基于CARR模型的交易量与股价波动性动态关系的研究

股市交易量与股价变化的关系就一直是学术界与实务界所共同关心的主题。基于Chou(2005)提出的CARR模型对两者的动态关系问题进行了研究。首先分析了作为量价关系理论基础的混合分布假说理论在CARR模型中的适川性,进而基于混合分布假说理论对我国上证综合指数、深证成份指数以及随机抽取的十只个股进行了量价关系的实证检验。研究发现:混合分布假说理论同样适用于CARR模型,这证实了股价波动性的CARR效应的存在。实证的结果也证实了CARR模型无论是对于股票指数还是单只股票交易量都具有了良好的解释作用。因此,CARR模型与GARCH模型相比,在交易量与股价波动关系动态关系的研究领域可以得到更为稳健的结果。     

金融市场波动性CARR类模型与GARCH类模型的比较研究

在CARR模型基础上提出它的衍生模型ABSCARR模型,并利用广义误差分布(GED)讨论了它们的条件残差分布问题,最后运用CARR类模型对高频金融时间序列进行了实证分析.研究结果表明:CARR及其衍生模型在高频金融时间序列的价格波动性捕捉方面具有良好的效果,而GED的引入可以很好的用于分析CARR模型具体的条件分布情况,而CARR模型的条件残差分布应该并非只有指数分布与威布尔分布两种形式.     

Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data

The Birnbaum‐Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of the BS distribution, quantile regression modeling has not been considered for this distribution. To fill this gap, we introduce a class of quantile regression models based on the BS distribution, which allows us to describe positive and asymmetric data when a quantile must be predicted using covariates. We use an approach based on a quantile parameterization to generate the model, permitting us to consider a similar framework to generalized linear models, providing wide flexibility. The methodology proposed includes a thorough study of theoretical properties and practical issues, such as maximum likelihood parameter estimation and diagnostic analytics based on local influence and residuals. The performance of the residuals is evaluated by simulations, whereas an illustrative example of income data is conducted using the methodology to show its potential for applications. The numerical results report an adequate performance of the approach to quantile regression, indicating that the BS distribution is a good modeling choice when dealing with data that have both positive support and asymmetry. The economic implications of our investigation are discussed in the final section. Hence, it can be a valuable addition to the tool kit of applied statisticians and econometricians.     

Bayesian analysis of constant elasticity of variance models

This paper focuses on the estimation of some models in finance and in particular, in interest rates. We analyse discretized versions of the constant elasticity of variance (CEV) models where the normal law showing up in the usual discretization of the diffusion part is replaced by a range of heavy‐tailed distributions. A further extension of the model is to allow the elasticity of variance to be a parameter itself. This generalized model allows great flexibility in modelling and simplifies the model implementation considerably using the scale mixtures representation. The mixing parameters provide a means to identify possible outliers and protect inference by down‐weighting the distorting effects of these outliers. For parameter estimation, Bayesian approach is adopted and implemented using the software WinBUGS (Bayesian inference using Gibbs sampler). Results from a real data analysis show that an exponential power distribution with a random shape parameter, which is highly leptokurtic compared with the normal distribution, forms the best CEV model for the data. Copyright © 2006 John Wiley & Sons, Ltd.     

Case Influence Analysis in Bayesian Inference

Abstract

We demonstrate how case influence analysis, commonly used in regression, can be applied to Bayesian hierarchical models. Draws from the joint posterior distribution of parameters are importance weighted to reflect the effect of deleting each observation in turn; the ensuing changes in the posterior distribution of each parameter are displayed graphically. The procedure is particularly useful when drawing a sample from the posterior distribution requires extensive calculations (as with a Markov Chain Monte Carlo sampler). The structure of hierarchical models, and other models with local dependence, makes the importance weights inexpensive to calculate with little additional programming. Some new alternative weighting schemes are described that extend the range of problems in which reweighting can be used to assess influence. Applications to a growth curve model and a complex hierarchical model for opinion data are described. Our focus on case influence on parameters is complementary to other work that measures influence by distances between posterior or predictive distributions.     

Robust statistical modeling using the Birnbaum‐Saunders‐t distribution applied to insurance

In this paper, we carry out robust modeling and influence diagnostics in Birnbaum‐Saunders (BS) regression models. Specifically, we present some aspects related to BS and log‐BS distributions and their generalizations from the Student‐t distribution, and develop BS‐t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model. Copyright © 2011 John Wiley & Sons, Ltd.     

模糊影响图评价算法在供应链金融信用风险评估中的应用

传统的银行信贷模式风险评价专注于个体企业的财务数据.供应链金融新融资模式下的信用风险评价不同于传统的融资模式风险评价,它的评价范围更宽,不确定性因素更加复杂.在分析供应链金融模式的信用风险评价体系的基础上,结合模糊集和影响图理论建立了模糊影响图评价模型,对评估中难以量化的问题进行模糊处理,对变量之间的模糊影响关系进行分析,最后计算出信用风险概率分布.方法定性与定量相结合,为供应链金融新模式下的风险评估提供了一种新思路.     

基于Skewed-t分布的FIGARCH模型与VaR的度量

金融资产收益率序列的波动具有典型的尖峰厚尾和非对称性特征,描述这种特性需以合适的概率分布函数为基础.因此,寻求更好的概率分布函数对风险度量、VaR的计算有着十分重要的意义.有鉴于此引入Skewed-t分布度量VaR,并比较分析了RiskMetrics及FIGARCH类模型度量VaR值的准确程度,本文同时分析了多头头寸和空头头寸情况下的VaR.结果表明,在两种头寸情况下,Skewed-t分布在空头和多头情形对资产厚尾特性以及非对称性的拟合效果均要比正态分布好;在两种头寸中不同的置信水平下,FIAGARCH(CHUNG)模型预测的VaR值改进了使用传统模型的精确性,高估或低估风险的程度较轻.     

单纯形分布非线性模型的局部影响分析及其应用

讨论了单纯形分布非线性模型的局部影响分析问题.应用Cook(1986)的影响曲率方法研究了该模型关于微小扰动的局部影响,得到了局部影响分析的曲率度量.同时也应用PoonW Y和Poon Y S(1997)的保形法曲率方法研究了该模型的局部影响.对常见的扰动模型,分别进行了局部影响分析,得到了计算影响矩阵的简洁公式.最后还研究了两个实例,说明文中方法的应用价值.     

Local and non‐local ductile damage and failure modelling at large deformation with applications to engineering

The numerical analysis of ductile damage and failure in engineering materials is often based on the micromechanical model of Gurson [1]. Numerical studies in the context of the finite‐element method demonstrate that, as with other such types of local damage models, the numerical simulation of the initiation and propagation of damage zones is strongly mesh‐dependent and thus unreliable. The numerical problems concern the global load‐displacement response as well as the onset, size and orientation of damage zones. From a mathematical point of view, this problem is caused by the loss of ellipticity of the set of partial di.erential equations determining the (rate of) deformation field. One possible way to overcome these problems with and shortcomings of the local modelling is the application of so‐called non‐local damage models. In particular, these are based on the introduction of a gradient type evolution equation of the damage variable regarding the spatial distribution of damage. In this work, we investigate the (material) stability behaviour of local Gurson‐based damage modelling and a gradient‐extension of this modelling at large deformation in order to be able to model the width and other physical aspects of the localization of the damage and failure process in metallic materials.     

AN ADAPTIVE LEARNING FRAMEWORK FOR FORECASTING SEASONAL WATER ALLOCATIONS IN IRRIGATED CATCHMENTS

Abstract This paper describes an adaptive learning framework for forecasting end‐season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end‐irrigation‐season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end‐season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk‐management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST‐SOI incorporated) demonstrated ANN capability of forecasting end‐of‐season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model.     

条件自回归极差模型的渐近性质

在误差项独立同分布的条件下，本文讨论了条件自回归极差模型条件解和无条件解的渐近性质．利用随机游动的极限性质得到了条件解收敛于无条件解的充分条件，任意阶矩有限的充要条件以及外生变量与内生变量持续性的充要条件．所得到的结论适用于已得到应用的平稳条件自回归极差模型，也适用于包含单位根的模型和满足条件的其他类型的非平稳过程，为模型的统计推断提供了理论基础．     

HABITAT PATCH OCCUPANCY DYNAMICS OF GLAUCOUS‐WINGED GULLS (LARUS GLAUCESCENS) II: A CONTINUOUS‐TIME MODEL

ABSTRACT. The diurnal distribution and abundance dynamics of loafing Glaucous‐winged Gulls (Larus glaucescens) were examined at Protection Island National Wildlife Refuge, Strait of Juan de Fuca, Washington. Asynchronous movement of gulls among three habitat patches dedicated to loafing was modeled as a function of environmental variables using differential equations. Multiple time scale analysis led to the derivation of algebraic models for habitat patch occupancy dynamics. The models were parameterized with hourly census data collected from each habitat patch, and the resulting model predictions were compared with observed census data. A four‐compartment model explained 41% of the variability in the data. Models that predict the dynamics of organism distribution and abundance enhance understanding of the temporal and spatial organization of ecological systems, as well as the decision‐making process in natural resource management.     

A MCMC approach for modeling customer lifetime behavior using the COM‐Poisson distribution

One of the major challenges associated with the measurement of customer lifetime value is selecting an appropriate model for predicting customer future transactions. Among such models, the Pareto/negative binomial distribution (Pareto/NBD) is the most prevalent in noncontractual relationships characterized by latent customer defections; ie, defections are not observed by the firm when they happen. However, this model and its applications have some shortcomings. Firstly, a methodological shortcoming is that the Pareto/NBD, like all lifetime transaction models based on statistical distributions, assumes that the number of transactions by a customer follows a Poisson distribution. However, many applications have an empirical distribution that does not fit a Poisson model. Secondly, a computational concern is that the implementation of Pareto/NBD model presents some estimation challenges specifically related to the numerous evaluation of the Gaussian hypergeometric function. Finally, the model provides 4 parameters as output, which is insufficient to link the individual purchasing behavior to socio‐demographic information and to predict the behavior of new customers. In this paper, we model a customer's lifetime transactions using the Conway‐Maxwell‐Poisson distribution, which is a generalization of the Poisson distribution, offering more flexibility and a better fit to real‐world discrete data. To estimate parameters, we propose a Markov chain Monte Carlo algorithm, which is easy to implement. Use of this Bayesian paradigm provides individual customer estimates, which help link purchase behavior to socio‐demographic characteristics and an opportunity to target individual customers.     

Return and Value at Risk using the Dirichlet Process

There exists a wide variety of models for return, and the chosen model determines the tool required to calculate the value at risk (VaR). This paper introduces an alternative methodology to model‐based simulation by using a Monte Carlo simulation of the Dirichlet process. The model is constructed in a Bayesian framework, using properties initially described by Ferguson. A notable advantage of this model is that, on average, the random draws are sampled from a mixed distribution that consists of a prior guess by an expert and the empirical process based on a random sample of historical asset returns. The method is relatively automatic and similar to machine learning tools, e.g. the estimate is updated as new data arrive.     

基于经验似然的部分线性模型的统计诊断

经验似然方法己经被广泛应用于许多模型的统计推断.本文基于经验似然对部分线性模型进行统计诊断.首先给出模型的估计方程,进而得到模型参数的极大经验似然估计;其次,基于经验似然研究了三种不同的影响曲率;最后通过随机模拟和实例分析,说明了统计诊断方法的有效性.     

Information initiatives of mobile retailers: a regression analysis of zero‐truncated count data with underdispersion

Our paper presents an empirical analysis of the association between firm attributes in electronic retailing and the adoption of information initiatives in mobile retailing. In our attempt to analyze the collected data, we find that the count of information initiatives exhibits underdispersion. Also, zero‐truncation arises from our study design. To tackle the two issues, we test four zero‐truncated (ZT) count data models—binomial, Poisson, Conway–Maxwell–Poisson, and Consul's generalized Poisson. We observe that the ZT Poisson model has a much inferior fit when compared with the other three models. Interestingly, even though the ZT binomial distribution is the only model that explicitly takes into account the finite range of our count variable, it is still outperformed by the other two Poisson mixtures that turn out to be good approximations. Further, despite the rising popularity of the Conway–Maxwell–Poisson distribution in recent literature, the ZT Consul's generalized Poisson distribution shows the best fit among all candidate models and suggests support for one hypothesis. Because underdispersion is rarely addressed in IT and electronic commerce research, our study aims to encourage empirical researchers to adopt a flexible regression model in order to make a robust assessment on the impact of explanatory variables. Copyright © 2014 John Wiley & Sons, Ltd.     

Bayesian cohort models for general cohort table analyses

Summary New Bayesian cohort models designed to resolve the identification problem in cohort analysis are proposed in this paper. At first, the basic cohort model which represents the statistical structure of time-series social survey data in terms of age, period and cohort effects is explained. The logit cohort model for qualitative data from a binomial distribution and the normal-type cohort model for quantitative data from a normal distribution are considered as two special cases of the basic model. In order to overcome the identification problem in cohort analysis, a Bayesian approach is adopted, based on the assumption that the effect parameters change gradually. A Bayesian information criterion ABIC is introduced for the selection of the optimal model. This approach is so flexible that both the logit and the normal-type cohort models can be made applicable, not only to standard cohort tables but also to general cohort tables in which the range of age group is not equal to the interval between periods. The practical utility of the proposed models is demonstrated by analysing two data sets from the literature on cohort analysis. The Institute of Statistical Mathematics     

On computational aspects of Bayesian spatial models: influence of the neighboring structure in the efficiency of MCMC algorithms

This study applies computationally intensive methods for Bayesian analysis of spatially distributed data. It is assumed that the space is divided in contiguous and disjoint regions or areas. The neighboring structure in a given problem may indicate a wide range of number of neighbors per area, ranging from very few neighbors to cases where all areas neighbor each other. The main aim of this work is to evaluate the influence of neighborhood on results of Markov Chain Monte Carlo (MCMC) methods. Proper and improper prior specifications for state parameters are compared. Three schemes, proposed in the literature, for sampling from the joint posterior distribution are also compared. The comparison criterion is based on the autocorrelation structure of the chains. Two classes of models are studied: the first one is characterized by a simple model without any explanatory variables and the second one is an extension with multiple regression components. Initially, sensitivity of the analysis to different prior distributions is addressed. Finally, extensive empirical analyses confront the outcomes obtained with different neighboring arrangements of the units. Results are shown to generalize those obtained with dynamic or state space models.     

单指标众数模型的统计诊断及在波士顿房价分析中的应用

在金融、经济、社会科学、气候科学、环境科学、工程技术和生物医学等领域,数据分布常常呈现出尖峰厚尾的特征,且密度分布是不对称的有偏分布。此时,单指标众数模型是刻画这些特征的一个重要方法。为此,非常有必要研究该模型下的统计诊断。本文将考虑单指标众数模型基于数据删除模型和众数漂移模型的统计诊断与局部影响分析。模拟研究和波士顿房价数据的结果表明所提出的方法是有效和可行的。