双变量参数联合函数的条件均值和条件方差(英文)

联合函数是指连接单变量边际分布的多变量函数.联合函数由Sklar(1959)在概率测度空间的内容时引入的.本文主要对边际分布是标准正态分布函数U(0,1)的Farlie-Gum-bel-Morgenstern和Gumbel-Hougaard这两个双变量参数联合函数进行研究,我们得到了他们密度函数的基本性质并导出了他们的条件均值和条件方差.另外,本文还给出了不同参数的条件均值和条件方差的相应图示,并进行了对比和解释.     

基于加权局域世界的金融网络演化模型研究

在加权局域世界网络模型的基础上,文章将复杂网络与金融系统相结合,提出一种新局域世界的加权金融网络模型.其主要思想是在加权局域世界网络中引入了服从指数分布的增长方式和双向择优选择的连边方式.利用平均场理论和仿真模拟得到网络节点度、强度和权值都服从幂律分布,其分布指数与所取参数有关.通过网络拓扑性质的研究和沪市A股股票数据的实证研究,发现该金融网络模型具有层次结构和异配特性,与实际网络具有相似的拓扑结构.     

沪深股市收益率分布的时变性

本文对沪深股市收益率的统计特性进行了讨论 ,检验了收益率分布的非正态性和独立同分布性 .就其独立同分布假设被拒绝的原因从相关和不同分布两方面进行了详细的分析与研究 .结果表明 :沪深股市收益率存在一定的自相关性 ,但相关程度很弱 ,不足以用来作为解释对其独立同分布假设拒绝的理由 ;均值的时变性不显著 ,但方差是时变的 ,均值与时变的方差一起可作为对独立同分布假设拒绝的原因 .     

带厚尾新息的非线性自回归函数型条件异方差模型的尾概率

研究带厚尾新息的非线性自回归函数型条件异方差(NARFCH)模型的平稳分布的尾概率. 结果表明, NARFCH序列的平稳分布的尾部紧密依赖于其条件方差. 当新息序列呈厚尾分布时, NARFCH序列的平稳分布的尾部会比新息序列的尾部更厚或更薄, 给出了具体的尾概率的增加或减少对条件方差的依赖公式, 并给出了两个具体例子来说明主要结果的应用.     

自回归序列的穿带率

穿零问题是时间序列分析中的一个重要研究内容,被广泛应用于语音识别、信号探测等科学研究领域.统计学者已经给出了二阶自回归序列AR(2)的渐进穿零率与一阶渐进相关函数的关系,以及均方渐近穿零率与自回归序列AR(P)的特征根的关系等一系列研究成果.在此基础上,本文引入了自回归序列AR(P)的渐近穿带率(BCR)的概念,建立了序列的2邻点渐近穿带率与一阶渐近相关函数之间的关系.当带宽足够窄时,用2邻点穿带率可以近似穿带率,从而建立了渐近穿带率和一阶渐近相关函数与方差的关系式.     

基于M估计的指数相关非线性混合效应模型的齐性检验(英文)

方差和相关系数的齐性是纵向数据分析中常用假设之一,然而,这些假设未必合适.本文主要研究的是具有指数相关结构的纵向数据非线性混合效应模型,首先将Huber函数引入模型的对数似然函数中,利用Fisher得分迭代法得到模型参数的稳健估计(M估计),然后基于M估计对模型的方差和相关系数的齐性进行了Score检验,并给出了检验统计量的Monte-Carlo模拟结果.最后用一个实例说明了本文的方法.     

均值波动率回归（英文）

在无风险资产和有风险证券的离散时间资产定价问题中,常用包含相关的随机成分和非随机成分的增量过程模型来表示.受此启发,文章提出了一类融合了非随机和随机成分的半参数回归模型.与经典的回归模型不同,在此模型中均值回归函数包含了方差部分,并且模型变量与某个状态变量有关联,因此模型更具有特定的经济意义.文中的一个例子解释了GARCH-M模型与现有的广义漂移模型不能包含本文中所提出的模型.文章还表明,虽然增量过程只是两个部分的加权和,但模型的统计推断不能够简单地通过两个独立系统来完成.文章研究了估计量的渐近理论性质,并通过蒙特卡洛模拟考察了估计量的小样本性质.最后利用中国金融年鉴2004-2005的数据分析了中国金融市场的财富增量过程.     

W链及其应用

本文定义一种新的马氏链,我们称为W链,它实际上是一致可数可加马氏链的推广,并用W链理论研究函数型随机方差非线性自回归模型平稳分布和矩的存在性,此时,只假设新息序列中的随机变量的分布函数关于Lebesgue分解中的绝对连续部分有密度函数,并不需要新息序列中的随机变量是连续型的.     

均值-方差准则及其应用

在位置-尺度分布族中研究了均值-方差准则与期望效用理论的一致性,特别指出当均值相等时或源的支撑可达到负无穷时,均值-方差准则与期望效用理论是完全一致的,这表明可以用均值-方差准则研究满足条件的经济问题、管理问题.还介绍了均值-方差准则在金融中的一些应用.     

偏正态混合效应模型中方差分量函数的参数Bootstrap推断

偏正态混合效应模型通过引入偏度参数,以便更好地刻画实际数据偏态特征,所以被广泛应用于众多实际领域.进一步,方差分量的假设检验一直是该模型的热点研究问题.因此,有必要在偏正态分布下系统讨论混合效应模型中方差分量函数的统计推断问题.首先,分别基于参数Bootstrap方法和广义方法探讨单个方差分量、方差分量之和、方差分量之比的单边假设检验和区间估计问题.其次,Monte Carlo结果表明,在所给样本量和参数设置下,参数Bootstrap方法大多数情况下优于广义方法.最后,将上述方法应用于空气质量指数的案例研究中,以验证所给方法的合理性与有效性.     

Temporal hierarchies with autocorrelation for load forecasting

We propose four different estimators that take into account the autocorrelation structure when reconciling forecasts in a temporal hierarchy. Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. In previous studies, weights assigned to the forecasts were given by the structure of the hierarchy or the forecast error variances without considering potential autocorrelation in the forecast errors. Our first estimator considers the autocovariance matrix within each aggregation level. Since this can be difficult to estimate, we propose a second estimator that blends autocorrelation and variance information, but only requires estimation of the first-order autocorrelation coefficient at each aggregation level. Our third and fourth estimators facilitate information sharing between aggregation levels using robust estimates of the cross-correlation matrix and its inverse. We compare the proposed estimators in a simulation study and demonstrate their usefulness through an application to short-term electricity load forecasting in four price areas in Sweden. We find that by taking account of auto- and cross-covariances when reconciling forecasts, accuracy can be significantly improved uniformly across all frequencies and areas.     

Inventory control under temporal demand heteroscedasticity

This paper studies an inventory control problem when the variance of demand is time-varying and exhibits temporal heteroscedasticity. We use a first-order autoregressive process to characterize the dynamic changes in the level of demand over time and a GARCH(1, 1) structure to describe the changes in the variance of demand. Under these demand settings, we quantify the effect of a temporal heterogeneous variance on inventory performance for a system controlled via an order-up-to-level policy. We show that the effect of temporal heteroscedasticity on the forecasting accuracy can be additively decomposed from the total forecasting error variance. The decomposition is used to derive the absolute and relative cost deviations when the temporal heteroscedasticity is ignored. The relationship of these cost deviations to demand autocorrelation and replenishment leadtime is investigated. Computational results show that ignoring temporal heteroscedasticity can increase firm’s inventory costs by as much as 30% when demand autocorrelation is highly positive.     

Variance swaps under the threshold Ornstein–Uhlenbeck model

Variance swap is a typical financial tool for managing volatility risk. In this paper, we evaluate different types of variance swaps under a threshold Ornstein–Uhlenbeck model, which exhibits both mean reversion and regime switching features in the underlying asset price. We derive the analytical solution for the joint moment generating function of log‐asset prices at two distinct time points. This enables us to price various types of variance swaps analytically. Copyright © 2017 John Wiley & Sons, Ltd.     

PARAMETER ESTIMATION FOR A DISCRETELY OBSERVED STOCHASTIC VOLATILITY MODEL WITH JUMPS IN THE VOLATILITY

In this paper a stochastic volatility model is considered. That is, a log price process Y which is given in terms of a volatility process V is studied. The latter is defined such that the log price possesses some of the properties empirically observed by Barndorff-Nielsen & Jiang[6]. In the model there are two sets of unknown parameters, one set corresponding to the marginal distribution of V and one to autocorrelation of V. Based on discrete time observations of the log price the authors discuss how to estimate the parameters appearing in the marginal distribution and find the asymptotic properties.     

成交量与价格限制对中国股市影响的实证分析

以我国股票市场的12支个股资料为例,研究了日收益相关性、成交量和价格限制的关系.研究发现,增加成交量会减弱自相关性的假设对近一半的股票成立.我们首先采用一般方法来估计模型,但这种方法用于研究这三者的关系存在一定的偏差,所以接下来又采用了广义矩估计(GMM)来观察估计结果的灵敏性.发现用GMM方法得出的结果表明成交量和价格限制的影响要强于一般方法得出的结果,价格限制影响表现出了对自回归系数更强烈的影响.     

Large Deviations of the Threshold Estimator of Integrated (Co-)Volatility Vector in the Presence of Jumps

Recently considerable interest has been paid to the estimation problem of the realized volatility and covolatility by using high-frequency data of financial price processes in financial econometrics. Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this paper, we adopt the threshold estimator introduced by Mancini (Scand Actuar J 1:42–52, 2004) where only the variations under a given threshold function are taken into account. The purpose of this work is to investigate large and moderate deviations for the threshold estimator of the integrated variance–covariance vector. This paper is an extension of the previous work in Djellout et al. (Stoch Process Appl 1–35, 2017), where the problem has been studied in the absence of a jump component. We will use the approximation lemma to prove large and moderate deviations results. As the reader can expect, we obtain the same results as in the case without jump.     

Non-stationarity in Sugar Prices

Time series analysis of unstable series such as raw material prices contains several methodological pitfalls. These are illustrated by a survey of the Box-Jenkins method of analysis applied to 12 years of daily sugar prices.Two problems in particular have received insufficient attention in previous analyses. First, the model variance fluctuates substantially over time. The logarithmic transformation of prices is recommended as significantly reducing the fluctuations. Second, it is essential to study the constant term in the Box-Jenkins model, which is shown to differ significantly from zero. This proves the existence of price trends. The commonly accepted random walk model for commodity prices is thus incorrect, at least for sugar.However, these price trends vary over time and this possibility is not covered by the Box-Jenkins models. Consequently, a new model for price movements is proposed, characterized by frequent changes in the daily price trend yet consistent with the results of the autocorrelation analysis.     

Forecasting model with asymmetric market response and its application to pricing of consumer package goods

This paper presents a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable—price promotion—by threshold models. As a threshold variable to generate a mechanism for different market responses, we use the counterpart to the concept of a price threshold applied to a representative consumer in a store. A Bayesian approach is taken for statistical modelling because of advantages that it offers over estimation and forecasting. The proposed model incorporates the lagged effects of a price variable. Thereby, myriad pricing strategies can be implemented in the time horizon. Their effectiveness can be evaluated using the predictive density. We intend to improve the forecasting performance over conventional linear time series models. Furthermore, we discuss efficient dynamic pricing in a store using strategic simulations under some scenarios suggested by an estimated structure of the models. Empirical studies illustrate the superior forecasting performance of our model against conventional linear models in terms of the root mean square error of the forecasts. Useful information for dynamic pricing is derived from its structural parameter estimates. This paper develops a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable—price promotion—by the threshold models. Copyright © 2005 John Wiley & Sons, Ltd.     

Portfolio selection under DEA-based relative financial strength indicators: case of US industries

Fundamental analysis is an approach for evaluating a firm for its investment-worthiness whereby the firm's financial statements are subject to detailed investigation to predict future stock price performance. In this paper, we propose an approach to combine financial statement data using Data Envelopment Analysis to determine a relative financial strength (RFS) indicator. Such an indicator captures a firm's fundamental strength or competitiveness in comparison to all other firms in the industry/market segment. By analysing the correlation of the RFS indicator with the historical stock price returns within the industry, a well-informed assessment can be made about considering the firm in an equity portfolio. We test the proposed indicator with firms from the technology sector, using various US industries and report correlation analyses. Our preliminary computations using RFS indicator-based stock selection within mean–variance portfolio optimization demonstrate the validity of the proposed approach.     

具有一致相关的纵向数据模型中方差和相关系数的齐性检验

在纵向数据分析中, 模型方差的齐性是一个基本假定, 但是该假定未必正确. 林金官、韦博成^[1]讨论了具有AR(1)误差的非线性纵向数据模型中方差和相关系数的齐性检验. 本文对具有一致相关协方差结构的纵向数据模型, 研究了方差齐性和相关系数齐性的检验, 得到了检验的score统计量, 并应用于葡萄糖数据. 最后, 本文还给出了模拟结果.