Abstract
Banking sector has close relationship with economic growth. At the end of 2015 condition of global economic has influence from China slowdown, fall down crude oil price, and rasie interest rate by the fed. These had bad impact for developing counties like Indonesia. In the fact financials crisis in Asia at 1998, sub prime mortage crisis in USA at 2008, and goverment debt crisis in Greece at 2011 made many bank in Indonesia collapse. From past crisis banking sector must have more attention to avoid sistemic crisis. This study aim to make prediction banking crisis models from three group of variable. There are internal bank, macroeconomic, and global economic. This research use Crisis and Default index to measure and identificate probably crisis in indivual bank. All of bank were listed in Indonesian Stock Exchange at 2009 until 2014 has taken as sample. This research chose logit model as a probability crisis models and use logistic regression to testing hypothesis. The result from internal factor with non performing loans, labor cost ratio, and loan to deposits ratio was positive relationship with probability banking crisis. Futhermore net interest margin and interest income to total aset was negative influence for banking crisis. Then from macroeconomic and global economic these are domestic inflation and USA real interest rate was positive influence for banking crisis. After that M2 to reserved ratio, USA growth, and oil price was negative impact to make banking crisis in Indonesia
Highlights
Nuris Journal of Education and Islamic Studies 1(1): 2021 p.11-30 yang berbentuk persamaan logit
All of bank were listed in Indonesian Stock Exchange
Asset quality 2Change value or apretiation and Closing price of exchange rate Chart IDR/USD, IDR/USD
Summary
2.2.1 Internal Bank Penelitian ini menggunakan CAMELS untuk menggambarkan kondisi internal bank. Kibritcioglu (2002) menjelaskan bahwa perbankan dikatakan krisis apabila memiliki nilai yang dibawah rata rata atau diatas rata rata dengan melebihi 3 kali standar deviasi dari nilai utama BSF. Bhattacharya dan Roy (2009) telah menjelaskan bahwa perubahan suku bunga adalah komponen yang paling rentan terhadap terjadinya krisis. Formula BSF dan BSS indeks adalah sama .Jadi BSS dan BSF dapat dikombinasi sehingga identifikasi krisis perbankan lebih akurat kombinasi tersebut memasukan empat komponen resiko yang dapat menggambarkan kondisi perbankan pada periode tertentu. Disisi lain jika sebuah bank memiliki CDI score yang positive ( CDI > 0 | 1-P) bank tersebut dinyatakan tidak krisis atau telah pulih dari crisis. Nilai 0 dan 1 digunakan untuk analisa regresi logistik, tetapi jika ingin melihat bank yang paling rentan terhadap krisis skor utama pada CDI dapat dirangking pada setiap periode. Berikut adalah tahapan dalam mengukur krisis perbankan menggunakan CD Indeks
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