Forecasting realized volatility through financial turbulence and neural networks

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-04-01 DOI:10.18559/ebr.2023.2.737

Hugo Gobato Souto, A. Moradi

引用次数: 3

Abstract

Abstract This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.

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预测通过金融动荡和神经网络实现波动

摘要本文介绍并研究了一种利用长短期记忆(LSTM)神经网络和风险度量金融动荡(FT)的新实现的波动率预测模型。将该模型与五种替代模型进行了比较，其中两种模型采用LSTM神经网络，其余三种模型包括GARCH(1,1)、EGARCH(1,1)和HAR模型。本文的结果表明，当应用于中高波动率的股票时，所提出的模型比所有其他已研究的模型具有统计上显著的准确性和稳健性。然而，考虑到低波动性股票，只能自信地肯定，相对于所有其他考虑的模型，所提出的模型在统计上产生更稳健的预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.