Copulaesque Versions of the Skew-Normal and Skew-Student Distributions

A recent paper presents an extension of the skew-normal distribution which is a copula. Under this model, the standardized marginal distributions are standard normal. The copula itself depends on the familiar skewing construction based on the normal distribution function. This paper is concerned with two topics. First, the paper presents a number of extensions of the skew-normal copula. Notably these include a case in which the standardized marginal distributions are Student’s t, with different degrees of freedom allowed for each margin. In this case the skewing function need not be the distribution function for Student’s t, but can depend on certain of the special functions. Secondly, several multivariate versions of the skew-normal copula model are presented. The paper contains several illustrative examples.

Research on Optimal Allocation Strategy of Bank Credit Funds Based on KMV Model and Logit Model

Using KMV model, normal Copula function, K-means cluster analysis and logit model, this paper constructs the enterprise credit risk assessment model, bank credit fund optimal allocation model, banking risk index system, and synthetically uses software such as MATLAB、SPSS to solve the problem of credit fund distribution strategy for small and medium-sized enterprises, and draws the conclusion that the loan interest rate classification of enterprise credit risk assessment, the weight of bank to credit fund distribution, and the change of bank risk index weight in sudden situation.Finally, the above model provides the strategy for bank credit fund allocation and gives the test and evaluation. The outstanding features of this paper are: using the KMV model and the normal Copula function, combining the enterprise credit rating and default times to establish a linear model to quantify the enterprise credit risk, will not beeasy to calculate the industry violation probability quantitative analysis, also get the bank credit annual interest rate fordifferent industries and levels of enterprises, and through the representative industries of the optimal loan weight calculation, so that the bank decision has the characteristics of the least unit risk. This paper also establishes a banking risk index system with emergency factors, which is of practical significance to make decision analysis of emergency events.

Modeling Local BES Indicators by Copula-Based Bayesian Networks

In Italy, the measure of the Equitable and Sustainable Well-being is provided by the Italian Institute of Statistics by means of a dashboard of basic and composite indicators. To investigate the dependence structure between the different domains of well-being, we propose the use of Non-Parametric Bayesian Networks based on the normal copula distribution, that allow to explore the conditional independence relationships between the composite indicators. The main advantage of the non-parametric models is that, as opposed to the parametric approach, they do not require any assumption on the marginal distributions of the variables. The proposed model is applied to the Equitable and Sustainable Well-being indicators measured at the provincial level and enriches the analysis of well-being by inspecting similarities and differences between Italian urban areas and territories.

A Time-Varying Multivariate Noncentral Contaminated Normal Copula Model and Its Application to the Visualized Dependence Analysis of Hong Kong Stock Markets

Financial data usually have the features of complexity and interdependence structure, such as asymmetric, tail, and time-varying dependence. This study constructs a new multivariate skewed fat-tailed copula, namely, noncentral contaminated normal (NCCN) copula, to analyze the dependent structure of financial market data. The dynamic conditional correlation (DCC) model is also incorporated into constructing the time-varying NCCN copula model. This study comprehensively examines the effects of the DCC-NCCN copula and related models on fitting dependence structures of Hong Kong stock markets. The results show that the DCC-NCCN copula model can better depict the dependence structures of returns. Considering the flexibility and complexity, the DCC-NCCN copula model is a relatively ideal, time-varying, multivariate skewed fat-tailed copula model.

Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application

The exchangeability and radial symmetry assumptions on the dependence structure of the multivariate data are restrictive in practical situations where the variables of interest are not likely to be associated to each other in an identical manner. In this paper, we propose a flexible class of multivariate skew normal copulas to model high-dimensional asymmetric dependence patterns. The proposed copulas have two sets of parameters capturing asymmetric dependence, one for association between the variables and the other for skewness of the variables. In order to efficiently estimate the two sets of parameters, we introduce the block coordinate ascent algorithm and discuss its convergence property. The proposed class of multivariate skew normal copulas is illustrated using a real data set.