Research on the Volatility Characteristics of Shanghai Stock Market Based on ARCH Model Family

Authors

  • Jie Wang

DOI:

https://doi.org/10.54097/fbem.v4i2.634

Keywords:

Shanghai stock market, ARCH model family, Volatility, Information shock curve.

Abstract

 In order to study the volatility characteristics of the Shanghai stock market, on the basis of reviewing the relevant literature, based on the ARCH model family, this paper selects 5797 data from the daily closing price of the Shanghai Stock Exchange on the Shanghai Stock Exchange from December 27, 1996 to November 30, 2020 to empirically analyze the volatility characteristics of the Shanghai stock market.

Downloads

Download data is not yet available.

References

Tang Qiming, Chen Jian. ARCH effect analysis of Chinese stock market [J]. World Economy, 2001(03):29-36.

Li Yajing, Zhu Hongquan, Peng Yuwei. Prediction of China's stock market volatility based on GARCH model family [J]. Practice and Understanding of Mathematics, 2003(11):65-71.

Yan Dingqi, Li Yufeng. Prediction of CSI 300 Index Volatility Based on GARCH Family Models [J]. Journal of Lanzhou Jiaotong University, 2008(01):92-95.

Luo Yang, Yang Guiyuan. Research on Shanghai Stock Market Volatility Based on GARCH Model [J]. Statistics and Decision, 2013(12):162-165.

Wu Qianwen. An Empirical Analysis of the Return on the Shanghai Stock Exchange Index: Based on the ARCH Family Model [J]. Regional Finance Research, 2014(04):42-48.

Zheng Tingguo, Shang Yuhuang. Measurement and prediction of stock market volatility based on macro fundamentals [J]. World Economy, 2014, 37(12): 118-139.

Zhu Jing, Zhang Jing, Fu Yunpeng. Measurement and Empirical Analysis of Stock Index VaR Based on Asymmetric GARCH Model [J]. Market Weekly (Theoretical Research), 2015(09):79-81.

Pagan A R, Schwert G W.Alternative Models for Conditional Stock Market Volatility[J].Journal of Econometrics,1990(1-2) :267-290. DOI: https://doi.org/10.1016/0304-4076(90)90101-X

Danielsson J.Stochastic volatility in asset prices estimation with simulated maximum likelihood[J].J.Econometrics, 1994, 64(1-2):375-400. DOI: https://doi.org/10.1016/0304-4076(94)90070-1

Awartani B, Corradi V.Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries[J].International Journal of Forecasting, 2005,21(1):167-183. DOI: https://doi.org/10.1016/j.ijforecast.2004.08.003

Sabiruzzaman M, Huq M M, Beg R A, et al.Modeling and forecasting trading volume index: GARCH versus TGARCH approach[J].Quarterly Review of Economics & Finance, 2010,50(2):141-145. DOI: https://doi.org/10.1016/j.qref.2009.11.006

Girardin E, Joyeux R.Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach[J]. Economic Modelling, 2013,34:59-68. DOI: https://doi.org/10.1016/j.econmod.2012.12.001

Sharma P, Vipul.Forecasting stock market volatility using Realized GARCH model:International evidence[J].The Quarterly Review of Economics and Finance, 2016,59:222-230. DOI: https://doi.org/10.1016/j.qref.2015.07.005

Downloads

Published

30-06-2022

Issue

Section

Articles

How to Cite

Wang, J. (2022). Research on the Volatility Characteristics of Shanghai Stock Market Based on ARCH Model Family. Frontiers in Business, Economics and Management, 4(2), 43-47. https://doi.org/10.54097/fbem.v4i2.634