Оценка рыночного риска на основе VaR с учетом дней ожидаемой повышенной волатильности.
Nowadays investors are facing changing conditions of global financial markets and should evaluate risks correctly. The most crucial factor is market risk that defines financial stability and investment results of professional participants at financial market and its clients. One of the characteristics of American stocks are higher volatility during financial report announcements. Common VaR methodology doesn’t take this into consideration as it lowers volatility during such periods and lowers it in other cases. Thereby a more flexible HVD-VaR model is proposed that allows risk estimation for each period separately. This can be possible due to the fact that announcement days are predefined. The proposed methodology is effective for a half of S&P500 stocks, so it’s useful for several financial instruments AS a result a more precise risk estimation method is proposed that considers extreme price movements caused by earnings announcement.
The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact connected by simple curves over which training and test accuracy are nearly constant. We introduce a training procedure to discover these high-accuracy pathways between modes. Inspired by this new geometric insight, we also propose a new ensembling method entitled Fast Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in the time required to train a single model. We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10, CIFAR-100, and ImageNet.
Advanced currency risk management as an integral part of the enterprise risk management system can deliver the best options for the corporate policy and free capital allocation in the money market, thus it can significantly improve the overall corporate efficiency in high-tech enterprises.
Research into the market graph is attracting increasing attention in stock market analysis. One of the important problems connected with the market graph is its identification from observations. The standard way of identifying the market graph is to use a simple procedure based on statistical estimations of Pearson correlations between pairs of stocks. Recently a new class of statistical procedures for market graph identification was introduced and the optimality of these procedures in the Pearson correlation Gaussian network was proved. However, the procedures obtained have a high reliability only for Gaussian multivariate distributions of stock attributes. One of the ways to correct this problem is to consider different networks generated by different measures of pairwise similarity of stocks. A new and promising model in this context is the sign similarity network. In this paper the market graph identification problem in the sign similarity network is reviewed. A new class of statistical procedures for the market graph identification is introduced and the optimality of these procedures is proved. Numerical experiments reveal an essential difference in the quality between optimal procedures in sign similarity and Pearson correlation networks. In particular, it is observed that the quality of the optimal identification procedure in the sign similarity network is not sensitive to the assumptions on the distribution of stock attributes.
The algorithm for finding the required equity capital for the insurance company is the basis of the Solvency II Directive of the European Union. Russia's accession to the WTO and the increase in individual companies of the financial stability and solvency at the expense of investment deals shows the relevance of the study. The purpose is to identify the degree of compliance with the requirements of Solvency II of the financial stability and solvency of Russian insurance company. To do this: first, describe the methodology of the Directive with a focus on the main characteristics and problems associated with the use in the Russian context; secondly, to justify the choice of a Russian insurance company; third, to apply the described method to the selected company.
The originality of this article is to assess risks on the example of Russian insurer with the application of the Solvency II methodology used for European insurers. The result can be considered, the resolution of disputes about the appropriateness of applying the requirements of the algorithm Solvency II to Russian insurance companies, their competitiveness, and the need for the use of risk management in insurance companies of Russia.
The paper aims at finding the most accurate VaR model for the four most liquid Russian stocks. Among the possible VaR modeling techniques, the estimates considered in this work are based on GARCH models with six different distributions. A back testing analysis is performed to evaluate the accuracy of the alternative models and to find the worst predictable period in terms of the volatility behavior.
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.