Table 1 Important research papers and...

Table 1

Important research papers and corresponding methods

	Authors	Research area	Method	Conclusions
	Amundsen and Elin (2005)	Denmark	Maximum entropy	Only small banks are affected by the risk contagion
	Elsinger et al. (2006)	England	Maximum entropy	Two primary sources of systemic risk are relationship credit and related exposure
	Hellwig (2009)	USA	Balance sheet shock	Systemic risk comes from the balance sheet effect, asset price effect and information contagion effect
	Upper (2011)	\	Maximum entropy	The possibility of default contagion is not high, but it cannot be completely ruled out
	Arinaminpathy et al. (2012)	USA	Balance sheet shock	The importance of large and well-connected banks in system stability is proportional to the size
	Gao and Pan (2012)	China	Maximum entropy and count model	Under the completely decentralized market structure, the contagion risk of the interbank market is minimal
	Paltalidis et al. (2015)	Euro zone	Maximum entropy	Sovereign credit risk is the main source of contagion in the banking network
	Souza et al. (2015)	Brazil	Maximum impact chart and minimum spanning tree	Scale is not the only determinant of the characters of the network. Some large financial institutions have fewer contagion losses than medium-sized institutions
	Sun (2020)	China	Maximum entropy	Bank defaults have the least contagious effect on China's interbank network
	Chen et al. (2020a, b)	China	Maximum entropy	The higher the ratio of interbank assets, the stronger the contagion effects of credit risk
	Chen et al. (2020a, b)	China	Maximum entropy	The level of contagion caused by liquidity shocks has shown a clear downward trend
	Haldane and May (2011)		Balance sheet shock	Proposed the framework to study the impact of risk on the balance sheet.
	Feng and Li (2021)	China	A model with random shocks	Cross shareholding networks magnify and spread small but continuous external shocks
Theoretical methods	Ren et al. (2014)	\	Liquidating payments	The existence, uniqueness and continuity of financial networks can be used as the basis of systematic risk measurement
	Hurd (2016)	\	Random graph	Bootstrap percolation is an accurate concept to solve and understand the cascade growth of simple networks
	Blavarg and Nimander (2002)	Sweden	Credit risk mitigation of counterparties	Banks with significant risk exposures are more likely to cause systemic risks
Bilateral transaction exposure	Lelyveld and Liedorp (2006)	Holland	Maximum entropy	The bankruptcy of a large bank will bring a considerable burden to other banks, but it will not lead to a complete collapse of the market.
	Frisell et al. (2007)	Sweden	Monte Carlo simulation	Reconstruct bilateral exposures could underestimate the risk of default contagion
	Memmel and Stein (2008)	German	Round by round algorithm	The overall risk of interbank contagion is very low, but contagion may occur if a large bank fails
	Mistrulli (2011)	Italy	Maximum entropy	The maximum entropy method overestimates the effects of contagion
	Kanno (2015)	Japan	Maximum entropy estimation and SIR model	The three global systemically essential banks overwhelmed other banking groups in terms of interconnectivity
	Hausenblas et al. (2015)	Czech Republic	Computational model	The possibility of contagion caused by credit loss of interbank risk exposure is limited
	Gorpe et al. (2019)	Euro zone	CoMap	A critical point at which less fragility spreads to a highly fragile country is a nonlinear function of the combination of network structure and bank characteristics
Others	Huynh et al. (2020)	Vietnam	Chi-plots and Kendall plots and copula	The risk of each bank may be passed on to other banks through stock returns
	Ahelegbey et al. (2020)	Worldwide	VAR model	Bilateral risk exposure and market prices are both infectious channels for one country to transmit shocks to other countries
	Xlg et al. (2020)	China	Macro jump and volatility spillover network	The capital market service industry plays a leading role in risk contagion, followed by the currency service industry and the insurance industry
	Chen et al. (2021)	China	A model for solvency contagion risk	Systemic contagion losses of the network are highly dependent on the perceived exogenous recovery rate
	Yang et al. (2021)	China	EPU network	China is the Asia–Pacific EPU network's center

	Authors	Research area	Method	Conclusions
	Amundsen and Elin (2005)	Denmark	Maximum entropy	Only small banks are affected by the risk contagion
	Elsinger et al. (2006)	England	Maximum entropy	Two primary sources of systemic risk are relationship credit and related exposure
	Hellwig (2009)	USA	Balance sheet shock	Systemic risk comes from the balance sheet effect, asset price effect and information contagion effect
	Upper (2011)	\	Maximum entropy	The possibility of default contagion is not high, but it cannot be completely ruled out
	Arinaminpathy et al. (2012)	USA	Balance sheet shock	The importance of large and well-connected banks in system stability is proportional to the size
	Gao and Pan (2012)	China	Maximum entropy and count model	Under the completely decentralized market structure, the contagion risk of the interbank market is minimal
	Paltalidis et al. (2015)	Euro zone	Maximum entropy	Sovereign credit risk is the main source of contagion in the banking network
	Souza et al. (2015)	Brazil	Maximum impact chart and minimum spanning tree	Scale is not the only determinant of the characters of the network. Some large financial institutions have fewer contagion losses than medium-sized institutions
	Sun (2020)	China	Maximum entropy	Bank defaults have the least contagious effect on China's interbank network
	Chen et al. (2020a, b)	China	Maximum entropy	The higher the ratio of interbank assets, the stronger the contagion effects of credit risk
	Chen et al. (2020a, b)	China	Maximum entropy	The level of contagion caused by liquidity shocks has shown a clear downward trend
	Haldane and May (2011)		Balance sheet shock	Proposed the framework to study the impact of risk on the balance sheet.
	Feng and Li (2021)	China	A model with random shocks	Cross shareholding networks magnify and spread small but continuous external shocks
Theoretical methods	Ren et al. (2014)	\	Liquidating payments	The existence, uniqueness and continuity of financial networks can be used as the basis of systematic risk measurement
	Hurd (2016)	\	Random graph	Bootstrap percolation is an accurate concept to solve and understand the cascade growth of simple networks
	Blavarg and Nimander (2002)	Sweden	Credit risk mitigation of counterparties	Banks with significant risk exposures are more likely to cause systemic risks
Bilateral transaction exposure	Lelyveld and Liedorp (2006)	Holland	Maximum entropy	The bankruptcy of a large bank will bring a considerable burden to other banks, but it will not lead to a complete collapse of the market.
	Frisell et al. (2007)	Sweden	Monte Carlo simulation	Reconstruct bilateral exposures could underestimate the risk of default contagion
	Memmel and Stein (2008)	German	Round by round algorithm	The overall risk of interbank contagion is very low, but contagion may occur if a large bank fails
	Mistrulli (2011)	Italy	Maximum entropy	The maximum entropy method overestimates the effects of contagion
	Kanno (2015)	Japan	Maximum entropy estimation and SIR model	The three global systemically essential banks overwhelmed other banking groups in terms of interconnectivity
	Hausenblas et al. (2015)	Czech Republic	Computational model	The possibility of contagion caused by credit loss of interbank risk exposure is limited
	Gorpe et al. (2019)	Euro zone	CoMap	A critical point at which less fragility spreads to a highly fragile country is a nonlinear function of the combination of network structure and bank characteristics
Others	Huynh et al. (2020)	Vietnam	Chi-plots and Kendall plots and copula	The risk of each bank may be passed on to other banks through stock returns
	Ahelegbey et al. (2020)	Worldwide	VAR model	Bilateral risk exposure and market prices are both infectious channels for one country to transmit shocks to other countries
	Xlg et al. (2020)	China	Macro jump and volatility spillover network	The capital market service industry plays a leading role in risk contagion, followed by the currency service industry and the insurance industry
	Chen et al. (2021)	China	A model for solvency contagion risk	Systemic contagion losses of the network are highly dependent on the perceived exogenous recovery rate
	Yang et al. (2021)	China	EPU network	China is the Asia–Pacific EPU network's center

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