Leverage and systemic risk pro-cyclicality in the Chinese financial system

Cincinelli, Peter; Pellini, Elisabetta; Urga, Giovanni

doi:10.1016/j.irfa.2021.101895

Cited by 21 publications

(6 citation statements)

References 76 publications

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“…Morelli & Vioto (2020) use the bootstrap Kolmogorov-Smirnov test and find that in the Chinese financial system banks contribute the most to systemic risk, followed by real estate and by insurance and brokerage companies. Finally, Cincinelli et al (2021) find that larger financial institutions increase systemic risk, in particular traditional banks, which from 2016 started increasing shadow banking activities, and the real estate financial services with their activity closer to traditional banks.…”

Section: Introductionmentioning

confidence: 90%

Systemic risk in the Chinese financial system: A panel Granger causality analysis

Cincinelli

Pellini

Urga

2022

International Review of Financial Analysis

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Section: Introductionmentioning

confidence: 90%

Systemic risk in the Chinese financial system: A panel Granger causality analysis

Cincinelli

Pellini

Urga

2022

International Review of Financial Analysis

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“… of  can be obtained by maximizing the log-likelihood function of Eq. (10), and in the Logit model, G is the standard logisticcdf, cdf , and  are Logit estimators. Under very general conditions, MLE is consistent, asymptotically normal, and asymptotically efficient.…”

Section: Logit Modelmentioning

confidence: 99%

“…Since the global financial crisis in 2008, China's insurance industry has been growing. The number of listed insurance companies has been gradually increasing, the amount of fund balance of China's insurance industry invested in the financial market has been growing, the relevance of China's insurance industry to the financial market has been increasing, and the position of listed insurance companies as an influential player in the financial market has also increased [10][11]. Listed insurance companies are in an important position in China's insurance industry, and their premium scale accounts for more than half of the industry.…”

Section: Introductionmentioning

confidence: 99%

A Study of Systemic Financial Risk Prediction Models in the Insurance Industry

Wang,

Chen

2024

Applied Mathematics and Nonlinear Sciences

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The influence of the stability of the insurance industry on the strength of the financial market cannot be ignored, and the healthy and strong growth of the insurance industry plays an indispensable role in maintaining the stability of the whole society. To fully analyze the influence of the stability of the insurance industry on the strength of the financial market, this paper proposes a prediction model of systemic financial risk. The insurance industry’s systemic financial risk prediction indicators are determined based on the principle of selecting systemic risk factors and indicators. After preprocessing the collected relevant research data, relevant research variables are determined, a Logit prediction model for the systemic risk of the insurance industry is constructed, and a case study is conducted. The data show that the average value of GDP during these 21 years is 379642.64, and it reaches about 2.4 times the average value in 2023. The insurance industry’s economy grows steadily and rapidly. It reached the maximum response in the 10th period (0.04616), i.e., the development of China’s life insurance industry, which has a more obvious role in promoting the development of the economy. It is predicted that China’s financial risk in the insurance industry in the second half of 2024 will have a great probability of being in a low-risk state. Its predicted probability value is close to 1. The insurance industry’s healthy development has a positive impact on economic development, and the insurance industry’s stable development is of great importance to economic development, as evidenced by this study.

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“…China's economy is concurrently facing formidable challenges, including a notable slowdown in growth, immense pressure to transform industrial structures, and widespread prevalence of high-leverage operations within enterprises [3][4][5]. Additionally, the surge in non-performing assets, financial crises within real estate enterprises, persistent high levels of hidden debts among local governments, and substantial fluctuations in exchange rates are progressively revealing a series of persistent and potential financial risks [6][7][8][9]. These risks have heightened scholarly focus on the intrinsic interconnectedness within China's real economy, the vulnerability of its financial system, and the speculative bubbles in the real estate market.…”

Section: Introductionmentioning

confidence: 99%

Research on the Risk Spillover among the Real Economy, Real Estate Market, and Financial System: Evidence from China

Huangfu,

Yu,

Dong

et al. 2024

Land

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Amidst escalating global policy uncertainties and the painful transformation phase of the Chinese economy, studying the time-varying characteristics of risk spillover among the real economy, real estate market, and financial system holds substantial practical relevance for preventing and resolving significant systemic risks. This paper employs the TVP-VAR-DY model, selects indices from the real sectors to construct a risk spillover index for the real economy, and incorporates indices from the real estate and financial sectors to develop a trivariate SV-TVP-VAR model for empirically analyzing the time-varying nature of risk spillover relationships among these variables. This study reveals that risk spillover among different sectors of the real economy exhibits asymmetry and volatility, with the industrial sector experiencing the highest degree of risk spillover. The prosperity of the real estate market consistently aligns with that of the financial system; however, shocks during periods of risk accumulation in the real estate market significantly amplify risks in the real economy. The financial system serves the real economy, which suffers lesser impacts. Nonetheless, post-2008, the financial system’s support for the real estate market has gradually diminished. Crises exacerbate the extent of risk spillover, but the causative factors and socio-economic context create heterogeneity in fluctuations. Based on these findings, in response to the current real estate shock, the Chinese government should discuss the real economy, the real estate industry, and the financial system within the same research framework. Policies should primarily focus on fiscal measures to promote the recovery of the real economy more rapidly. Additionally, by allowing local governments to implement tailored policies based on local conditions, potential homebuying demand has been effectively stimulated.

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Leverage and systemic risk pro-cyclicality in the Chinese financial system

Cited by 21 publications

References 76 publications

Systemic risk in the Chinese financial system: A panel Granger causality analysis

Systemic risk in the Chinese financial system: A panel Granger causality analysis

A Study of Systemic Financial Risk Prediction Models in the Insurance Industry

Research on the Risk Spillover among the Real Economy, Real Estate Market, and Financial System: Evidence from China

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