Deep Learning Enhanced Realized GARCH
This provides improved volatility forecasting for financial markets, though it is incremental as it hybridizes existing methods.
The authors tackled volatility modeling by combining deep learning (LSTM) with realized volatility measures in a GARCH framework, achieving superior predictive performance compared to benchmarks across 31 stock indices, including during the COVID-19 pandemic.
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distills modeling advances from financial econometrics, high frequency trading data and deep learning. Bayesian inference via the Sequential Monte Carlo method is employed for statistical inference and forecasting. The new framework can jointly model the returns and realized volatility measures, has an excellent in-sample fit and superior predictive performance compared to several benchmark models, while being able to adapt well to the stylized facts in volatility. The performance of the new framework is tested using a wide range of metrics, from marginal likelihood, volatility forecasting, to tail risk forecasting and option pricing. We report on a comprehensive empirical study using 31 widely traded stock indices over a time period that includes COVID-19 pandemic.