Analysis on the Leverage Effect of Stock Market in China and America Based on EARCH Model

Research Article
Open access

Analysis on the Leverage Effect of Stock Market in China and America Based on EARCH Model

Zixiang Zhou 1*
  • 1 Durham University    
  • *corresponding author zixiang1126@163.com
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/8/20230301
AEMPS Vol.8
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-43-0
ISBN (Online): 978-1-915371-44-7

Abstract

Volatility in the stock market as a significant standard is always used in the risk assessment field. Among them, asymmetry is the main feature of stock market volatility, and the leverage effect is one of the important mechanisms of asymmetry. It is of positive practical significance to study the leverage effect of stock market volatility in the open economy. In order to make volatility research more quantitative, economists proposed the ARCH model and continuously modified and innovated it, which expanded to the ARCH family model. This essay selected the 10 years of data (from 2012 to 2022) of the Shanghai Composite Index and the S&P500 Index that represent China's stock market and the American stock market, respectively, based on the EGARCH model, one of the ARCH family models, to analyze their volatility and by using Eview12.0 to build the EGARCH model to assess the leverage effect for both markets and compare the two market differences. As a result, it illustrates that both markets have significant leverage effects and the investors in two markets have obvious "herd behavior". Based on this empirical conclusion, the paper finally puts forward the corresponding policy suggestions for the government and stock investors.

Keywords:

volatility, leverage effect, Shanghai Composite Index, S&P500, EGARCH model, herd behavior

Zhou,Z. (2023). Analysis on the Leverage Effect of Stock Market in China and America Based on EARCH Model. Advances in Economics, Management and Political Sciences,8,165-171.
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References

[1]. Chen Xiu, 2018. Volatility Analysis of Stock Market Price Based on GARCH Model. Advances in Applied Mathematics, 07(06), pp.653-660.

[2]. Zhao, S., Chen, X. and Zhang, J., 2019. The systemic risk of China’s stock market during the crashes in 2008 and 2015. Physica A: Statistical Mechanics and its Applications, 520, pp.161-177.

[3]. Hall, A., 1994. Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection. Journal of Business & Economic Statistics, 12(4), p.461.

[4]. Fu, 2015. The Empirical Study of the Shanghai Composite Index Based on ARCH Model. Advances in Applied Mathematics, 04(02), pp.124-128.

[5]. Bikhchandani, S., 2000. Herd Behavior in Financial Markets: A Review. SSRN Electronic Journal,.

[6]. Yu Liqun, Jia Ke, 2012. An empirical study on the leverage effect of stock market volatility in China's open economy. Times Finance, 481(05).

[7]. Chiang, T. and Zheng, D., 2010. An empirical analysis of herd behavior in global stock markets. Journal of Banking & Finance, 34(8), pp.1911-1921.

[8]. Engelhardt, N., Krause, M., Neukirchen, D. and Posch, P., 2021. Trust and stock market volatility during the COVID-19 crisis. Finance Research Letters, 38, p.101873.

Cite this article

Zhou,Z. (2023). Analysis on the Leverage Effect of Stock Market in China and America Based on EARCH Model. Advances in Economics, Management and Political Sciences,8,165-171.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Business and Policy Studies

ISBN：978-1-915371-43-0(Print) / 978-1-915371-44-7(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.confbps.org/
Conference date: 26 February 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.8
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Chen Xiu, 2018. Volatility Analysis of Stock Market Price Based on GARCH Model. Advances in Applied Mathematics, 07(06), pp.653-660.

[2]. Zhao, S., Chen, X. and Zhang, J., 2019. The systemic risk of China’s stock market during the crashes in 2008 and 2015. Physica A: Statistical Mechanics and its Applications, 520, pp.161-177.

[3]. Hall, A., 1994. Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection. Journal of Business & Economic Statistics, 12(4), p.461.

[4]. Fu, 2015. The Empirical Study of the Shanghai Composite Index Based on ARCH Model. Advances in Applied Mathematics, 04(02), pp.124-128.

[5]. Bikhchandani, S., 2000. Herd Behavior in Financial Markets: A Review. SSRN Electronic Journal,.

[6]. Yu Liqun, Jia Ke, 2012. An empirical study on the leverage effect of stock market volatility in China's open economy. Times Finance, 481(05).

[7]. Chiang, T. and Zheng, D., 2010. An empirical analysis of herd behavior in global stock markets. Journal of Banking & Finance, 34(8), pp.1911-1921.

[8]. Engelhardt, N., Krause, M., Neukirchen, D. and Posch, P., 2021. Trust and stock market volatility during the COVID-19 crisis. Finance Research Letters, 38, p.101873.

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