Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics

Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory.     

一类地下水水质污染问题广义解的唯一性及对初值和自由项的连续依赖性

本文讨论了一类地下水水质污染问题，其数学模型为一组非线性双曲—抛物耦合方程组，并证明了在相应初边值条件下广义解的唯一性及对初值和自由项的连续依赖性．     

Rockefeller Philanthropy and the Institute of Pacific Relations: A Reappraisal of Long-Term Mutual Dependency

Archival evidence is used to explore the development of funding for the Institute of Pacific Relations (IPR) and the Canadian Institute of International Affairs (the secretariat for the Canadian national council of the IPR) provided by the Rockefeller Foundation (RF). It suggests that the withdrawal of funding should be interpreted in a long-term perspective as a joint responsibility of mutually dependent recipients and funders, rather than simply as the fault of RF decision makers responding reactively to the prevailing climate of McCarthyism. Moreover, it is argued that the conventional wisdom as to the balance of influence within the RF between key staff and trustees is in need of revision. Conceptual implications of these examples of recipient dependence are explored, including the reasons for and dangers of such mutual dependence, the meaning of donor responsibility, and the mediating role of foundation officers.     

The Extremal Dependence Measure and Asymptotic Independence

Abstract

Extremal dependence analysis assesses the tendency of large values of components of a random vector to occur simultaneously. This kind of dependence information can be qualitatively different than what is given by correlation which averages over the total body of the joint distribution. Also, correlation may be completely inappropriate for heavy tailed data. We study the extremal dependence measure (EDM), a measure of the tendency of large values of components of a random vector to occur simultaneously and show consistency of an estimator of the EDM. We also show asymptotic normality of an idealized estimator in a restricted case of multivariate regular variation where scaling functions do not have to be estimated.     

Tail dependence for skew Laplace distribution and skew Cauchy distribution

ABSTRACT

Coefficient of tail dependence measures the strength of dependence in the tail of a bivariate distribution and it has been found useful in the risk management. In this paper, we derive the upper tail dependence coefficient for a random vector following the skew Laplace distribution and the skew Cauchy distribution, respectively. The result shows that skew Laplace distribution is asymptotically independent in upper tail, however, skew Cauchy distribution has asymptotic upper tail dependence.     

A family of bivariate binomial distributions generated by extreme bernoulli distributions

In this paper, bivariate binomial distributions generated by extreme bivariate Bernoulli distributions are obtained and studied. Representation of the bivariate binomial distribution generated by a convex combination of extreme bivariate Bernoulli distributions as a mixture of distributions in the class of bivariate binomial distribution generated by extreme bivariate Bernoulli distribution is obtained. A subfamily of bivariate binomial distributions exhibiting the property of positive and negative dependence is constructed. Some results on positive dependence notions as it relates to the bivariate binomial distribution generated by extreme bivariate Bernoulli distribution and a linear combination of such distributions are obtained.     

Elder Abuse in Residential Settings in Israel—Myth or Reality?

ABSTRACT

The article tries to answer the question: Is elder abuse in residential care in Israel a myth or a reality? A broad definition of abuse was used, including the ‘classical’ forms presented in the literature, and also violation of rights and any act that infringes on quality of life of residents. As no data exist which directly explored this issue, the analysis was based on: recent laws mandating the report of abuse, governmental surveillance data, court cases, media clippings, and data from quality of life studies. Findings reveal that abuse in its ‘classical’ form exists only in a limited number of small private unlicensed facilities. However, when using the broader definition, abuse is a reality in many settings, exercised mainly by nurses' aides.     

On LSE in regression model for long-range dependent random fields on spheres

We study the asymptotic behaviour of least squares estimators (LSE) in regression models for long-range dependent random fields observed on spheres. The LSE can be given as a weighted functional of long-range dependent random fields. It is known that in this scenario the limits can be non-Gaussian. We derive the limit distribution and the corresponding rate of convergence for the estimators. The results were obtained under rather general assumptions on the random fields. Simulation studies were conducted to support theoretical findings.     

Measures of dependence for the multivariate t distribution with applications to the stock market

Two measures of dependence for multivariate t and Cauchy random variables are developed based on Kullback–Leibler number. The mutual information number T(X) is obtained in a closed expression form, as well as its asymptotic distribution. A dependence coefficient ρ₁, is defined (based on the Kullback–Leibler number) with the properties of ρ₁=0 indicating independence and ρ₁=1indicating degeneracy. Two real life examples from the stock market are used to analyze the level of dependence and correlation among stocks.     

A Copula‐Based Non‐parametric Measure of Regression Dependence

Abstract. This article presents a framework for comparing bivariate distributions according to their degree of regression dependence. We introduce the general concept of a regression dependence order (RDO). In addition, we define a new non‐parametric measure of regression dependence and study its properties. Besides being monotone in the new RDOs, the measure takes on its extreme values precisely at independence and almost sure functional dependence, respectively. A consistent non‐parametric estimator of the new measure is constructed and its asymptotic properties are investigated. Finally, the finite sample properties of the estimate are studied by means of a small simulation study.