In this article, an analysis of the fundamental methods of risk assessment and risk management of credit portfolio is conducted. In particular, complex and qualitative methods of risk management of credit portfolio studied in details, namely analytical, statistical and coefficient methods. Based on the coefficient method the author proposes a number of standards for the assessment of potential losses in credit activity.
This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.
Goodness-of-Fit Tests for Bivariate Time Series of Counts