scholarly journals BankCaR (Bank Capital-at-Risk): A Credit Risk Model for US Commercial Bank Charge-Offs

2008 ◽  
Author(s):  
Jon Frye ◽  
Eduard A. Pelz
Keyword(s):  
At Risk ◽  
Credit Risk ◽  
Commercial Bank ◽  
Risk Model ◽  
Bank Capital ◽  
Credit Risk Model ◽  
Capital At Risk

Lending Booms

2019 ◽  
pp. 116-131
Author(s):  
Hyun Song Shin
Keyword(s):  
At Risk ◽  
Credit Risk ◽  
Value At Risk ◽  
Risk Model ◽  
House Prices ◽  
Loan Portfolio ◽  
Balance Sheets ◽  
Credit Risks ◽  
Credit Risk Model

A system of interlinked balance sheets of intermediaries that follow the Value-at-risk rule has the feature that an increase in house prices transmits valuation changes through the value of debt instruments. The analysis uses the Vasicek credit risk model for the diversification of individual credit risks in the loan portfolio. Leverage and wholesale funding is key to understanding lending booms.

scholarly journals ANALISIS LIQUIDITY CREATION PADA PERBANKAN DI INDONESIA TAHUN 2007-2013 (STUDI KASUS PADA 10 BANK BESAR DI INDONESIA TAHUN 2013)

2016 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Muhammad Mirajudin ◽  
Prasetiono Prasetiono
Keyword(s):  
Credit Risk ◽  
Commercial Bank ◽  
Banking Industry ◽  
Third Party ◽  
Bank Credit ◽  
Bank Capital ◽  
Banking Supervision ◽  
Earnings Volatility ◽  
Negative Effect ◽  
Income Instability

Problems related to banking in Indonesia today is the problem of liquidity. It is shownfrom a commercial bank credit grew 23.03% but not matched by growth in depositswhich only reached 16.56% in 2012 (Report of Banking Supervision, 2012). Therefore,this study aims to determine the liquidity creation in Indonesia as well as to analyze theinfluence of bank capital, credit risk and income instability towards liquidity creation.The samples includes 10 major banks in Indonesia with total assets of more thanRp120billion in 2013. The reason for choosing this sample because of the 10 largestbanks reflects the state of the banks in Indonesia which accounted for 65.2% of totalassets, 65.6% of total loans, and 66% of total deposits or deposits in the banking industry(PEFINDO, 2014). The results of this research note that the bank's capital and earningsvolatility is significant negative effect on liquidity creation. While the credit risk of anegative but insignificant effect on liquidity creation. In the determination coefficient testshowed that 43.6% dependent variable is the liquidity creation can be explained by theindependent variable is the capital of banks, credit risk and earnings volatility. While56.4% is explained by other variables outside the model of this study.Keywords: liquidity creation, capital of banks, credit risk, third-party funds, banks inIndonesia.

Replication of Contingent Claims in a Reduced-Form Credit Risk Model with Discontinuous Asset Prices

2006 ◽  
Vol 22 (4) ◽  
pp. 661-687 ◽  
Author(s):  
Tomasz R. Bielecki ◽  
Monique Jeanblanc ◽  
Marek Rutkowski
Keyword(s):  
Credit Risk ◽  
Asset Prices ◽  
Risk Model ◽  
Contingent Claims ◽  
Reduced Form ◽  
Credit Risk Model

A HMM Intensity-Based Credit Risk Model and Filtering

2013 ◽  
pp. 169-184 ◽  
Author(s):  
Robert J. Elliott ◽  
Tak Kuen Siu
Keyword(s):  
Credit Risk ◽  
Risk Model ◽  
Credit Risk Model
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