Empirical Study on the Factors Influencing Stock Market Volatility in China

ArXiv ID: 2501.08668 “View on arXiv”

Authors: Unknown

Abstract

This paper mainly utilizes the ARDL model and principal component analysis to investigate the relationship between the volatility of China’s Shanghai Composite Index returns and the variables of exchange rate and domestic and foreign bond yields in an internationally integrated stock market. This paper uses a daily data set for the period from July 1, 2010 to April 30, 2024, in which the dependent variable is the Shanghai Composite Index return, and the main independent variables are the spot exchange rate of the RMB against the US dollar, the 10-year treasury bond yields in China and the United States and their lagged variables, with the effect of the time factor added. Firstly, the development of the stock, foreign exchange and bond markets and the basic theories are reviewed, and then each variable is analyzed by descriptive statistics, the correlation between the independent variables and the dependent variable is expanded theoretically, and the corresponding empirical analyses are briefly introduced, and then the empirical analyses and modeling of the relationship between the independent variables and the dependent variable are carried out on the basis of the theoretical foundations mentioned above with the support of the daily data, and the model conclusions are analyzed economically through a large number of tests, then the model conclusions are analyzed economically. economic analysis of the model conclusions, and finally, the author proposes three suggestions to enhance the stability and return of the Chinese stock market, respectively. Key Words: Chinese Stock Market, Volatility, GARCH, ARDL Model

Keywords: ARDL Model, Principal Component Analysis, Volatility, GARCH, Stock-Bond-Foreign Exchange Nexus, Equity (Chinese Stock Market)

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders
  • Why: The paper uses established econometric models (ARDL, PCA, GARCH) with standard derivations, placing it at moderate math complexity, while the extensive daily dataset, statistical testing, and multi-variable analysis indicate strong empirical rigor suitable for implementation.
  flowchart TD
    Start["Research Goal:<br/>'Identify Factors Influencing<br/>China's Stock Market Volatility'"] --> Data["Data (Daily: Jul 2010 - Apr 2024)<br/>- Shanghai Composite Index Returns<br/>- RMB/USD Exchange Rate<br/>- China & US 10Y Treasury Yields"]
    Data --> Methods["Key Methodology<br/>- ARDL Model<br/>- Principal Component Analysis<br/>- GARCH (Volatility Estimation)"]
    Methods --> Process["Computational Process<br/>1. Descriptive Statistics<br/>2. Correlation Analysis<br/>3. Unit Root Tests<br/>4. Long-run & Short-run Modeling"]
    Process --> Outcomes["Key Findings/Outcomes<br/>- Established Stock-Bond-FX Nexus<br/>- Identified Lagged Variables Impact<br/>- Policy Suggestions:<br/>  1. Diversify Investment<br/>  2. Improve Market Mechanisms<br/>  3. Monitor Global Spillovers"]