股指期权定价的非参数数值方法研究

扩散过程估计的参数化方法存在先入为主的不足,并且扩散项函数形式的设定十分困难,而非参数方法不需要数据产生过程的先验信息,直接从数据出发估计扩散函数,克服了以上不足。本文提出了一种基于连续时间过程的非参数股指期权定价模型。对于刻画基础资产动态行为特性的扩散函数不加任何函数形式限制,利用离散数据匹配密度函数构造它的非参数估计,进而计算股指期权的均衡价格。论文从理论上论证了扩散项估计的一致性和渐进正态性。实证研究表明,该方法对于实际市场价格具有较高的拟合效果,特别是在市场波动剧烈时期,非参数方法更优于经典B-S方法。     

核心竞争力:理论与战略问题

企业核心竞争力是以企业战略转型为实践根据提出的 ,是管理理论不断创新的结果。企业核心竞争力强调 ,在企业资源有效整合的基础上 ,形成独具的、支撑企业持续竞争优势的能力 ,这是企业长期制胜之根本。核心竞争力是企业战略层面的问题 ,是企业集聚核心业务、开发核心技术和提高核心价值的一整套战略。研究企业核心竞争力重在确立企业的核心竞争力战略     

21世纪的选择:新经济、新企业与新管理

21世纪新经济形态将会有很大的发展 ,新经济形态的标志是产生了新的生产方式即网络技术支撑下的智能化大规模定制生产方式。新的生产方式产生了新的经济运行规则 ,导致目前的企业必须进行转型成为适应新经济规则的新企业。新企业有许多不同于旧企业的特点 ,其中最根本的是新企业资源配置过程中知识是关键的要素 ,智力资本最为重要。因此新企业需要全新的管理理念、管理思想、管理模式、管理方式即新管理。新管理的核心价值观为 :以人为本 ,合作创新 ,自我超越 ,永远领先。     

Reducing data dimension for cluster detection

Clustering high-dimensional data is often a challenging task both because of the computational burden required to run any technique, and because the difficulty in interpreting clusters generally increases with the data dimension. In this work, a method for finding low-dimensional representations of high-dimensional data is discussed, specifically conceived to preserve possible clusters in data. It is based on the critical bandwidth, a nonparametric statistic to test unimodality, related to kernel density estimation. Some useful properties of the aforementioned statistic are enlightened and an adjustment to use it as a basis for reducing dimensionality is suggested. The method is illustrated by simulated and real data examples.     

Bewertung von sheätzungen bei einem linearen modell auf der grundlage des fiduzialkonzepts

Usingf fiducial inference the quality of estimations is investigated for a linear model under the conditionj that information about the vector to be estimated is derived from a small sample. By application of that model to the deduction of atmospheric temperature profiles the obtained results are illustrated numerically.     

Local and Variable Bandwidths and Local Linear Regression

Two types of non-global bandwidth, which may be called local and variable, have been defined in attempts to improve the performance of kernel density estimators. In nonparametric regression, local linear fitting has become a method of much popularity. It is natural, therefore, to consider the use of non-global bandwidths in the local linear context, and indeed local bandwidths are often used. In this paper, it is observed that a natural proposal in the literature for combining variable bandwidths with local linear fitting fails in the sense that the resulting mean squared error properties are those normally associated with local rather than variable bandwidths. We are able to understand why this happens in terms of weightings that are involved. We also attempt to investigate how the bias reduction expected of well-chosen variable bandwidths might be achieved in conjunction with local linear fitting.     

Finding Relevant Linear Manifolds in Classification by Gaussian Mixtures

In this article, we present a strategy for producing low-dimensional projections that maximally separate the classes in Gaussian Mixture Model classification. The most revealing linear manifolds are those along which the classes are maximally separable. Here we consider a particular probability product kernel as a measure of similarity or affinity between the class-conditional distributions. It takes an appealing closed analytical form in the case of Gaussian mixture components. The performance of the proposed strategy has been evaluated on real data.     

Estimation of a change point in the variance function based on the χ2-distribution

ABSTRACT

Let us consider that the variance function or its νth derivative in a regression model has a change/discontinuity point at an unknown location. To use the local polynomial fits, the log-variance function which break the positivity is targeted. The location and the jump size of the change point are estimated based on a one-sided kernel-weighted local-likelihood function which is provided by the χ²-distribution. The whole structure of the log-variance function is then estimated using the data sets split by the estimated location. Asymptotic results of the proposed estimators are described. Numerical works demonstrate the performances of the methods with simulated and real examples.     

Nonparametric estimation of the hazard function under dependence conditions

The estimation of the hazard rate has a great number of practical appli¬cations in dependence situations (seismicity analysis, reliability, economics), Based on kernel estimates of the density and the distribution function, we study the properties of the nonparametric estimator of the hazard function as-sociated with a strongly mixing time series. We prove consistency and asymp¬totic normality properties, and a cross-validation method for the smoothing parameter selection is studied. Some simulations and a practical application to real data are also shown.