Текущая экономическая нестабильность и волатильность финансовых рынков усилили необходимость использования эффективных методов количественной оценки кредитного риска в банковских учреждениях. Традиционные подходы к управлению кредитными рисками не всегда могут адекватно учесть неопределенности и случайные отклонения, влияющие на финансовые показатели компаний и вероятность дефолта заемщиков. В данном исследовании рассматривалась возможность применения методов стохастического анализа для улучшения оценки кредитных рисков в банковском секторе. В рамках исследования использовались стохастические дифференциальные уравнения и модель Блэка-Коулса для оценки изменения стоимости активов заемщиков во времени. Стохастическая природа финансовых показателей была смоделирована с использованием процессов Ито, что позволило учесть случайные колебания внешней экономической среды. Для оценки вероятности дефолта применялись методы Монте-Карло, которые позволили провести количественный анализ рисков при различных сценариях развития событий. Анализ показал, что использование стохастических моделей позволяет значительно повысить точность прогнозов по сравнению с традиционными детерминированными методами. Модели выявили более высокий уровень риска для ряда заемщиков, что дало возможность банковским учреждениям скорректировать свои кредитные портфели и улучшить защиту от потенциальных потерь. Алгоритмы Монте-Карло также продемонстрировали свою эффективность в оценке вероятностной природы кредитных событий. Применение методов стохастического анализа в управлении кредитными рисками может значительно улучшить прогнозную достоверность и гибкость оценки. Это особенно важно в условиях неопределенной экономической среды. В дальнейшем планируется внедрение этих подходов в операционные процессы крупных банков для улучшения качества управления рисками.
The current economic instability and volatility of financial markets have increased the need to use effective methods for quantifying credit risk in banking institutions. Traditional approaches to credit risk management cannot always adequately take into account the uncertainties and random deviations that affect the financial performance of companies and the likelihood of borrowers defaulting. This study considered the possibility of using stochastic analysis methods to improve the assessment of credit risks in the banking sector. The study used stochastic differential equations and the Black-Coles model to estimate changes in the value of borrowers' assets over time. The stochastic nature of financial indicators was modeled using Ito processes, which made it possible to take into account random fluctuations in the external economic environment. Monte Carlo methods were used to assess the probability of default, which made it possible to conduct a quantitative risk analysis under various scenarios. The analysis showed that the use of stochastic models can significantly improve the accuracy of forecasts compared to traditional deterministic methods. The models revealed a higher level of risk for a number of borrowers, which made it possible for banking institutions to adjust their loan portfolios and improve protection against potential losses. Monte Carlo algorithms have also demonstrated their effectiveness in assessing the probabilistic nature of credit events. The use of stochastic analysis methods in credit risk management can significantly improve the predictive reliability and flexibility of the assessment. This is especially important in an uncertain economic environment. In the future, it is planned to introduce these approaches into the operational processes of large banks to improve the quality of risk management.
The current economic instability and volatility of financial markets have increased the need to use effective methods for quantifying credit risk in banking institutions. Traditional approaches to credit risk management cannot always adequately take into account the uncertainties and random deviations that affect the financial performance of companies and the likelihood of borrowers defaulting. This study considered the possibility of using stochastic analysis methods to improve the assessment of credit risks in the banking sector. The study used stochastic differential equations and the Black-Coles model to estimate changes in the value of borrowers' assets over time. The stochastic nature of financial indicators was modeled using Ito processes, which made it possible to take into account random fluctuations in the external economic environment. Monte Carlo methods were used to assess the probability of default, which made it possible to conduct a quantitative risk analysis under various scenarios. The analysis showed that the use of stochastic models can significantly improve the accuracy of forecasts compared to traditional deterministic methods. The models revealed a higher level of risk for a number of borrowers, which made it possible for banking institutions to adjust their loan portfolios and improve protection against potential losses. Monte Carlo algorithms have also demonstrated their effectiveness in assessing the probabilistic nature of credit events. The use of stochastic analysis methods in credit risk management can significantly improve the predictive reliability and flexibility of the assessment. This is especially important in an uncertain economic environment. In the future, it is planned to introduce these approaches into the operational processes of large banks to improve the quality of risk management.