Predicting the volatility of Chinese stock indices based on realized recurrent conditional heteroskedasticity.

ABSTRACT: The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedasticity (RealGARCH) model. However, at present, there is no literature on the ability of the RealRECH model to fit and predict volatility in the Chinese market. In this paper, a study is conducted to test the in-sample explainability and out-of-sample prediction ability of the RealRECH model for the SSE50, CSI300, CSI500 and CSI1000 indices in the Chinese market and to determine whether it performs better than the RealGARCH model. The results of the in-sample analysis show that the RealRECH model not only provides better in-sample interpretabili

SUBMITTER: Zhang G 

PROVIDER: S-EPMC11488709 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature