对分法在多元优化问题中的推广应用

将一维优化方法中的对分法推广应用于无约束多元优化问题的寻优求解中,基于对分法求解的优化问题必须具有有限边界搜索区域的考虑,讨论了问题初始搜索域的确定方法;并进一步给出了对分法推广应用的算法实施过程.最后通过算例与目前现有的几种确定性求导寻优法进行了比较.     

基于Android系统的课堂记录与多元分析系统

为避免教师听课评课过程中的过分主观性,开发出基于Android系统的量化分析与质性分析的听课软件,可以帮助教师对信息时代下的课堂教学进行更为有效的剖析与解读.在移动学习、教师共同体及课堂案例多元分析法等理论基础上,确定了由认知目标、学习方式、教学策略和技术作用四方面组成的课堂教学多元分析框架,设计并实现了基于Android系统的课堂教学分析系统的主要功能.     

Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting.     

基于多元统计分析的故障检测方法

作为数据驱动故障检测方法中的重要分支,基于多元统计分析的故障检测方法主要包括主元分析、偏最小二乘、独立元素分析和费舍尔判别分析.本文回顾了上述几种方法,包括数据模型、故障检测的原理及方法优劣.仿真实验说明了几种方法的特性及其故障检测的效果,并探讨了基于数据故障检测方法中的一些问题.     

Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility

This paper considers how information from the implied volatility (IV) term structure can be harnessed to improve stock return volatility forecasting within the state-of-the-art HAR model. Factors are extracted from the IV term structure and included as exogenous variables in the HAR framework. We found that including slope and curvature factors leads to significant forecast improvements over the HAR benchmark at a range of forecast horizons, compared with the standard HAR model and HAR model with VIX as IV information set.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

基于多变量高斯过程模型的贝叶斯建模与稳健参数设计

针对模型预测偏差和波动的稳健参数设计问题,在多变量高斯过程(multivariate Gaussian process,MGP)建模的框架下,结合质量损失函数和非线性优化约束方法构建一个新的多响应优化模型.首先,利用成对估计方法获得超参数近似值,构建多变量高斯模型;其次,结合MGP模型特征,构造充分考虑响应波动因素的质量损失函数.利用蒙特卡罗模拟方法,获得响应落入指定区间的期望概率;然后,以期望概率为约束,结合本文所提质量损失函数建立优化模型;最后,利用全局优化算法进行寻优,获得考虑响应期望概率的优化结果.实际案例和软件仿真表明,该方法综合权衡了预测偏差和预测波动引起的不确定性对优化结果的影响.获得了兼顾质量损失和期望概率最优均衡解,从而实现稳健参数设计.     

Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Morgan—Scott剖分上样条空间S6^3（Δms）的维数

众所周知,ＭｏｒｇｅｎＳｃｏｔ剖分上的样条空间的维数依赖于剖分的几何性质．本文证明了Ｄ．Ｄｉｅｎｅｒ提出的猜想对ｒ＝３是正确的．     

基于一类完备格诱导的直觉模糊集的多元扩展运算

基于一种二元序完备格引入了直觉模糊集的截集的概念,给出了直觉模糊集的加、减和乘法等算术运算,获得了这种截集的算术运算性质,并通过实例说明这些包含关系可以严格成立.此外,在完备格诱导的直觉模糊集的截集基础上,得到了直觉模糊集的多元扩展原理,进而讨论了多元扩展运算的2个基本性质.