Abstract
Draining liquidity is one of the main reasons for the financial crisis. Therefore, exploring the link between various indicators of banks and bank liquidity has great significance for studying bank liquidity risk. The article applies principal component to analysis the main components are extracted from the perspectives of assets, loans, deposits, borrowings and reserves. Then the Bayesian network is used to construct a network of links between liquidity and indicators based on this.
Highlights
The difficulty of liquidity recovery is one of the main reasons for triggering liquidity risk and causing banking crisis
This paper first extracts the principal components from the perspectives of asset class, loan class, deposit class, borrowing class and reserve fund, and uses the hill climbing algorithm to learn the structure of various indicators of banks and bank liquidity risk indicators, Thereby obtaining a link between each comprehensive indicator and liquidity risk
This paper will extract more than 80% of the main components from various indicators, and build a network of links between various categories of indicators and liquidity risk based on this comprehensive indicator
Summary
The difficulty of liquidity recovery is one of the main reasons for triggering liquidity risk and causing banking crisis. It’s very significant to carefully process the liquidity risk of banks and to properly assess and prevent them through the link relationship between indicators. The liquidity risk of commercial banks is of great significance to the stability of the financial system (Fecht, 2004 ; Bonner, 2012) [1,2]. From the existing facts and research, the liquidity risk of commercial banks is due to the maturity mismatch between income and expenditure La Ganga, 2009) [4,5], The big financial crisis in history is mostly due to the liquidity crisis in commercial banks. It is possible to provide advice on the monitoring and evaluation of the liquidity of the bank in terms of the link relationship of the indicators
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