This study explores the application of stochastic matrix theory in financial risk management, focusing on its effectiveness in constructing risk prediction models and asset portfolio optimization. By introducing stochastic matrix theory, we improve the accuracy and robustness of risk models, especially when dealing with big data and complex financial market structures. It is found that the theory not only helps to estimate the covariance matrix of asset returns more accurately, but also effectively identifies and cuts down systematic risks. The methodology of this paper provides a new mathematical tool for financial risk management and helps financial institutions to better understand and control risks.

Yufei Jia. A study of mathematical modeling of stochastic matrix theory in financial risk management. Academic Journal of Mathematical Sciences (2024) Vol. 5, Issue 2: 41-44. https://doi.org/10.25236/AJMS.2024.050207.

[1] Wang Bicheng, Xia Qiu. Prevention and Resolution of Systematic Financial Risks and Suggestions for Commercial Banks [J]. Financial Theory Exploration, 2023, (02): 35-41. DOI: 10.16620/j.cnki. jrjy. 2023.02.004

[2] He Liang. Financial Risk Management of the Stock Market of the Exchange [J]. Modern Commerce, 2023, (05): 113-116. DOI: 10.14097/j.cnki.5392/2023.05.029

[3] Yu Yani, Xu Yuan. Research on Systematic Financial Risk Characteristics and Management Measures [J]. Western Finance, 2021, (07): 93-97. DOI: 10.16395/j.cnki.61-1462/f.2021.07.017

[4] Huang Chunmin, Liu Guirong. Sino US trade frictions and systematic risk of manufacturing stocks: an empirical study based on stochastic matrix theory [J]. North China Finance, 2022, (02): 53-65

[5] Han Hua, Wu Lingyan, Song Ningning. Financial network model based on random matrix [J]. Journal of Physics, 2014, 63 (13): 439-448