Prediction on Shanghai Composite Index Volatility with Day-of-week Effect, Volume and Turnover Based on HAR Model

DOI

10.2991/assehr.k.211209.310How to use a DOI?

Keywords

Shanghai Composite Index; HAR-type model; Volatility prediction

Abstract

The Shanghai Composite Index is the earliest and one of the most important indexes in Chinese stock market, which is calculated by capitalization market weighted average for all the stocks listed on Shanghai Stock Exchange. However, previous researches mainly use low- frequency-data-based GARCH-type model to predict the volatility of the Shanghai Composite Index without considering the day-of-week effects and the impacts of volume and turnover. In this paper, the HAR-RV model is established primarily based on heterogeneous autoregressive (HAR) theory and five-minute middle-frequency data. Then, trading volume, turnover and day-of-week effects are taken into consideration, respectively, i.e., the HAR-RV-VT model and HAR-RV-W model are constructed. Finally, a mixed HAR-RV-VT-W model is obtained by using the above three factors simultaneously. According to the result, the day-of-week effect and turnover have significant negative impacts on volatility of Shanghai Composite Index while volume has a positive influence. In general, more useful information will be provided based on our mixed model combining volume, turnover and day-of-week effect, which pave a better path to predict the volatility of Shanghai Composite Index.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Yingfa Zhang
PY  - 2021
DA  - 2021/12/15
TI  - Prediction on Shanghai Composite Index Volatility with Day-of-week Effect, Volume and Turnover Based on HAR Model
BT  - Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)
PB  - Atlantis Press
SP  - 1903
EP  - 1910
SN  - 2352-5428
UR  - https://doi.org/10.2991/assehr.k.211209.310
DO  - 10.2991/assehr.k.211209.310
ID  - Zhang2021
ER  -