Financial Market Volatility Forecasting and Volatility Adjustment Algorithm Combined with Time Series Analysis

Abstract

A robust financial market volatility forecasting and adjustment algorithm, leveraging time series analysis techniques, offers a comprehensive approach to anticipate and adapt to market fluctuations. By integrating sophisticated modeling with data, it enhances risk management strategies and decision-making processes for investors and financial institutions. One drawback of combining financial market volatility forecasting with time series analysis is the potential for inaccuracies in predictions due to the assumption of stationary data, which may not hold in rapidly changing market conditions. In this manuscript Financial Market Volatility Forecasting and Volatility Adjustment Algorithm Combined with Time Series Analysis (FMVF-VAAC-TSA-PGCN-SINN) is proposed. Initially, data are collected from mainland China given that financial data and info as Bloomberg. The data are fed to feature extraction; sequence features like Volume, night, bias, pctChg, money are extracted based on Quadratic Phase S-Transform (QPST).Finally the extracted features are fed to Hybrid Progressive Graph Convolutional Networks with Statistics-Informed Neural Network (PGCN-SINN) for Financial Market Volatility Forecasting. In General, Hybrid PGCN-SINN does not precise adjusting optimization schemes to define optimal parameters to certify correct Financial Market Volatility Forecasting. Hence, Stock Exchange Trading Optimization Algorithm (SETOA) is to optimize to Hybrid Progressive Graph Convolutional Networks with Statistics-Informed Neural Network (PGCN-SINN) which accurately Financial Market Volatility Forecasting. The proposed technique implemented in python and efficacy of FMVF-VAAC-TSA-PGCN-SINN technique is assessed with support of numerous performances like accuracy, MAE, Mean Square Error(MSE), RMSE, mean absolute percentage error (MAPE), mean squared logarithmic error (MSLE), also symmetric mean absolute percentage error (SMAPE)is analysed. Proposed FMVF-VAAC-TSA-PGCN-SINN method attain 30.53%, 23.34%, and 32.64% higher  Accuracy ; 29.43%, 21.30%, and 31.63% low Mean Absolute Percentage Error and 39.57%, 25.30%, and 33.68% low Root mean square error analysed with the existing for Financial Market Volatility Forecasting and Volatility Adjustment Algorithm Combined with Time Series Analysis using Hybrid Progressive Graph Convolutional Networks with Statistics-Informed Neural Network. Then, performance of FMVF-VAAC-TSA-PGCN-SINN technique is analysed with existing methods, such as Instability estimating for stock market guide depend on difficult system and cross DL method (VF-SMI-CNN), Ensure artificial neural networks (NN) deliver better volatility predictions: Evidence since Asian markets (IVF-EAM-ANN), and Research arranged Graph Neural Network in Stock Market (SM-RGNN)respectively.