GARCH Model Integration

Generalized Autoregressive Conditional Heteroskedasticity models are statistical tools used to estimate and forecast volatility by modeling it as a time-varying process. Integrating GARCH with neural networks allows traders to combine the statistical rigor of traditional econometrics with the pattern recognition capabilities of deep learning.

While GARCH captures the tendency of volatility to cluster ⎊ meaning high volatility periods follow high volatility ⎊ the neural network can identify additional non-linear features that GARCH might miss. This hybrid approach provides a more robust estimate of future volatility, which is essential for pricing options and calculating the Greeks.

It bridges the gap between classical financial theory and modern machine learning techniques.