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<p>There exists a complex interplay between the aggregate risk of the stock market and the risks inherent to various industries. However, existing studies have largely overlooked the disparities in risk contagion effects between different industries and the overall market across diverse time scales, and there remains instability in delineating risk frequency domains. Consequently, it becomes challenging to fully unravel the intricate correlation dynamics between different industries and the broader stock market across varying temporal dimensions. Therefore, this paper takes the China stock market as a representative of emerging markets, selects eleven distinct industry indices along with the Shanghai-Shenzhen 300 Index (CSI 300 Index), and introduces the Variable Mode Decomposition (VMD) algorithm to extract intricate information from time series data. Additionally, the Fuzzy Entropy (FE) algorithm is employed to effectively reconstruct different frequency domains. Furthermore, this paper integrates the strengths of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, the Copula function, and the Conditional Value at Risk (CoVaR) model. By constructing the novel VMD-FE-GARCH-Copula-CoVaR hybrid model, this research aims to explore the risk contagion characteristics of various industries and the Shanghai-Shenzhen 300 Index across different time scales, offering a fresh perspective for the paper of risk contagion within stock market industries.</p>

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Multiscale risk spillover analysis of China’s stock market industry: evidence supported by a novel hybrid model based on signal decomposition technology

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Zhan Linjie,
  • Tang Zhenpeng

摘要

There exists a complex interplay between the aggregate risk of the stock market and the risks inherent to various industries. However, existing studies have largely overlooked the disparities in risk contagion effects between different industries and the overall market across diverse time scales, and there remains instability in delineating risk frequency domains. Consequently, it becomes challenging to fully unravel the intricate correlation dynamics between different industries and the broader stock market across varying temporal dimensions. Therefore, this paper takes the China stock market as a representative of emerging markets, selects eleven distinct industry indices along with the Shanghai-Shenzhen 300 Index (CSI 300 Index), and introduces the Variable Mode Decomposition (VMD) algorithm to extract intricate information from time series data. Additionally, the Fuzzy Entropy (FE) algorithm is employed to effectively reconstruct different frequency domains. Furthermore, this paper integrates the strengths of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, the Copula function, and the Conditional Value at Risk (CoVaR) model. By constructing the novel VMD-FE-GARCH-Copula-CoVaR hybrid model, this research aims to explore the risk contagion characteristics of various industries and the Shanghai-Shenzhen 300 Index across different time scales, offering a fresh perspective for the paper of risk contagion within stock market industries.