Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model

Bolin Lei, Boyu Zhang and Yuping Song
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Bolin Lei: School of Business and Finance, Shanghai Normal University, Shanghai 200234, China
Boyu Zhang: The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
Yuping Song: School of Business and Finance, Shanghai Normal University, Shanghai 200234, China

Mathematics, 2021, vol. 9, issue 4, 1-17

Abstract: The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.

Keywords: High-frequency Financial Data; Deep Learning Model; Baidu Search Index; Realized Volatility; Investor Attention (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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