Luo, Yuanyuan (2023) Comparing recurrent neural network with GARCH model on forecasting volatility based on SSE 50ETF. In: Dai, Wanyang and Jin, Shi, (eds.) Second International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022. Proceedings of SPIE - The International Society for Optical Engineering. Society of Photo-optical Instrumentation Engineers. ISBN 9781510663183
Full text not available from this repository.Abstract
This paper compares the performance of DL models (RNN, LSTM and GRU) and GARCH model in three different train set, which with different time span. The data of empirical analysis is SSE 50ETF from 4th May 2010 to 26th Aug 2022. And the performance is compared with realized volatility. The result shows that SSE 50ETF is more relying on long historical information and pay less attention to new information. And in long periods, the DL models has better performance. However, the stability of DL models are significantly worse than GARCH model. The performance of DL models are highly relying on the selection of a train set.
| Item Type: | Book Section |
|---|---|
| Additional Information: | © 2023 SPIE. |
| Divisions: | Statistics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics H Social Sciences > HA Statistics |
| Date Deposited: | 02 Jun 2023 15:54 |
| Last Modified: | 11 Dec 2024 18:09 |
| URI: | http://eprints.lse.ac.uk/id/eprint/119330 |
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