Volatility Forecasting

Volatility forecasting, the prediction of price fluctuations in financial markets, aims to improve risk management and investment strategies. Current research heavily focuses on hybrid models combining traditional econometric approaches like GARCH with advanced deep learning architectures such as LSTMs, GRUs, Transformers, and Convolutional Neural Networks, often incorporating high-frequency data and alternative data sources like social media sentiment or macroeconomic indicators. These efforts aim to enhance forecasting accuracy and reliability, particularly for short-term predictions and extreme volatility events, impacting financial modeling, risk assessment, and algorithmic trading.