预测中国股票指数的波动性,基于实现的循环条件异构性
Gongtao Zhang1, Huanyu Zhao1, Rujie Fan1
1School of Finance, China Academy of Financial Research, Southwestern University of Finance and Economics, Chengdu, China.
PloS one
|October 18, 2024
概括
整合LSTM与RealGARCH的RealRECH模型显示,中国股票指数的样本适应性得到改善. 它可以更好地预测CSI500和CSI1000的波动性,但不能预测SSE50和CSI300.
科学领域:
- 金融计量经济学 金融计量经济学
- 机器学习在金融中的应用.
- 时间序列分析时间序列分析.
背景情况:
- 通过将长短期内存 (LSTM) 纳入RealGARCH模型,RealRECH模型提高了波动性预测.
- 现有的文献缺乏关于RealRECH模型在中国金融市场的表现的研究.
研究的目的:
- 评估RealRECH模型对中国股票指数 (SSE50,CSI300,CSI500,CSI1000) 的样本内可解释性和样本外预测准确性.
- 为了比较RealRECH模型与RealGARCH模型在中国市场的性能.
主要方法:
- 将RealRECH模型应用于SSE50,CSI300,CSI500和CSI1000指数的应用.
- 在样本中的适合性分析.
- 在样本之外的波动性预测评估.
主要成果:
- 在所有四个指数中,RealRECH模型表现出优异的样本内可解释性.
- RealRECH捕获了复杂的波动动力学,包括长期依赖性和非线性,而RealGARCH错过了这一点.
- 样本之外的结果表明,RealRECH在CSI500和CSI1000方面表现优于RealGARCH,但在SSE50和CSI300方面表现不佳.
结论:
- 该RealRECH模型提供了增强的样本内匹配,并捕捉了复杂的波动动态.
- 该模型的预测性能在中国股票指数之间有所不同,对于CSI500和CSI1000波动性预测是有效的.
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