Estimating a Joint Probability of Default Index for Indonesian Banks: A Copula Approach

Abstract

We develop a joint default probability index to signal potential systemic risks in the highly concentrated Indonesian banking industry. To build the index, we estimate bank-level tail risks using monthly bank financial reports. We use the copula approach to derive the joint multivariate dependencies at the bank level, as reflected in the monthly financial reports. Our results, which are based on a sample of 104 banks fromDecember 2003 to April 2020, show joint multivariate dependencies at the bank level suggesting that the standard univariate normal distribution is unsuitable for capturing tail risks of individual banks. Our index accurately captures the global financial crisis of 2007-2008 indicating that it is a valid joint default probability index. Further, our index also signaled a higher degree of joint default before the COVID-19 outbreak in2020, suggesting that it is a good indicator of potential systemic risk in the economy.

Macro-Financial Determinants of Default Probability Using Copula: A Case Study of Indonesian Banks

“…In this study, we identify the determinants of the default probability for Indonesian banks. We follow Husodo et al (2020) to estimate the default probability for each bank. We use financial variables from bank balance sheets consisting of current assets, current liabilities, long-term assets, and long-term liabilities.…”

“…In this paper, we follow Valle et al, 2016 andHusodo et al (2020) to investigate the macro-financial factor that drives the default probability in Indonesian banks using the copula approach. Our main idea is that default probability could possibly interact with the business cycle when banks engage in excessive risktaking behaviour during the upturn phase (Anginer et al, 2014), and resulting in upward risk during the downturn phase that links to their bank-specific factor and macroeconomic situation (Fiordelisi and Mare, 2013;and Parrado-Martínez et al, 2019).…”

“…Our paper contributes to the literature on default probability in four ways. First, we develop a method to assess a bank's default probability based on a multivariate distribution following the methodology introduced by Husodo et al (2020) and Valle et al (2016). The copula approach has the advantage of capturing non-linear relationships between variables with complex data structures, where the dependency structure of two random variables is asymmetrical (upper negative or upper positive) (see Brechmann et al, 2013;Pourkhanali et al, 2016;and Zhang, 2014).…”