A0388
Title: Realized recurrent conditional heteroskedasticity model for volatility modelling
Authors: Chao Wang - The University of Sydney (Australia) [presenting]
Minh-Ngoc Tran - University of Sydney (Australia)
Robert Kohn - University of New South Wales (Australia)
Abstract: A new approach to volatility modelling by combining deep learning (LSTM) is proposed and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distils modelling 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, and 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, and volatility forecasting, to tail risk forecasting and option pricing. A comprehensive empirical study is reported on using 31 widely traded stock indices over a time period that includes the COVID-19 pandemic.
