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"A Statistical Recurrent Stochastic Volatility Model for Stock Markets" by Trong Nghia Nguyen, Minh Ngoc Tran et al.

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 us ....

Hong Kong , Deep Learning , Financial Econometrics , Recurrent Neural Networks , Volatility Modeling ,

Study Findings on Risk Management Reported by Researchers at Nicolaus Copernicus University in Torun (Volatility Modeling and Dependence Structure of ESG and Conventional Investments): Insurance - Risk Management

2022 FEB 03 By a News Reporter-Staff News Editor at Insurance Daily News Investigators publish new report on insurance. According to news originating from Torun, Poland, by NewsRx editors, the research stated,“ The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has. ....

Kujawsko Pomorskie , Katarzyna Kuziak , Joanna Gorka , European Banking Authority , Department Of Econometrics , Nicolaus Copernicus University In Torun , Insurance Daily , Reporter Staff News Editor , Copernicus University , Volatility Modeling , Dependence Structure ,