Deep Learning Enhanced Realized GARCH
- URL: http://arxiv.org/abs/2302.08002v2
- Date: Tue, 17 Oct 2023 09:37:05 GMT
- Title: Deep Learning Enhanced Realized GARCH
- Authors: Chen Liu, Chao Wang, Minh-Ngoc Tran, Robert Kohn
- Abstract summary: 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.
- Score: 6.211385208178938
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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