运筹与管理 ›› 2023, Vol. 32 ›› Issue (8): 193-199.DOI: 10.12005/orms.2023.0270

• 应用研究 • 上一篇    下一篇

中国金融市场风险溢出效应及其时空特征——基于溢出指数方法与DCC-GARCH模型

李博阳1, 张嘉望2, 沈悦3   

  1. 1.长安大学 经济与管理学院,陕西 西安 710064;
    2.陕西师范大学 国际商学院,陕西 西安 710119;
    3.西安交通大学 经济与金融学院,陕西 西安 710061
  • 收稿日期:2021-05-12 出版日期:2023-08-25 发布日期:2023-09-22
  • 通讯作者: 张嘉望(1990-),男,陕西咸阳人,讲师,博士,研究方向:金融风险与公司金融。
  • 作者简介:李博阳(1991-),女,陕西西安人,讲师,博士,研究方向:金融风险管理。
  • 基金资助:
    国家自然科学基金资助项目(72104035,71974157);中国博士后科学基金面上项目(2021M692749);中央高校基本科研业务费专项资金项目(300102232604);陕西省软科学基金项目(2023-CX-RKX-061);陕西省社会科学基金项目(2021D031);西安市社会科学规划基金(23GL65)

Risk Spillover Effect and Its Temporal and Spatial Characteristics in China’s Financial Market: Based on Spillover Index Method and DCC-GARCH Model

LI Boyang1, ZHANG Jiawang2, HEN Yue3   

  1. 1. School of Economics and Management, Chang’an University, Xi’an 710064, China;
    2. International Business School, Shaanxi Normal University, Xi’an 710119, China;
    3. School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China
  • Received:2021-05-12 Online:2023-08-25 Published:2023-09-22

摘要: 运用溢出指数模型和DCC-GARCH模型对我国2005年7月22日至2021年8月27日七大金融市场的风险溢出效应及其时空特征做出了全面分析。结果显示:在时间维度上,中国金融市场风险溢出指数在18%~52%之间波动,动态相关系数在0.09至0.31间变动,当重要政策和事件冲击时金融市场风险溢出程度明显增强,并且风险溢出水平随时间的累积和消减具有非对称性特征。在空间维度上,金融市场间存在非对称溢出效应。房地产、商品和股票市场的风险净溢出指数为正,黄金、货币、外汇和债券市场的风险净溢出指数为负,商品和黄金市场、股票和房地产市场以及债券与黄金市场间的风险关联性较大。

关键词: 金融市场, 风险溢出, 溢出指数模型, DCC-GARCH模型, 非对称性

Abstract: With the accelerating pace of financial globalization, financial innovations emerge one after another, financial markets are closely related, and cross-market contagion of financial risks has seriously threatened China’s economic and financial operation. Under this background, it is of great theoretical significance and practical value to accurately describe the intensity, scale and direction of financial market risk spillover effect, accurately measure the conditional correlation coefficient between financial markets, and carefully describe the time-varying, fluctuating and asymmetric characteristics of risk spillover index and dynamic correlation coefficient, which can not only help to understand the occurrence mechanism and infection characteristics of risk spillover between financial markets, but also the regulatory authorities to formulate and take effective regulatory measures to prevent the chain reaction of cross-market risk spillover from triggering systemic risks under the impact of extreme events.
This paper uses spillover index model and DCC-GARCH model to make a comprehensive analysis of the risk spillover effect and its temporal and spatial characteristics in China’s financial market from July 22, 2005 to August 27, 2021. In this paper, China’s financial market is divided into seven sub-markets, namely, stock market (using CSI 300 index), bond market (using CSI comprehensive net price index), money market (using 7-day interbank offered rate), foreign exchange market (using the central parity of US dollar against RMB exchange rate), commodity market (using Wind commodity comprehensive index), gold market (using spot price of AU9995) and real estate market (using Shenwan real estate industry).
The results show that the risk spillover index of China financial market fluctuates between 18% and 52% in time dimension, which can be roughly divided into three stages: The first stage is from 2007 to 2011, during which the risk spillover index fluctuates greatly. The second stage is the post-crisis era, from 2012 to 2017, during which the overall operation trend of China’s financial market is relatively stable, and the risk spillover index fluctuates at around 33%. In the third stage, from 2018 to the present, firstly, under the influence of the large-scale default of the China bond market in early 2018 and the Sino-US trade dispute, the volatility correlation of China’s financial market has been strengthened. Since then, under the impact of the COVID-19 epidemic, the risk spillover index has greatly increased in early 2020 through the transmission channels of virus infection, panic infection and financial risk cross-market infection, and has continued to do so to this day. The dynamic correlation coefficient varies from 0.09 to 0.31, and the jumping up and down of the dynamic correlation coefficient in the time dimension is asymmetric. In the face of the impact of important domestic and foreign policies and risk events, the dynamic correlation coefficient of China financial markets has a leap-forward growth in a short period of time. After reaching the peak, the dynamic correlation coefficient between financial markets will not weaken immediately, but it will take some time to return to the size before the impact, which shows that the dynamic correlation coefficient between financial markets is asymmetric in the time dimension.
In the spatial dimension, the direction of financial market risk spillover is asymmetric. As far as the acceptance risk spillover index is concerned, the acceptance risk spillover index of stock market, real estate market and gold market ranks in the top three, which are 49.60%, 47.90% and 29.70% respectively, indicating that these three markets are extremely vulnerable to risk contagion and have systemic fragility. As far as the external risk spillover index is concerned, the external risk spillover indexes of the stock market, the real estate market and the commodity market are relatively high, which are 51.90%, 51.90% and 28.20% respectively, indicating that these three financial markets are in the leading position of information in China’s financial system, and their information transmission efficiency is high. Once an emergency happens, the risks will be quickly transmitted to the whole financial system, and they are systemically important financial markets in China. Because the stock market and the real estate market have high acceptance risk spillover index and external risk spillover index, they play an intermediary and bridge role in China’s financial system. The stock market, commodity market and real estate market are the net spillers of risks, while the bond market, money market, foreign exchange market and gold market are the net recipients of risks. The risk correlation between stock market and real estate market, commodity market and gold market, and bond market and gold market is relatively large.
Limited by data and capacity, this paper only analyzes the risk spillover effect among the seven major financial markets in China. In future research, we can consider distinguishing “good fluctuations” and “bad fluctuations” in financial markets. In addition, it is worth exploring to consider the price correlation and risk spillover effect between green financial markets and carbon markets under the background of energy transformation.

Key words: financial market, risk spillover, spillover index, DCC-GARCH model, asymmetry

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