Does inclusion of GARCH variance in deep learning models improve financial contagion prediction?

被引:1
|
作者
Rayadurgam, Vikram Chandramouli [1 ]
Mangalagiri, Jayasree [1 ]
机构
[1] GITAM Univ, GITAM Sch Business, Hyderabad, India
关键词
Financial contagion; Hybrid models; Deep learning; LSTM; ANN; CONDITIONAL CORRELATION; VOLATILITY SPILLOVERS; STOCK; CONNECTEDNESS; OIL;
D O I
10.1016/j.frl.2023.103707
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Financial contagions have traditionally been studied using GARCH models to explain the vola-tility, whereas regression models have been used to predict the stock index values based on different underlying assets. Past research has shown that deep learning regression models have demonstrated superiority over traditional statistical models. This paper creates a hybrid predic-tion model by using the outputs of GARCH models as an input variable in deep learning regression models. It has been observed that hybrid models provide better results as compared to deep learning models. Such improved prediction shall provide portfolio managers an additional tool to determine the investment strategy.
引用
收藏
页数:9
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