Neural network approach to the problem of predicting interest rate anomalies under the influence of correlated noise

The aim of this study is to analyze bifurcation points in financial models using colored noise as a stochastic component. The research investigates the impact of colored noise on change-points and approach to their detection via neural networks. The paper presents a literature review on the use of colored noise in complex systems. The Vasicek stochastic model of interest rates is the object of the research. The research methodology involves approximating numerical solutions of the model using the Euler–Maruyama method, calibrating model parameters, and adjusting the integration step. Methods for detecting bifurcation points and their application to the data are discussed. The study results include the outcomes of an LSTM model trained to detect change-points for models with different types of noise. Results are provided for comparison with various change-point windows and forecast step sizes.