Abstract:The National Banking Acts (NBAs) of 1863–1864 established rules governing the amounts and locations of interbank deposits, thereby reshaping the bank networks. Using unique data on bank balance sheets and detailed interbank deposits in 1862 and 1867 in Pennsylvania, we study how the NBAs changed the network structure and quantify the effect on financial stability in an interbank network model. We find that the NBAs induced a concentration of interbank deposits at both the city and bank levels, creating systemi… Show more
“…Regarding the formation of complex risk networks of financial markets, Berndsen et al [9] propose an interdependent network that couples multiple layers of transmission paths of financial institutions to facilitate a more accurate understanding of the true connectivity architecture of the financial system. Anderson et al [10] show that transactional behaviors among financial institutions lead financial markets to form a complex network, and the degree of network centrality has an inconsistent effect on the stability of financial markets. ey suggest that small and medium-sized financial institutions with a more centralized network structure have better risk diversification, while central banks are exposed to larger shocks that increase network vulnerability.…”
Section: Network Contagion Of Financial Riskmentioning
confidence: 99%
“…Consistent with the risk spillover networks constructed for each sector of Chinese financial markets, the return series of 19 financial institutions, and 5 real estate companies are all stationary time series. Based on the lag order test, we construct a second-order VAR model and calculate the proportion of variation in forecast error d ij(10) for the forward 10step forecast. In a single risk spillover network, network nodes and connected edges are identified using the same color for institutions belonging to the same sector.…”
The global financial market shocks have intensified due to the COVID-19 epidemic and other impacts, and the impacts of economic policy uncertainty on the financial system cannot be ignored. In this paper, we construct asymmetric risk spillover networks of Chinese financial markets based on five sectors: bank, securities, insurance, diversified finance, and real estate. We investigate the complexity of the risk spillover effect of Chinese financial markets and the impact of economic policy uncertainty on the level of network contagion of financial risk. The study yields three findings. First, the cross-sectoral risk spillover effects of Chinese financial markets are asymmetric in intensity. The bank sector is systemically important in the risk spillover network. Second, the level of risk stress in the real estate sector has increased in recent years, and it plays an important role in the path of financial risk contagion. Third, Economic policy uncertainty has a significant positive impact on the level of network contagion of financial risk of Chinese financial markets.
“…Regarding the formation of complex risk networks of financial markets, Berndsen et al [9] propose an interdependent network that couples multiple layers of transmission paths of financial institutions to facilitate a more accurate understanding of the true connectivity architecture of the financial system. Anderson et al [10] show that transactional behaviors among financial institutions lead financial markets to form a complex network, and the degree of network centrality has an inconsistent effect on the stability of financial markets. ey suggest that small and medium-sized financial institutions with a more centralized network structure have better risk diversification, while central banks are exposed to larger shocks that increase network vulnerability.…”
Section: Network Contagion Of Financial Riskmentioning
confidence: 99%
“…Consistent with the risk spillover networks constructed for each sector of Chinese financial markets, the return series of 19 financial institutions, and 5 real estate companies are all stationary time series. Based on the lag order test, we construct a second-order VAR model and calculate the proportion of variation in forecast error d ij(10) for the forward 10step forecast. In a single risk spillover network, network nodes and connected edges are identified using the same color for institutions belonging to the same sector.…”
The global financial market shocks have intensified due to the COVID-19 epidemic and other impacts, and the impacts of economic policy uncertainty on the financial system cannot be ignored. In this paper, we construct asymmetric risk spillover networks of Chinese financial markets based on five sectors: bank, securities, insurance, diversified finance, and real estate. We investigate the complexity of the risk spillover effect of Chinese financial markets and the impact of economic policy uncertainty on the level of network contagion of financial risk. The study yields three findings. First, the cross-sectoral risk spillover effects of Chinese financial markets are asymmetric in intensity. The bank sector is systemically important in the risk spillover network. Second, the level of risk stress in the real estate sector has increased in recent years, and it plays an important role in the path of financial risk contagion. Third, Economic policy uncertainty has a significant positive impact on the level of network contagion of financial risk of Chinese financial markets.
“…With teams getting involved by themselves in annotation campaigns and leveraging Deep Learning techniques, large firms are now starting to use more advanced systems. .Since the financial crisis, economists have been trying to understand systemic risk (Acemoglu, Ozdaglar, & Tahbaz‐Salehi, 2015; Anderson, Paddrik, & Wang, 2019; Chen, Iyengar, & Moallemi, 2013; Löffler & Raupach, 2018). Network models, big data analysis and text mining, have been used to understand risk (Gai & Kapadia, 2019; Gang, Xiangrui, Yi, Alsaadi, & Herrera‐Viedma, 2019; Yun, Jeong, & Park, 2019).…”
Artificial Intelligence (AI) is creating a rush of opportunities in the financial sector, but financial organizations need to be aware of the risks inherent in the use of this technology. Financial organizations are integrating AI in their operations: in-house, outsourced, or ecosystem-based. The growth of AI-based fintech firms has encouraged several mergers and acquisitions among financial service providers and wealth managers as they grapple with volatility, uncertainty, complexity, and ambiguity. AI's unique promise of combined cost reduction and increased differentiation makes it generally attractive across the board. However, perhaps other than fraud detection, these benefits depend on the scale of an organization. Risk arises from nonrepresentative data, bias inherent in representative data, choice of algorithms, and human decisions, based on their AI interpretations (and whether humans are involved at all once AI has been unleashed). Risk reduction requires a vigilant division of labour between AI and humans for the foreseeable future.
“…Carlson, Correia, and Luck (2018) exploit a discontinuity in bank capital requirements during the 19th century National Banking era in the United States to investigate how banking competition affect credit provision and growth. Anderson, Paddrik, and Wang (2019) use original data on bank balance sheets and a network model to show that the introduction of the reserve requirements by the National Banking Acts of 1863 and 1864 in the US changed the nature of the financial links between banks. Calomiris and Jaremski (2019) also use a discontinuity in regulation (i.e.…”
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