The Volatility Research in CSI 300 Index Futures by Using High Frequency Data based on GARCH Model

DOI

10.2991/emehss-17.2017.67How to use a DOI?

Keywords

High frequency data, GARCH model, CSI 300 index futures, volatility characteristics

Abstract

By using the high frequency data in CSI 300 index futures, we research the pattern of CSI 300 index futures, and find that the returns of high frequency CSI 300 index futures have obvious volatility clustering effect, and there is peakÿandÿfat-tailed phenomenon. After that, the ARCH effect test is carried out on the residual error of the high frequency data, and the results show that the residuals have obvious ARCH effect. After eliminating the autocorrelation, the optimal GARCH model is established, and the adequacy of the model fit was verified. The fitting results show that GARCH can well describe the characteristics of high frequency volatility of CSI 300 index futures, the impact on the conditional variance, has a strong persistence, long memory effect is found in the volatility

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Junbo Wang
AU  - Susheng Wang
AU  - Yongbo Kang
PY  - 2017/04
DA  - 2017/04
TI  - The Volatility Research in CSI 300 Index Futures by Using High Frequency Data based on GARCH Model
BT  - Proceedings of the 2017 International Conference on Economics and Management, Education, Humanities and Social Sciences (EMEHSS 2017)
PB  - Atlantis Press
SP  - 298
EP  - 301
SN  - 2352-5398
UR  - https://doi.org/10.2991/emehss-17.2017.67
DO  - 10.2991/emehss-17.2017.67
ID  - Wang2017/04
ER  -