Algorithm Analysis of VaR for Financial Market Risk Based on Optimized BP Neural Network

Abstract

VaR (Value at Risk) is an important tool for predicting and preventing financial risk, and it is also a widely used method for quantitatively measuring financial risk internationally. Due to the spontaneity and disorderly nature of financial markets, there are many complex and difficult problems in forecasting. The BP neural network analysis method, which is also non-linear, can be used to extract financial information through modelling, thus making financial theory more relevant. However, disadvantages of BP neural networks include excessive arbitrariness, slow convergence rate, local optimality and the need to optimise them in order to improve the accuracy of financial risk prediction. In this paper, the shortcomings of BP neural networks are taken into account and genetic algorithms will be used to optimise their specification. It is also combined with the VaR algorithm model to change the dynamic impact of the BP neural network subjected to random perturbation terms. Finally, a BP neural network financial warning model is established and brought into the current month CSI 300 index for forecasting. The experimental simulation results show that the optimised BP neural network is capable of faster convergence, greater stability and higher accuracy in financial alerting. In this paper, by optimizing the training algorithm of BP neural network, the ability of neural network to fit financial market related data is improved, thereby more accurately predicting market fluctuations. By combining VaR theory with neural network models, a new financial market risk VaR algorithm is established, which has high accuracy and practicality. Based on the above achievements, the financial market risk VaR algorithm based on optimized BP neural network has been widely used in the financial field, providing scientific and reliable algorithm support for financial market transactions and risk management.