GARCH Model

GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are statistical tools used to model and forecast volatility in time series data, particularly financial markets. Current research focuses on improving GARCH's predictive accuracy through hybrid approaches combining it with deep learning architectures like LSTMs, GRUs, and Temporal Fusion Transformers, as well as exploring Bayesian inference methods for parameter estimation. These advancements aim to enhance risk management, portfolio optimization, and option pricing by providing more accurate and robust volatility forecasts, addressing limitations of traditional GARCH models in capturing complex market dynamics.