Discrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational Science2658-46702658-7149Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)2518110.22363/2658-4670-2020-28-4-346-360ArticlesСтатьиResearch ArticleOn methods of quantitative analysis of the company’s financial indicators under conditions of high risk of investmentsО методах количественного анализа финансовых показателей компании в условиях высокой рискованности инвестицийShchetininEugeny Yu.ЩетининЕ. Ю.

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

riviera-molto@mail.ruFinancial University under the Government of Russian FederationФинансовый университет при Правительстве Российской Федерации15122020284VOL 28, NO4 (2020)ТОМ 28, №4 (2020)34636009122020Copyright ©; 2020, Shchetinin E.Y.Copyright ©; 2020, Щетинин Е.Ю.2020Shchetinin E.Y.Щетинин Е.Ю.http://creativecommons.org/licenses/by/4.0https://journals.rudn.ru/miph/article/view/25181

The paper investigates the methods of quantitative analysis of hidden statistical relationships of the financial indicators of companies under conditions of high investment risk. A new semi-parametric method for estimating tail dependence indicators using BB1 and BB7 dependence structures is proposed. For a dataset containing the cost indicators of leading Russian companies, computer experiments were carried out, as a result of which it was shown that the proposed method has a higher stability and accuracy in comparison with other considered methods. Practical application of the proposed risk management method would allow financial companies to assess investment risks adequately in the face of extreme events.

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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