Application of GARCH Model in the Field of Finance

This paper reviews the fundamental principles, mathematical formulation, advantages, and disadvantages of the GARCH model and its extensive applications in finance. Initially, this paper introduces the mathematical structure and fundamental properties of the GARCH model and analyzes the advantages and disadvantages of the model in practical applications. Furthermore, this paper applies the GARCH model in financial market forecasting, risk management, and asset pricing, illustrating these through specific cases. In particular, the GARCH model is employed to forecast the volatility of the electricity and stock markets, demonstrating its efficacy in handling high volatility and time series data. In risk management, the GARCH model assists investors in assessing potential losses and formulating risk management strategies by predicting future volatility. Additionally, the GARCH model and its variants enhance the precision of asset pricing by considering the skewness and kurtosis of the stock market. Furthermore, the current research frontiers and future development trends are also discussed. Research indicates that future enhancements to the GARCH model will primarily concentrate on capturing nonlinear market characteristics, developing multivariate GARCH models, and incorporating long memory characteristics into the model. As blockchain technology develops and cryptocurrency markets expand, it is anticipated that the GARCH model will become increasingly prevalent in these emerging markets. The integration of machine learning and artificial intelligence will further enhance the precision and reliability of the GARCH model, providing more accurate and robust tools for financial market analysis and forecasting.