Indispensable Source of Risk Contagion With Big Data Analysis From a More Comprehensive View on Shadow Banking

Indispensable Source of Risk Contagion With Big Data Analysis From a More Comprehensive View on Shadow Banking

Peijin Li, Xiutong Yi, Chonghui Zhang, Tomas Baležentis
Copyright: © 2024 |Pages: 29
DOI: 10.4018/JGIM.339190
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Abstract

Although shadow banking widely exists in the financial systems of various countries, their definitions vary significantly due to specific economic and financial characteristics. This paper classifies Chinese shadow banking into six categories: securities, trust, private lending, banking, fund, and insurance. The AR-GARCH-DCC model is used to measure systemic risk spillover through from an industrial and institutional perspective. The network topology index is employed to analyze risk contagion and further explore influencing factors. Firstly, based on the results of the AR-GARCH-DCC, the estimated dynamic volatility (σ) indicates that shadow banking risk spillover is time-varying, especially in trust and securities. Second, according to the static risk spillover analysis, various institutions play different roles and can transform between risk spillovers and overflowers. Thirdly, eigenvector centrality, leverage, assets, CPI, and macroeconomic prosperity significantly impact shadow banking systemic risk spillover.
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Literature

Despite the widespread presence of shadow banking around the globe, its definition varies in different countries. At the beginning of the 2008 financial crisis, McCulley (2007) introduced the concept of the “shadow banking system.” This concept primarily refers to financial institutions that are separated from traditional sovereign regulatory banks. It is loosely defined as MMFs, structural investment vehicles, and channels among financial intermediaries that leverage the overall financial landscape. Krugman (2008) considered it as nonbank financial institution that makes various complicated financial arrangements to escape supervision. Gorton (2009) described it as institutions that combine repo with securitization or other information-insensitive debt to accomplish the same function for firms but differ from depository institutions by involving the repo market. Tucker (2010) focused on the instruments, structures, firms, or markets that replicate the core features of commercial banks on liquidity services, maturity mismatch, and leverage alone or in combination. Pozsar et al. (2010) defined shadow credit intermediation to include all credit intermediation activities that are implicitly and indirectly enhanced or unenhanced by official guarantees, including finance companies, asset-backed commercial paper (ABCP) conduits, structured investment vehicles (SIVs), credit hedge funds, money market mutual funds, securities lenders, limited-purpose finance companies (LPFCs), and government-sponsored enterprises (GSEs). Financial Stability Board (2011) believed that shadow banking should be interpreted from two aspects. In a broad sense, shadow banking involves the financial system and credit intermediaries excluded from the banking system. In a narrow sense, it refers to businesses executed by financial institutions that may lead to systemic risk, as regulatory blind spots exist—especially institutions whose business scope is limited to maturity or liquidity transformation and leverage trading. In summary, the definition of shadow banking has focused on financial or nonfinancial institutions that are outside the supervision of the traditional banking system and mainly engaged in traditional liquidity conversion and credit risk management. However, various countries have different definitions of shadow banking due to variations in financial structure caused by different levels of financial development. Therefore, for each country, the study should follow the special definition given by the government. Before the financial crisis, research on systemic risk mainly focused on a single institution, using VaR to measure the risk. However, certain limitations arose, particularly in ignoring the correlation between institutions. Adrian & Brunnermeier (2011) introduced CoVaR as a measure of systemic risk—an effective indicator representing the value at risk for financial institutions when exposed to risk. Based on this measurement, scholars developed methods for measuring and expanding indicators. Girardi et al. (2013) utilized GARCH to calculate CoVaR and introduced ∆CoVaR, defining systemic risk contribution. Then, Acharya et al. (2017) introduced the systemic expected shortfall (SES), calculated as the expected loss of a single institution during a crisis based on leverage, and marginal expected shortfall (MES). Brownless & Engle (2017) used SRISK to overcome the shortcomings of SES in reflecting risks. The contingent claims approach (CCA), based on the Black-Scholes option pricing formula, was employed by Gray et al. (2007) to measure sovereign risk, quantifying the degree of asset-liability mismatch and capturing the “nonlinear” aspects. Gray & Jobst (2013) proposed the system contingent claims approach (SCCA) by calculating tail risk. Despite continuous measurement development, research on systemic risk mainly excludes shadow banking, whose systemic risk is significant, disregarding the triggering factors of shadow banking systemic risk.

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