A Statistical Recurrent Stochastic Volatility Model for Stock Markets. Issue 2 (3rd April 2023)

• Record Type: Journal Article
• Authors: Nguyen, Trong-Nghia; Tran, Minh-Ngoc; Gunawan, David; Kohn, Robert
• Is Part Of: Journal of business & economic statistics. Volume 41:Issue 2(2023)
• Page Start: 414
• Page End: 428
• Publication Date: 2023-04-03
• Subjects: Deep learning -- Financial econometrics -- Recurrent neural networks -- Volatility modeling; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2022.2028631
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract: The stochastic volatility (SV) model and its variants are widely used in the financial sector, while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of deep learning. We combine these two methods in a nontrivial way and propose a model, which we call the statistical recurrent stochastic volatility (SR-SV) model, to capture the dynamics of stochastic volatility. The proposed model is able to capture complex volatility effects, for example, nonlinearity and long-memory auto-dependence, overlooked by the conventional SV models, is statistically interpretable and has an impressive out-of-sample forecast performance. These properties are carefully discussed and illustrated through extensive simulation studies and applications to five international stock index datasets: the German stock index DAX30, the Hong Kong stock index HSI50, the France market index CAC40, the U.S. stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the article are available at https://github.com/vbayeslab .