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Network-based estimation of systematic and idiosyncratic contagion: The case of Chinese financial institutions

Published on Jul 1, 2019in Emerging Markets Review 2.11
· DOI :10.1016/j.ememar.2019.100624
Jingyu Li (CAS: Chinese Academy of Sciences), Yanzhen Yao2
Estimated H-index: 2
(CAS: Chinese Academy of Sciences)
+ 1 AuthorsXiaoqian Zhu7
Estimated H-index: 7
(CAS: Chinese Academy of Sciences)
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Abstract
Abstract To distinguish systematic and idiosyncratic contagion during financial crises has attracted increasing attention because it can shed light on the potential drivers of contagion. However, the existing methods for distinguishing the two types of contagion cannot work with a large number of institutions. Therefore, this paper innovatively proposes a network-based framework which is able to distinguish the two types of contagion among numerous institutions. By applying the framework to the publicly listed Chinese financial institutions, we've figured out the main drivers of contagion during three financial crises, beneficial for understanding financial stability in China better.
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References54
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Published on Feb 19, 2019in Accounting and Finance 1.48
Lu Wei2
Estimated H-index: 2
(CAS: Chinese Academy of Sciences),
Guowen Li2
Estimated H-index: 2
(CAS: Chinese Academy of Sciences)
+ 1 AuthorsJianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences)
Published on Dec 1, 2018in Emerging Markets Review 2.11
Zhihong Jian (HUST: Huazhong University of Science and Technology), Shuai Wu (HUST: Huazhong University of Science and Technology), Zhican Zhu (HUST: Huazhong University of Science and Technology)
This paper proposes a predictive CoVaR measure to analyze asynchronous risk spillovers between the Chinese stock and futures market. We jointly model the intraday CoVaR dynamics using an extended MV-CAViaR model. The results show the presence of asymmetric spillovers under different market states, different trading rules, and different confidence levels. Specifically, there exist significant downside spillovers and insignificant upside spillovers. Moreover, the futures (stock) market becomes dom...
Published on Jun 1, 2018in Emerging Markets Review 2.11
Miguel A. Rivera-Castro8
Estimated H-index: 8
,
Andrea Ugolini7
Estimated H-index: 7
,
Juan Arismendi Zambrano1
Estimated H-index: 1
(University of Reading)
Abstract In this study the tail systemic risk of the Brazilian banking system is examined, using the conditional quantile as the risk measure. Multivariate conditional dependence between Brazilian banks is modelled with a vine copula hierarchical structure. The results demonstrate that Brazilian financial systemic risk increased drastically during the global financial crisis period. Our empirical findings show that Bradesco and Itau are the origin of the larger systemic shocks from the banking s...
Published on Jun 1, 2018in Emerging Markets Review 2.11
Libing Fang4
Estimated H-index: 4
(NU: Nanjing University),
Boyang Sun2
Estimated H-index: 2
(NU: Nanjing University)
+ 1 AuthorsHonghai Yu4
Estimated H-index: 4
(NU: Nanjing University)
Abstract The Chinese stock market crash in June 2015 has demonstrated necessary to improve understanding of systemic risk from the perspective of financial network. This study constructs a tail risk network to present overall systemic risk of Chinese financial institutions, given the macroeconomic and market externalities. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) method of high-dimensional models, our results show that firm's idiosyncratic risk can be affected by its...
Published on Jan 1, 2018in Journal of Risk 0.52
Xiaoqian Zhu7
Estimated H-index: 7
,
Lu Wei2
Estimated H-index: 2
,
Dengsheng Wu10
Estimated H-index: 10
Published on Jan 1, 2018in Journal of Applied Econometrics 2.05
Mert Demirer5
Estimated H-index: 5
(MIT: Massachusetts Institute of Technology),
Francis X. Diebold78
Estimated H-index: 78
(UPenn: University of Pennsylvania)
+ 1 AuthorsKamil Yilmaz18
Estimated H-index: 18
(Koç University)
We use LASSO methods to shrink, select, and estimate the high‐dimensional network linking the publicly traded subset of the world's top 150 banks, 2003–2014. We characterize static network connectedness using full‐sample estimation and dynamic network connectedness using rolling‐window estimation. Statically, we find that global bank equity connectedness has a strong geographic component, whereas country sovereign bond connectedness does not. Dynamically, we find that equity connectedness increa...
Published on Dec 1, 2017in Emerging Markets Review 2.11
Gang-Jin Wang15
Estimated H-index: 15
(Hunan University),
Zhi-Qiang Jiang24
Estimated H-index: 24
(ECUST: East China University of Science and Technology)
+ 2 AuthorsH. Eugenestanley114
Estimated H-index: 114
(BU: Boston University)
We investigate the interconnectedness and systemic risk of China's financial institutions by constructing dynamic tail-event driven networks (TENETs) at 1% risk level based on weekly returns of 24 publicly-listed financial institutions from 2008 to 2016. Total connectedness reaches a peak when the system exhibits stress, especially during the recent period from mid-2014 to end-2016. Large commercial banks and insurers usually exhibit systemic importance, but some small firms are systemically imp...
Published on Oct 1, 2017in Emerging Markets Review 2.11
Syed Jawad Hussain Shahzad13
Estimated H-index: 13
,
Walid Mensi14
Estimated H-index: 14
(Sultan Qaboos University)
+ 2 AuthorsKhamis Hamed Al-Yahyaee9
Estimated H-index: 9
(Sultan Qaboos University)
This paper examines the downside and upside risk spillovers and dependence structure between five Islamic stock markets (the Islamic Market World index, Islamic indices of USA, UK, Japan and the Islamic Financials sector index) which are of paramount importance for faith-oriented investors and particpants in the oil market. The results underscore the presence of time-varying lower tail dependence between the oil and Islamic stock markets. Furthermore, we provide supportive evidence of asymmetric...
Published on Sep 2, 2017in Quantitative Finance 1.36
Gang-Jin Wang15
Estimated H-index: 15
(BU: Boston University),
Chi Xie23
Estimated H-index: 23
(Hunan University)
+ 1 AuthorsH. Eugenestanley114
Estimated H-index: 114
(BU: Boston University)
Using the CAViaR tool to estimate the value-at-risk (VaR) and the Granger causality risk test to quantify extreme risk spillovers, we propose an extreme risk spillover network for analysing the interconnectedness across financial institutions. We construct extreme risk spillover networks at 1% and 5% risk levels (which we denote 1% and 5% VaR networks) based on the daily returns of 84 publicly listed financial institutions from four sectors—banks, diversified financials, insurance and real estat...
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