School of Finance, China Academy of Financial Research, Southwestern University of Finance and Economics, Chengdu, China.
PLoS One. 2024 Oct 18;19(10):e0308967. doi: 10.1371/journal.pone.0308967. eCollection 2024.
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 interpretability for all four indices but also captures the complex dynamics of time series volatility that the RealGARCH model cannot capture, such as long-term dependence and nonlinearity. The results of out-of-sample volatility prediction show that the RealRECH model better predicts the volatility of the CSI500 and CSI1000 indices but yields worse predictions for the SSE50 and CSI300 indices. Thus, the RealRECH model can be used for CSI500 and CSI1000 prediction.
实现的递归条件异方差性（RealRECH）模型通过将长短期记忆（LSTM），一种递归神经网络单元，集成到实现的广义自回归条件异方差性（RealGARCH）模型中，提高了波动性预测能力。然而，目前还没有关于 RealRECH 模型在拟合和预测中国市场波动性方面的能力的文献。在本文中，研究了 RealRECH 模型对中国市场的 SSE50、CSI300、CSI500 和 CSI1000 指数的样本内解释能力和样本外预测能力，以确定其是否优于 RealGARCH 模型。样本内分析的结果表明，RealRECH 模型不仅为所有四个指数提供了更好的样本内可解释性，而且还捕捉到了 RealGARCH 模型无法捕捉的时间序列波动性的复杂动态，例如长期依赖和非线性。样本外波动性预测的结果表明，RealRECH 模型对 CSI500 和 CSI1000 指数的波动性预测效果更好，但对 SSE50 和 CSI300 指数的预测效果较差。因此，RealRECH 模型可用于 CSI500 和 CSI1000 的预测。
