Industry shocks and empirical evidences on defaults comovements

It is commonly agreed that the credit defaults are correlated. However, the structure and magnitude of such dependence is not yet fully understood. This paper contributes to the current understanding about the defaults comovement in the following way. Assuming that the industries provides the basis of defaults comovement it provides empirical evidence as to how such comovements can be modeled using correlated industry shocks. Generalized linear mixed model (GLMM) with correlated random effects is used to model the defaults comovement. It is also demonstrated as to how a GLMM with complex correlation structure can be estimated through a very simple way. Empirical evidences are drawn through analyzing quarterly individual borrower level credit history data obtained from two major Swedish banks between the period 1994 and 2000. The results show that, conditional on the borrower level accounting data and macro business cycle variables, the defaults are correlated both within and between industries but not over time (quarters). A discussion has also been presented as to how a GLMM for defaults correlation can be explained.Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden</p

Computation and application of likelihood prediction with generalized linear and mixed models

This paper presents the computation of likelihood prediction with the generalized linear and mixed models. The method of likelihood prediction is briefy discussed and approximate formulae are provided to make easy computation of the likelihoodprediction with generalized linear models. For complicated prediction problems, simulation methods are suggested. An R add-in package is accompanied to carryout the computation of the predictive inference with the generalized linear and mixed models. The likelihood prediction is applied to the prediction of the credit defaults using a real data set. Results show that the predictive likelihood can be a useful tool to predict portfolio credit risk.Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden</p

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable general-ized linear model, it has been shown that in complicated cases LP produces better results than already know methods.Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden</p

Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups

This paper presents a two-step pseudo likelihood estimation for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the random intractable integrals in  the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study: An application of it with binary response variable is presented using a real dara set on credit defaults from two Swedish banks. Thanks to   the use of two-step estimation technique, the proposed algorithm outperforms conventional likelihood algoritms in terms of computational time.Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden</p

Computation and application of likelihood prediction with generalized linear and mixed models

This paper presents the computation of likelihood prediction with the generalized linear and mixed models. The method of likelihood prediction is briefy discussed and approximate formulae are provided to make easy computation of the likelihoodprediction with generalized linear models. For complicated prediction problems, simulation methods are suggested. An R add-in package is accompanied to carryout the computation of the predictive inference with the generalized linear and mixed models. The likelihood prediction is applied to the prediction of the credit defaults using a real data set. Results show that the predictive likelihood can be a useful tool to predict portfolio credit risk.Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden</p

Industry shocks and empirical evidences on defaults comovements

It is commonly agreed that the credit defaults are correlated. However, the structure and magnitude of such dependence is not yet fully understood. This paper contributes to the current understanding about the defaults comovement in the following way. Assuming that the industries provides the basis of defaults comovement it provides empirical evidence as to how such comovements can be modeled using correlated industry shocks. Generalized linear mixed model (GLMM) with correlated random effects is used to model the defaults comovement. It is also demonstrated as to how a GLMM with complex correlation structure can be estimated through a very simple way. Empirical evidences are drawn through analyzing quarterly individual borrower level credit history data obtained from two major Swedish banks between the period 1994 and 2000. The results show that, conditional on the borrower level accounting data and macro business cycle variables, the defaults are correlated both within and between industries but not over time (quarters). A discussion has also been presented as to how a GLMM for defaults correlation can be explained.Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden</p

Computation and application of likelihood prediction with generalized linear and mixed models

This paper presents the computation of likelihood prediction with the generalized linear and mixed models. The method of likelihood prediction is briefy discussed and approximate formulae are provided to make easy computation of the likelihoodprediction with generalized linear models. For complicated prediction problems, simulation methods are suggested. An R add-in package is accompanied to carryout the computation of the predictive inference with the generalized linear and mixed models. The likelihood prediction is applied to the prediction of the credit defaults using a real data set. Results show that the predictive likelihood can be a useful tool to predict portfolio credit risk.Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden</p