A note on a simplified and general approach to simulating from multivariate copula functions

2013 ◽  
Vol 20 (9) ◽  
pp. 910-915 ◽  
Author(s):  
Barry K. Goodwin
Keyword(s):  
Copula Functions ◽  
Multivariate Copula

Setting of Academic Warning Based on Multivariate Copula Functions

2014 ◽  
Vol 571-572 ◽  
pp. 156-163 ◽  
Author(s):  
Jiu Ru Dai ◽  
Meng Yi Li ◽  
Wu Wei Li ◽  
Zhou Lu ◽  
Zhi Gang Zhang
Keyword(s):  
Colleges And Universities ◽  
Universal Method ◽  
Credit System ◽  
Copula Functions ◽  
Teaching Management ◽  
Credit Line ◽  
Current Setting ◽  
Train Of Thought ◽  
Academic Warning ◽  
Multivariate Copula

With the prevalence of credit system, the stipulation of “academic warning” is written into the teaching management constitution by more colleges and universities. However, the present research in this stipulation is only limited to the simulation of multivariate normal distribution. This paper aims to improve the current setting of academic warning through Monte Carlo simulation of multivariate Copula functions, and to calculate more reasonable academic warning credit line. The result demonstrates that the accuracy is significantly improved, therefore, this approach can provide a new train of thought and universal method for colleges and universities to set specific standards.

Flood Coincidence Risk Analysis Using Multivariate Copula Functions

2012 ◽  
Vol 17 (6) ◽  
pp. 742-755 ◽  
Author(s):  
Lu Chen ◽  
Vijay P. Singh ◽  
Guo Shenglian ◽  
Zenchao Hao ◽  
Tianyuan Li
Keyword(s):  
Risk Analysis ◽  
Copula Functions ◽  
Multivariate Copula

Effects of covariates on alternating recurrent events in accelerated failure time models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moumita Chatterjee ◽  
Sugata Sen Roy
Keyword(s):  
Recurrent Events ◽  
Failure Time ◽  
Likelihood Method ◽  
Model Parameters ◽  
Accelerated Failure Time ◽  
Survival Times ◽  
Copula Functions ◽  
Event Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

AbstractIn this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

ON DEFAULT CORRELATION AND PRICING OF COLLATERALIZED DEBT OBLIGATION BY COPULA FUNCTIONS

2006 ◽  
Vol 05 (03) ◽  
pp. 483-493 ◽  
Author(s):  
PING LI ◽  
HOUSHENG CHEN ◽  
XIAOTIE DENG ◽  
SHUNMING ZHANG
Keyword(s):  
Credit Derivatives ◽  
Copula Function ◽  
Dependence Structure ◽  
Default Correlation ◽  
Specific Kind ◽  
Copula Functions ◽  
Recovery Rates ◽  
Collateralized Debt Obligation ◽  
Collateralized Debt ◽  
Debt Obligation

Default correlation is the key point for the pricing of multi-name credit derivatives. In this paper, we apply copulas to characterize the dependence structure of defaults, determine the joint default distribution, and give the price for a specific kind of multi-name credit derivative — collateralized debt obligation (CDO). We also analyze two important factors influencing the pricing of multi-name credit derivatives, recovery rates and copula function. Finally, we apply Clayton copula, in a numerical example, to simulate default times taking specific underlying recovery rates and average recovery rates, then price the tranches of a given CDO and then analyze the results.

scholarly journals Parametric and Semiparametric Estimations of Bivariate Truncated Type I Generalized Logistic Models driven from Copulas

2017 ◽  
Vol 7 (1) ◽  
pp. 72 ◽  
Author(s):  
Lamya A Baharith
Keyword(s):  
Logistic Models ◽  
Real Data ◽  
Information Criterion ◽  
Logistic Distribution ◽  
Type I ◽  
Copula Functions ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Generalized Logistic Distribution ◽  
Weibull Models

Truncated type I generalized logistic distribution has been used in a variety of applications. In this article, a new bivariate truncated type I generalized logistic (BTTGL) distributional models driven from three different copula functions are introduced. A study of some properties is illustrated. Parametric and semiparametric methods are used to estimate the parameters of the BTTGL models. Maximum likelihood and inference function for margin estimates of the BTTGL parameters are compared with semiparametric estimates using real data set. Further, a comparison between BTTGL, bivariate generalized exponential and bivariate exponentiated Weibull models is conducted using Akaike information criterion and the maximized log-likelihood. Extensive Monte Carlo simulation study is carried out for different values of the parameters and different sample sizes to compare the performance of parametric and semiparametric estimators based on relative mean square error.

scholarly journals A Copula-Based Framework for Studying Intergenerational Income Mobility

2021 ◽  
Author(s):  
Anning Hu ◽  
Zhipeng Zhou
Keyword(s):  
Analytical Framework ◽  
Dependence Structure ◽  
Income Mobility ◽  
Mobility Patterns ◽  
Copula Functions ◽  
Intergenerational Income Mobility ◽  
Intergroup Comparison ◽  
Copula Density ◽  
Von Mises ◽  
Morphological Patterns

The sociological analysis of the mobility tables enhances the examination of the circulation mobility and helps one reveal the nuanced morphological patterns of mobility. In contrast, the economic analysis based on the measure of elasticity provides a handy way of covariate conditioning and statistically testing the similarities of mobility patterns across groups. In this article, we argue that the distinct methodological merits of these two approaches can be equipped by adopting a more comprehensive analytical framework using the copula functions: (1) The copula functions concern the dependence structure that is independent from the margins, which enable scholars to focus on the relative mobility; (2) The copula density, estimated either parametrically or non-parametrically, reveals the nuanced morphological mobility patterns; (3) By residualizing the marginal variables, the detected mobility pattern can be interpreted in a stronger causal sense; and (4) the Cramér–von Mises Test offers an easy-to-use statistic to conduct intergroup comparison of mobility patterns. The copula-based framework is illustrated by investigating the income mobility between 1978 and 2017 in the U.S., using the National Longitudinal Survey of Youth 1979 (NLSY79).

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