О методах количественного анализа финансовых показателей компании в условиях высокой рискованности инвестиций

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В работе исследованы методы количественного анализа скрытых статистических связей финансовых показателей компаний в условиях высокой рискованности инвестирования. Предложен новый полупараметрический метод оценивания показателей хвостовой зависимости с использованием моделей структур зависимости BB1 и BB7. Для набора данных, содержащих стоимостные показатели ведущих российских компаний, проведены компьютерные эксперименты, в результате которых показано, что предложенный метод обладает более высокой устойчивостью и точностью по сравнению с другими рассмотренными методами. Практическое применение представленного метода управления рисками позволило бы финансовым компаниям адекватно оценивать инвестиционные риски в условиях наступления экстремальных событий.

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1. Introduction The global changes currently taking place in the world financial markets caused by the global pandemic of the coronavirus COVID-19, along with a catastrophic decline in oil prices, will obviously entail the need for serious changes in the business structure of both individual companies and entire industries, regional economies and countries. In the Russian conditions of high volatility of financial markets, a successful solution to this problem is impossible without the application of an analysis of the company’s resistance to the effects of the external environment, the implementation of constant monitoring of the behavior of a number of indicators of the enterprise profitability. In particular, trigger analysis [1] is one of such effective methods of analysis. The use of trigger analysis to study the sensitivity of a business structure to the impact of disturbing factors is important, since it allows identifying in advance its most weakly protected, most risky business lines, the socalled trigger points. Examples of these are individual lines of business of a company, credit and debt obligations, etc., precisely those areas of activity that potentially pose a threat of an emergency in the company. Therefore, in order to weaken © Shchetinin E. Yu., 2020 This work is licensed under a Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ their influence on the structure as a whole, and on its individual lines, we proposed instead of the trigger point in its vicinity on the scatter diagram to form an aggregate set with the statistical properties of the meta-elliptic type dependence structure [2]. This allows weakening to certain limits the impact of the statistical dependence of the rest of the business lines on the problematic one and redistributing the aggregate damage to the rest of the business structure lines. In addition, the transition from an extreme dependence to a meta-elliptic one simplifies the calculation of structural risk indicators by the usual summation of the marginal risks for this structure. Examples of such an approach to managing extreme risks are debt-restructuring, transfer of fixed assets to subsidiaries and offshore companies, insurance, hedging (issuance of credit derivatives), limiting and securitization operations in the banking sector, etc. This paper considers effective methods for analyzing the financial performance of companies in the face of increased volatility in stock markets associated with the global COVID-19 pandemic, as well as declining oil prices. Using the example of analyzing the value of shares of leading Russian companies, the advantages of their application are shown in comparison with the classical multivariate analysis using a Gaussian distribution. 2. Methods for the quantitative analysis of structure indicators of statistical dependences Let (

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Об авторах

Е. Ю. Щетинин

Финансовый университет при Правительстве Российской Федерации

Автор, ответственный за переписку.
Email: riviera-molto@mail.ru

Doctor of Physical and Mathematical Sciences, lecturer of Department of Data Analysis, Decision Making and Financial Technologies

Ленинградский проспект, д. 49, Москва, 125993, Россия

Список литературы

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  2. E. Y. Shchetinin, K. M. Nazarenko, and A. V. Paramonov, “Instrumental methods of stochastic analysis of extreme events [Instrumental’nye metody stohasticheskogo analiza ekstremal’nyh sobytij],” Vestnik NNGU, Matematicheskoye modelirovaniye i optimal’noye upravleniye, vol. 2, no. 29, pp. 56-63, 2004, In Russian.
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  7. S. Resnick, Extreme values, regular variation and point processes. Berlin: Springer, 1987.
  8. E. Y. Shchetinin, “Vine copulas structures modeling on Russian stock market,” Discrete and Continuous Models and Applied Computational Science, vol. 27, no. 4, pp. 343-354, 2019. doi: 10.22363/2658-4670- 2019-27-4-343-354.
  9. E. Y. Shchetinin, “Modeling of D-branching structures in the Russian stock market [Modelirovanie struktur D-vetvlenij na rossijskom fondovom rynke],” Vestnik komp’yuternykh i informatsionnykh tekhnologiy, vol. 8, no. 182, pp. 38-45, 2019, In Russian.
  10. C. Genest, K. Ghoudi, and L. Rivest, “A semiparametric estimation procedure of dependence parameters in multivariate families of distributions,” Biometrika, vol. 82, no. 3, pp. 543-552, 1995. DOI: 10.1093/ biomet/82.3.543.

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© Щетинин Е.Ю., 2020

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Эта статья доступна по лицензии Creative Commons Attribution 4.0 International License.