Measurement of Systemic Risk Based on the QRDCCNN Model-Reference-Cited by-同舟云学术

Measurement of Systemic Risk Based on the QRDCCNN Model

Published:2024-08-06 Issue: Volume: Page:
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Author:

LI JUCHAO¹,SHENG JILIANG²^ORCID,HUANG YI²

Affiliation:

1. Beijing University of Technology

2. Jiangxi University of Finance and Economics

Abstract

Measuring and preventing systemic risk have always been core issues in finance. To accurately capture systemic risk, this is the first introduction of the Quantile Regression Dilated Causal Convolution Neural Network (QRDCCNN) model for assessing systemic risk. This model focuses on the causal consistency of financial time series and effectively expands the model's receptive field by increasing the dilation rate layer by layer. The study selects the daily closing prices of the S\&P 500 index and 38 US financial institutions as subjects. The QRDCCNN model is employed to measure the VaR of each financial institution and the CoVaR of the financial system when these institutions are in extreme risk conditions. This paper compares the results of the QRDCCNN model with those from the DCC-GARCH, quantile regression, QRNN, and QRCNN models using the Kupiec test. The research results show that the QRDCCNN model has the highest accuracy, followed by QRNN and QRCNN models, while the DCC-GARCH model has the lowest accuracy.

Publisher

Springer Science and Business Media LLC

Reference28 articles.

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5. Gong, Xiao-Li and Liu, Xi-Hua and Xiong, Xiong and Zhang, Wei (2020) Research on China's financial systemic risk contagion under jump and heavy-tailed risk. International Review of Financial Analysis 72: 101584 https://doi.org/https://doi.org/10.1016/j.irfa.2020.101584, Journal Article, https://www.sciencedirect.com/science/article/pii/S1057521920302283, 1057-5219, Jump risk Tail risk Volatility spillover network Systemic risk, To accurately measure the dynamic characteristics of systemic risk contagion under the impact of extreme financial events, we construct a research framework that analyzes the contagion dynamics of systemic risk under extreme risk impact from the perspectives of both time and space. Based on the macro-jump CCA method, this paper extracts the heterogeneous volatility sequence of financial industries considering the thick tail of the distribution of financial assets returns. Then, the dynamic variation of systemic risk in the financial sectors is characterized from the time dimension. The volatility spillover network method is used to examine the spillover contagion of systemic risk among financial system sectors from the spatial dimension. Empirical studies have found that when considering the risk contagion level, the capital market service sector plays a risk ‑leading role, followed by the currency service sector and the insurance sector. The measurement indicators that consider the jump risk and the tail risk have good early warning effects on extreme financial events. Seen from the spatial direction of risk spillover, the real estate sector exhibits the most obvious risk spillover effect on other sectors and can be regarded as the source of systemic risk, which suggests differentiated regulation.