Financial Market Volatility Time Series Prediction and Volatility Adjustment Algorithm

Abstract

Time series analysis of financial market volatility involves examining historical price data to understand patterns, identify trends, and predict future fluctuations. This method utilizes statistical techniques and models, such as Autoregressive Conditional Heteroskedasticity (ARCH), Generalized ARCH (GARCH), and their variants, to capture the time-dependent nature of market volatility. Analysts focus on measuring risk, detecting anomalies, and understanding market reactions to external events, such as economic policies or geopolitical crises. This paper introduces a novel approach to financial market forecasting and volatility estimation using Time Series Optimized Deep Learning Forecasting (TSODLF) in Chinese Market. Leveraging the capabilities of deep learning and optimization algorithms, TSODLF offers a comprehensive framework for capturing complex temporal patterns and adapting to changing market conditions. Through a series of experiments and analyses, we demonstrate the effectiveness of TSODLF in accurately predicting future values of financial variables and estimating market volatility. TSODLF demonstrates its effectiveness in accurately predicting future values of financial variables. Through empirical analyses, our model achieves promising results, with mean absolute error (MAE) values ranging from 0.012 to 0.015 and root mean squared error (RMSE) values ranging from 0.018 to 0.022 across different forecasting and volatility estimation tasks. Additionally, TSODLF exhibits strong performance with adjusted R-squared values between 0.78 and 0.85, indicating its ability to explain a significant portion of the variability in the data.