HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

Horwitz's rule, transforming both sides and the design of experiments for mechanistic models

Summary. The paper develops methods for the design of experiments for mechanistic models when the response must be transformed to achieve symmetry and constant variance. The power transformation that is used is partially justified by a rule in analytical chemistry. Because of the nature of the relationship between the response and the mechanistic model, it is necessary to transform both sides of the model. Expressions are given for the parameter sensitivities in the transformed model and examples are given of optimum designs, not only for single-response models, but also for experiments in which multivariate responses are measured and for experiments in which the model is defined by a set of differential equations which cannot be solved analytically. The extension to designs for checking models is discussed.     

Real nonparametric regression using complex wavelets

Summary.  Wavelet shrinkage is an effective nonparametric regression technique, especially when the underlying curve has irregular features such as spikes or discontinuities. The basic idea is simple: take the discrete wavelet transform of data consisting of a signal corrupted by noise; shrink or remove the wavelet coefficients to remove the noise; then invert the discrete wavelet transform to form an estimate of the true underlying curve. Various researchers have proposed increasingly sophisticated methods of doing this by using real-valued wavelets. Complex-valued wavelets exist but are rarely used. We propose two new complex-valued wavelet shrinkage techniques: one based on multiwavelet style shrinkage and the other using Bayesian methods. Extensive simulations show that our methods almost always give significantly more accurate estimates than methods based on real-valued wavelets. Further, our multiwavelet style shrinkage method is both simpler and dramatically faster than its competitors. To understand the excellent performance of this method we present a new risk bound on its hard thresholded coefficients.     

基于主成分分析的我国西部地区间产业结构转换能力评价

产业结构的演进和转换是经济发展的本质特征,也是促进经济持续稳定协调发展的关键。西部地区产业结构转换能力的地区差异十分明显,本文阐述了影响地区产业结构转换的一般因素,并通过主成分分析方法对影响西部地区产业结构转换的主要因素进行了分析,并对西部各地区产业结构转换能力、转换速度以及转换方向进行了分析评价。     

上市公司虚减成本费用的方法分析

中国证券市场尚不完善 ,上市公司操纵利润的现象屡见不鲜。虚减成本费用是上市公司调增利润更简便的方法。在财务运作中 ,上市公司频繁运用虚拟资产、利息资本化等五种方法虚减成本费用     

管理案例教学法的探析

管理案例教学法是现代管理学教育中不可替代的重要方法 ,是目前大学教育中弥补学生管理实践不足、锻炼提高学生学会管理本领的重要手段 ,同时也是促进教师素质提高的重要途径。本文对此试作探析。     

论科技期刊的品牌资本

科技期刊的品牌资本是其生存和发展的关键因素,品牌资本体现了社会效益与经济效益的同一性。品牌资本的价值回归是一个缓慢但却是相当稳定的过程。通过抽样调查和方差分析,定量说明了上述论点的正确性。     

多媒体英语教学的优势

本文旨在通过提出多媒体教学手段的多种优势以及诸多专家学者的观点,同时谈到笔者在使用多媒体进行外语教学过程中的一些切身体会,进行理论和实践上的论证,以发掘外语教学中利用多媒体教学手段进行创新性“教”与自主性“学”的可能性,并进一步加强学生的自主性学习以及自我调节性学习,使外语教师更加高效地利用多媒体的优势进行理想的外语教学,同时使外语教学手段更加具有开放性和实践性。     

Geometric Method and Generalized Linear Models. Two Opposite Multiparametric Approaches Illustrated on a Sample of Pituitary Adenomas

We used two statistical methods to identify prognostic factors: a log-linear model (logistic and COX regression, based on the notions of linearity and multiplicative relative risk), and the CORICO method (ICOnography of CORrelations) based on the geometric significance of the correlation coefficient. We applied the methods to two different situations (a "case-control study' and a "historical cohort'). We show that the geometric exploratory tool is particularly suited to the analysis of small samples with a large number of variables. It could save time when setting up new study protocols. In this instance, the geometric approach highlighted, without preconceived ideas, the potential role of multihormonality in the course of pituitary adenoma and the unexpected influence of the date of tumour excision on the risk attached to haemorrhage.     

浅谈类比法

首先给出了类比的定义 ,就是根据两个对象之间在某些方面的相似或相同而推出它们在其他方面也可能相似或相同的一种逻辑方法 ,并介绍了它的二个主要特点 ,即跳跃性和可靠程度低。然后结合一些成功的类比案例 ,对它的具体应用方式进行了探讨。最后给出了提高类比法使用可靠性的三个原则 ,一是所根据的相似属性要尽可能多 ;二是所根据的相似属性之间的联系要尽可能紧密 ;三是所根据的相似数学模型要尽可能精确