Using Neural Networks to Predict Stock Market Volatility

Abstract

Stock market volatility prediction is crucial for financial institutions, investors, and policymakers to make informed decisions. Neural networks, due to their ability to model complex and non-linear relationships in financial time series data, have become a promising tool for forecasting market volatility. This article explores the application of neural networks, particularly deep learning models, in predicting stock market volatility. It examines the various architectures used in volatility prediction, including feedforward neural networks, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. The article also discusses the challenges, advantages, and potential future directions in the field of volatility prediction using neural networks