CBOE Volatility Index Forecasting under COVID-19:An Integrated BiLSTM-ARIMA-GARCH Model  

作　　者：Min Hyung Park Dongyan Nan Yerin Kim Jang Hyun Kim 

机构地区：[1]Department of Applied Artificial Intelligence,Sungkyunkwan University,Seoul,03063,Korea [2]Department of Human-Artificial Intelligence Interaction,Sungkyunkwan University,Seoul,03063,Korea [3]Department of Interaction Science,Sungkyunkwan University,Seoul,03063,Korea

出　　处：《Computer Systems Science & Engineering》2023年第10期121-134,共14页计算机系统科学与工程（英文）

基　　金：This work was supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(NRF-2020R1A2C1014957).

摘　　要：After the outbreak of COVID-19,the global economy entered a deep freeze.This observation is supported by the Volatility Index(VIX),which reflects the market risk expected by investors.In the current study,we predicted the VIX using variables obtained fromthe sentiment analysis of data on Twitter posts related to the keyword“COVID-19,”using a model integrating the bidirectional long-term memory(BiLSTM),autoregressive integrated moving average(ARIMA)algorithm,and generalized autoregressive conditional heteroskedasticity(GARCH)model.The Linguistic Inquiry and Word Count(LIWC)program and Valence Aware Dictionary for Sentiment Reasoning(VADER)model were utilized as sentiment analysis methods.The results revealed that during COVID-19,the proposed integrated model,which trained both the Twitter sentiment values and historical VIX values,presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.

关 键 词：Forecasting VIX sentiment analysis COVID-19 ARIMA GARCH bidirectional LSTM 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] R563.1[医药卫生—呼吸系统]