A0694
Title: Deep learning for multivariate volatility forecasting in high-dimensional financial time series
Authors: Yasumasa Matsuda - Tohoku University (Japan) [presenting]
Rei Iwafuchi - Tohoku University (Japan)
Abstract: Volatility modeling is considered for high-dimensional financial time series by a long short-term memory (LSTM) neural network. We apply a deep LSTM neural network to describe multivariate volatility dynamic behaviours in financial time series. We discuss the empirical features of the LSTM modeling for the SP500 return series in comparison with those of popular existing models in terms of volatility forecasting performances.
