Статистические методы построения, уточнения и проверки соответствия моделей
An article represents a comprehensive overview of approaches to capital structure modeling on the example of the public corporation Silvinit. At first, there are provided a short review of the company and of the corresponding industry followed by the description of how the analogues for the company were chosen. The next part of the article gives a step-by-step description of the practical implementation of such models as WACC model, EBIT-EPS, method of operational profit. Monte-Carlo approach is used for demonstrating an influence of the leverage increase on tax and interest payments as well as company's default risk. In conclusion the authors compare the results of different approaches with the current capital structure of Silvinit.
This paper examines two Markov chain Monte Carlo methods that have been widely used in econometrics. An introductory exposition of the Metropolis algorithm and the Gibbs sampler is provided. These methods are used to simulate multivariate distributions. Many problems in Bayesian statistics can be solved by simulating the posterior distribution. Invariance condition is of importance, the proofs are given for both methods. We use finite Markov chains to explore and substantiate the methods. Several examples are provided to illustrate the applicability and efficiency of the Markov chain Monte Carlo methods. They include bivariate normal distribution with high correlation, bivariate exponential distribution, mixture of bivariate normals.