The paper examines dynamic systematic risk nature of Indian companies in the frame of the market model. The closing weekly prices of 89 Indian stocks and BSE 100 Index as the market index during the period from January 2000 to December 2013 are analyzed with rolling OLS, multivariate GARCH models, semiparametric regression and a Kalman filter. According to the results for the analyzed period, in 44 out of 89 cases Kalman Filter is the best model, while semiparametric regressions - in the other 45 cases. What concerns the forecasted period, for 41 out of 89 stocks multivariate GARCH models surprisingly outperform both semiparametric models (33 out of 89) and a Kalman Filter technique (15 out of 89). Moreover, analysis of betas dynamic shows that for 5% significance level 59 and 62 out of 89 time-varying betas processes are non-stationary according to ADF and Philips-Perron tests respectively and only one of the processes is stationary according to KPSS test.
This paper examines the dynamic beta of Russian companies within the framework of the market model. The closing weekly prices of 29 Russian stocks, six Russian sector indices and the MICEX Index as a market index during the period from January 2009 to June 2015 are used to estimate time-varying beta using various econometric techniques. According to the results for the analyzed period, semiparametric regressions are confirmed to be the most effective model. As regards the forecast period, multivariate GARCH models surprisingly outperform all the other methods. An analysis of beta dynamics shows that most of time-varying betas are non-stationary.
In this paper we consider the behavior of Kalman Filter state estimates in the case of distribution with heavy tails .The simulated linear state space models with Gaussian measurement noises were used. Gaussian noises in state equation are replaced by components with alpha-stable distribution with different parameters alpha and beta. We consider the case when "all parameters are known" and two methods of parameters estimation are compared: the maximum likelihood estimator (MLE) and the expectation- maximization algorithm (EM). It was shown that in cases of large deviation from Gaussian distribution the total error of states estimation rises dramatically. We conjecture that it can be explained by underestimation of the state equation noises covariance matrix that can be taken into account through the EM parameters estimation and ignored in the case of ML estimation.
Smoking is a problem, bringing signifi cant social and economic costs to Russiansociety. However, ratifi cation of the World health organization Framework conventionon tobacco control makes it possible to improve Russian legislation accordingto the international standards. So, I describe some measures that should be taken bythe Russian authorities in the nearest future, and I examine their effi ciency. By studyingthe international evidence I analyze the impact of the smoke-free areas, advertisementand sponsorship bans, tax increases, etc. on the prevalence of smoking, cigaretteconsumption and some other indicators. I also investigate the obstacles confrontingthe Russian authorities when they introduce new policy measures and the public attitudetowards these measures. I conclude that there is a number of easy-to-implementanti-smoking activities that need no fi nancial resources but only a political will.
One of the most important indicators of company's success is the increase of its value. The article investigates traditional methods of company's value assessment and the evidence that the application of these methods is incorrect in the new stage of economy. So it is necessary to create a new method of valuation based on the new main sources of company's success that is its intellectual capital.