Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network

Gong, Xiao-Li; Liu, Jianmin; Xiong, Xiong; Zhang, Wei

doi:10.1016/j.irfa.2022.102359

Cited by 18 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang, et al [11] used the rolling window VAR method to calculate the time-varying volatility spillover index of the Stock markets in Shanghai, Hong Kong and New York after the 'Shanghai-Hong Kong Stock Connect', and then analyzed the size and change of the volatility spillover effect between the two markets. Gong, et al [12] used the variance decomposition spillover index based on TVP-VAR model to analyze the dynamic co-activity and risk transmission mechanism of volatility spillover in the financial system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Risk Spillovers in Non-Ferrous Metal Future Markets During COVID-19: A Frequency Domain Analysis

YANG,

LIU,

ZHANG

et al. 2024

系统科学与信息学报(英文)

View full text Add to dashboard Cite

During the COVID-19 pandemic, the international financial markets experienced severe turbulence. Under the background of "Made in China 2025", substantial entity enterprises have a large demand for non-ferrous metals. With the enhancement of financial attributes of non-ferrous metals, it is vital to prevent financial systemic risk contagion in the non-ferrous metal markets. In this article, the ensemble empirical mode decomposition method is used to decompose the prices of eight important non-ferrous metals futures, and then the dynamic DY risk spillover index model is established from the perspectives of long-term and short-term. The risk spillover between non-ferrous metals during the COVID-19 is quantitatively analyzed from different frequency domains. The study finds that in the long run, the risk spillover relationship between non-ferrous metals remained basically stable, and the change of it after the epidemic is slight. In the short run, the risk spillover relationship has different degrees of structural changes after the outbreak of the COVID-19 pandemic. The ensemble empirical mode decomposition method can distinguish the risk spillovers in different cycles, and help to formulate policies for preventing systemic risks in the non-ferrous metal markets according to the different length of terms.

show abstract

Section: Introductionmentioning

confidence: 99%