Algorithm-Driven Perspectives on Stochastic Dynamic Modeling and Financial Risk Management

With the rapid development and increasing complexity of global financial markets, market volatility has become highly unpredictable, posing significant challenges to traditional risk management methods. Stochastic dynamic modelling has emerged as a critical tool for financial risk management due to its theoretical rigor and practical applicability. This study systematically reviews the development and application of stochastic dynamic modelling in financial risk management. By analyzing classical models such as the Black-Scholes model and the Mean-Variance Optimization model, the study identifies their theoretical foundations and limitations in addressing modern market complexities, including dynamic volatility, nonlinear behaviors, and high-dimensional data. Recent advancements in algorithmic technologies, such as machine learning and big data, have driven innovations in stochastic dynamic modelling, with techniques like Long Short-Term Memory Networks (LSTMs), Bayesian Dynamic Linear Models (BDLM), and Generative Adversarial Networks (GANs) enhancing predictive accuracy and real-time adaptability. However, these innovations also introduce new challenges, including increased model complexity, dependence on large datasets, and the risk of overfitting. Future research should focus on hybrid models that integrate classical stochastic processes with modern algorithmic techniques, balancing theoretical rigor with enhanced adaptability to address the evolving demands of financial markets.