多元cardinal样条空间的极值性质

通过研究多元ｃａｒｄｉｎａｌ样条函数插植的逼近性质,给出了各向异性Ｓｏｂｏｌｅｖ光滑函数类Ｗ＾ｒ∞（Ｒ＾ｄ）在Ｌ∞（Ｒ＾ｄ）尺度下最佳逼近的弱渐近估计,这个结果表明,多元ｃａｒｄｉｎａｌ样条函数空间是各向异性Ｓｏｂｏｌｅｖ光滑类Ｗ＾ｒ∞（Ｒ＾ｄ）在Ｌ∞（Ｒ＾ｄ）尺度下关于无穷维Ｋｏｌｍｏｇｏｒｏｖ宽度的弱渐近极子空间,也表明多元ｃａｒｄｉｎａｌ样条函数插值是实现线性宽度的最优算子。     

基于代数神经网络的多元多项式不可约判定及学习算法

把感知器作为数学模型,充分利用神经元的运算特性,以二元多项式近似求根神经网络模型为基础,设计一类多元多项式不可约判定的神经网络模型,它是单输入多输出三层前向神经网络,给出神经网络学习算法,这种学习算法在ｐ－ａｄｉｃ意义下,通过调整隐层与输出层的权值Ｃｉ,ｊ完成学习,可确定出多元多项式不可约,通过算例表明,该算法有效,相比传统的判定算法,可操作性强。     

多项分布与多元Poisson分布

通过对多项分布与多元Poisson分布关系的研究,得到多元独立的非负整值随机变量X1,X2,…,Xn每一个服从Poisson分布的充分必要条件,并从另一个方面描述了二项分布与Poisson分布的内在关系。     

非均匀矩形格点上的插值基

利用构造性代数几何工具, 给出任意非均匀矩形格点上的插值基.     

基于神经网络技术的干散货运价指数预测

在考虑了干散货运输需求、干散货船舶供给以及干散货船舶闲置等对运价指数影响的基础上,采用神经网络技术对月度干散货指数进行了研究,并且与基于ARCH模型和多元线性回归模型的预测进行了对比研究,结果表明从提高预测精度的角度来说神经网络技术是最优的.     

多元零次有理插值的存在条件及算法

用构造性代数几何工具, 研究由 R^d中一组给定节点的信息构造节点子集上的多元零次有理插值函数, 给出了插值函数的存在条件及相应算法.     

The US Dollar/Euro Exchange Rate: Structural Modeling and Forecasting During the Recent Financial Crises

The paper investigates the determinants of the US dollar/euro within the framework of the asset pricing theory of exchange rate determination, which posits that current exchange rate fluctuations are determined by the entire path of current and future revisions in expectations about fundamentals. In this perspective, we innovate by conditioning on Fama–French and Carhart risk factors, which directly measures changing market expectations about the economic outlook, on new financial condition indexes and macroeconomic variables. The macro‐finance augmented econometric model has a remarkable in‐sample and out‐of‐sample predictive ability, largely outperforming a standard autoregressive specification. We also document a stable relationship between the US dollar/euro Carhart momentum conditional correlation (CCW) and the euro area business cycle. CCW signals a progressive weakening in economic conditions since June 2014, consistent with the scattered recovery from the sovereign debt crisis and the new Greek solvency crisis exploded in late spring/early summer 2015. Copyright © 2016 John Wiley & Sons, Ltd.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting the duration of short‐term deflation episodes

The paper proposes a simulation‐based approach to multistep probabilistic forecasting, applied for predicting the probability and duration of negative inflation. The essence of this approach is in counting runs simulated from a multivariate distribution representing the probabilistic forecasts, which enters the negative inflation regime. The marginal distributions of forecasts are estimated using the series of past forecast errors, and the joint distribution is obtained by a multivariate copula approach. This technique is applied for estimating the probability of negative inflation in China and its expected duration, with the marginal distributions computed by fitting weighted skew‐normal and two‐piece normal distributions to autoregressive moving average ex post forecast errors and using the multivariate Student t copula.     

The Information Content of Equity Block Trades on the Warsaw Stock Exchange: Conventional and Bootstrap Approaches

This paper focuses on the Polish stock market by analysing the information content of 95 equity block trade transactions executed on shares of companies constituting the WIG20 index. A normalized conventional approach and a bootstrap approach are used to draw inferences. These approaches make use of a multivariate regression model with two explanatory variables: a market return and a dummy variable for the event. Resampling allows construction of an empirical distribution of the normalized test statistic. The outcomes obtained from the application of a normalized conventional approach as well as a bootstrap approach are in line and confirm that equity block trade transactions carry an important signal to investors. Significant abnormal positive (negative) returns are associated with the execution of the equity block trades, the prices of which are higher (lower) than the closing prices 2 days before the execution of the equity block trade transactions. Copyright © 2015 John Wiley & Sons, Ltd.