A mixture deep neural network GARCH model for volatility forecasting

Wenhui Feng; Yuan Li; Xingfa Zhang; Wenhui Feng; Yuan Li; Xingfa Zhang

doi:10.3934/era.2023194

Electronic Research Archive

2023, Volume 31, Issue 7: 3814-3831. doi: 10.3934/era.2023194

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Research article Special Issues

A mixture deep neural network GARCH model for volatility forecasting

1.
School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
2.
Institute of Applied Mathematics, Shenzhen Polytechnic, Shenzhen 518000, China

Received: 01 March 2023 Revised: 20 April 2023 Accepted: 22 April 2023 Published: 08 May 2023

Recently, deep neural networks have been widely used to solve financial risk modeling and forecasting challenges. Following this hotspot, this paper presents a mixture model for conditional volatility probability forecasting based on the deep autoregressive network and the Gaussian mixture model under the GARCH framework. An efficient algorithm for the model is developed. Both simulation and empirical results show that our model predicts conditional volatilities with smaller errors than the classical GARCH and ANN-GARCH models.
- volatility forecasting,
- deep autoregressive network,
- GARCH model
Citation: Wenhui Feng, Yuan Li, Xingfa Zhang. A mixture deep neural network GARCH model for volatility forecasting[J]. Electronic Research Archive, 2023, 31(7): 3814-3831. doi: 10.3934/era.2023194

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Abstract

Recently, deep neural networks have been widely used to solve financial risk modeling and forecasting challenges. Following this hotspot, this paper presents a mixture model for conditional volatility probability forecasting based on the deep autoregressive network and the Gaussian mixture model under the GARCH framework. An efficient algorithm for the model is developed. Both simulation and empirical results show that our model predicts conditional volatilities with smaller errors than the classical GARCH and ANN-GARCH models.

References

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