financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2025-29-2-6-19finance-2851Research ArticleФИНАНСОВЫЕ РИСКИFINANCIAL RISKSМоделирование рисков и взаимосвязь глобального и индустриального рынка финтех‑акций США: данные о влиянии кризиса COVID‑19Risk Modeling and Connectedness Across Global and Industrial US Fintech Stock Market: Evidence from the COVID‑19 Crisishttps://orcid.org/0000-0002-5660-9320ГарбиУ.GharbiO.

Умайма Гарби — PhD в сфере финансов, Факультет экономики и менеджмента Сфакса, Лаборатория научной и творческой деятельности

Сфакс

Oumayma Gharbi — PhD in Finance, Faculty of Economics and Management of Sfax, Laboratory URECA

Sfax

oumayma.gharbii@gmail.comhttps://orcid.org/0000-0002-2616-3794БужельбенМ.BoujelbèneM.

Муна Бужельбен — PhD, профессор финансов, Факультет экономики и менеджмента в Сфаксе, Лаборатория научной и творческой деятельности

Сфакс

Mouna Boujelbène — PhD, Prof. of Finance, Faculty of Economics and Management of Sfax, Laboratory URECA

Sfax

abbes.mouna@gmail.comhttps://orcid.org/0000-0002-2634-5280ЗуариР.ZouariR.

Рамзи Зуари — PhD инженерных наук, Национальная школа инженеров

Сфакс

Ramzi Zouari — PhD in Engineering, National School of Engineers

Sfax

ramzi.zouari@gmail.comУниверситет СфаксаТунисUniversity of SfaxTunisia202509052025292619Copyright © Гарби У., Бужельбен М., Зуари Р., 20252025Гарби У., Бужельбен М., Зуари Р.Gharbi O., Boujelbène M., Zouari R.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/2851

Основной целью данной работы является проверка эффективности моделей GARCH при оценке и прогнозировании VaR (стоимости с учетом риска) американского рынка финтех-акций с 20 июля 2016 г. по 31 декабря 2021 г. Кроме того, в данном исследовании изучается влияние COVID-19 на перераспределение рисков между адекватными рядами VaR глобального американского индекса KFTX и пяти финтех-индустрий. В частности, мы сравниваем различные оценки VaR (862 ежедневной прибыли внутри выборки) и прогнозы (550 ежедневной прибыли вне выборки) нескольких спецификаций GARCH-модели при нормальном и Student-t распределении с 1%-ной и 5%-ной значимостью. Результаты бэктестинга показывают, что I-GARCH с распределением Student-t является хорошей моделью для оценки и прогнозирования VaR американского рынка финтех-акций до и во время COVID-19. Кроме того, общие результаты связи свидетельствуют о том, что глобальная и каждая финтех-индустрия значительно расширяется в условиях турбулентного рынка. Учитывая эти соображения, данная работа дает возможность политикам и регуляторам лучше понять риски в финтех-индустрии, не препятствуя инновациям.

The main purpose of this paper is to test the performance of GARCH models in estimating and forecasting VaR (value at risk) of the US Fintech stock market from July 20, 2016, to December 31, 2021. In addition, this study examines the impact of COVID-19 on the risk spillover between the adequate VaR series of the US global KFTX index and the five Fintech industries. Specifically, we compare different VaR estimates (862 in-sample daily returns) and predictions (550 out-of-sample daily returns) of several GARCH model specifications under a normal and Student-t distribution with 1% and 5% significance. The Backtesting results indicate that I-GARCH with Student-t distribution is a good model for estimating and forecasting VaR of the US Fintech stock market before and during COVID-19. Moreover, the total connectedness results suggest that global and each Fintech industry increases significantly under turbulent market conditions. Given these considerations, this paper provides policymakers and regulators with a better understanding of risk in the Fintech industry without inhibiting innovation.

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The authors declare that there are no conflicts of interest present.