a direct calibration to American options). Since it is based on ad hoc simplifications, however, theoretical results guaranteeing reliability are not available. To quantify the resulting methodological risk, we empirically test the performance of the de-Americanization method for calibration. We classify the scenarios in which de-Americanization performs very well. However, we also identify the cases where de-Americanization oversimplifies and can result in large errors.     

基于任务规则树实现厂/站自动开操作票

提出了建立操作任务规则树而实现厂/站自动开票的新方法。其过程为:根据给定的操作任务，借助“与或”树建立规则树，然后分解为方案树，最后从方案树中整理出操作序列，实现系统中目标设备由初始状态向目标状态的转换。在对规则树的扩展研究过程中，解决了规则死锁、节点冗余及冲突等关键问题，从而实现了自动开操作票系统。     

GO法在继电保护可靠性评估中的初步应用

基于状态概率公式推导的方法可以对系统进行精确的定量分析，但当系统中包含有共有信号时分析将变得十分复杂。针对该问题，探讨了一种新的定量计算方法，将一种新的可靠性分析方法———GO法引入到电力系统领域中，探讨了该方法在继电保护系统可靠性分析中的应用。结合所开发的GO程序，可十分方便快捷地对系统进行精确的定量与定性分析。基于故障树法对同一系统的分析结果验证了GO法在继电保护可靠性分析中的可用性与正确性。最后，通过对2种方法特点的比较，阐明了GO法的特点和优势。     

树型网络中IPTV系统的流量均衡策略

提出一种在树型网络中的网络电视(IPTV)系统负载均衡算法,该算法通过服务器放置点的选择和服务器内容分配实现负载均衡.首先把大的树型网络分解成多个小的树型网络,对每个小的树型网络应用一种改进的搜索算法,最后把在多个子树的解组合成原问题的解,较好地将流量均匀分配到整个网络中.     

Is the ex‐ante equity risk premium always positive? Evidence from a new conditional expectations model

We model the conditional risk premium by combining principal component analysis and a statistical learning technique, known as boosted regression trees. The method is validated through various out‐of‐sample tests. We apply the estimates to test the positivity restriction on the risk premium and find evidence that the risk premium is negative in periods of low corporate and government bond returns, high inflation and downward‐sloping term structure. These periods are linked with changes in business cycles; the states when theories predict the existence of negative risk premium. Based on the evidence, we reject the conditional capital asset pricing model and raise a question over the practice of imposing the positive risk premium constraint in predictive models.     

Mapping technological development using patent citation trees: an analysis of bogie technology

We create a generational patent citation tree to reveal the development of bogie technology in the high-speed rail industry. We cull the information in patent citations to reveal the generational development of bogie technology by analysing patent titles, patent publication dates and patent-granting regions. We then measure the development of bogie technology by calculating its technological development on-orbit rate and by mapping the stages of technological development and its regional diffusion. The results show that bogie technologies are in a period of sustained growth and this period of sustained growth will remain. Technological development is accelerating and this acceleration will persist, although the global dispersion of technology lags behind in scope and speed.     

NONPARAMETRIC MODELS OF FINANCIAL LEVERAGE DECISIONS

This paper applies nonparametric decision tree models to the analysis of financial leverage decisions. This approach presents three appealing features: (i) the relationship between leverage and explanatory variables is not predetermined but is derived from information provided by the data, (ii) the models respect the fractional nature of leverage ratios, and (iii) each covariate is allowed to influence in different ways the financial leverage decisions of firms automatically assigned to different groups. Based on a data set of Portuguese firms, decision trees are used to tackle both classification (the decision to issue debt) and regression (the decision on the amount of debt to be issued, conditional on using debt) problems. It is found that: (i) two‐part models are the most appropriate specification for explaining the overall amount of debt used by firms, (ii) there are no drastic differences between the results produced by tree and parametric models, although some divergences may arise, and (iii) tree models suggest relationships between covariates and leverage that parametric models fail to capture, especially when the sample size is small.     

决策树算法在大学生心理档案系统中的应用研究

本文充分利用已有的学生心理测量结果与学生的基本信息,将数据挖掘技术嵌入到心理档案系统中,使用C4.5算法生成了决策树,并提出了一种新的决策树剪枝方法,该剪枝方法能够有效地屏蔽掉置信度较低的导出规则,极大提高了导出规则的有效性和准确性,为构建科学的大学生心理档案服务体系和模式奠定坚实基础,使大学生心理档案系统在心理健康教育中发挥高效、便捷的作用。     

Willow tree algorithms for pricing Guaranteed Minimum Withdrawal Benefits under jump-diffusion and CEV models

This paper presents the willow tree algorithms for pricing variable annuities with Guaranteed Minimum Withdrawal Benefits (GMWB), where the underlying fund dynamics evolve under the Merton jump-diffusion process or constant-elasticity-of-variance (CEV) process. The GMWB rider gives the policyholder the right to make periodic withdrawals from his policy account throughout the life of the contract. The dynamic nature of the withdrawal policy allows the policyholder to decide how much to withdraw on each withdrawal date, or even to surrender the contract. For numerical valuation of the GMWB rider, we use willow tree algorithms that adopt more effective placement of the lattice nodes based on better fitting of the underlying fund price distribution. When compared with other numerical algorithms, like the finite difference method and fast Fourier transform method, the willow tree algorithms compute GMWB prices with significantly less computational time to achieve a similar level of numerical accuracy. The design of our pricing algorithm also includes an efficient search method for the optimal dynamic withdrawal policies. We perform sensitivity analysis of various model parameters on the prices and fair participating fees of the GMWB riders. We also examine the effectiveness of delta hedging when the fund dynamics exhibit various jump levels.     

数据挖掘方法及其在我国财政管理领域的应用

以数据挖掘方法的基本概念和挖掘过程为基础,探讨数据挖掘方法在财政管理中的应用。主要内容有：（1）数据预处理,是指在对数据进行数据挖掘之前,先对原始数据进行必要的清洗、集成、转换、离散和规约等一系列的处理工作,使之符合挖掘算法进行知识获取研究所要求的最低标准;（2）分类挖掘法,包括贝叶斯算法、决策树算法、遗传算法、k－近邻算法和人工神经网络;（3）关联挖掘法,挖掘探索出隐藏在数据间的相关关系;（4）聚类挖掘法,按照样本在性质上的亲疏远近进行分类。通过对财政管理中的财政收入进行挖掘,得出结论：企业所得税虽然在数值上往往比营业税或者增值税高,但通过决策树算法进行挖掘,可以看到在财政收入中企业所得税与税收占比极可能具有更强的关联性。