Empirical estimation of default and asset correlation of large corporates and banks in India

Abstract

Estimation of default and asset correlation is crucial for banks to manage and measure portfolio credit risk. This would require studying the risk profile of the banks' entire credit portfolio and developing the appropriate methodology for the estimation of default dependence. Measurement and management of correlation risk in the credit portfolio of banks has also become an important area of concern for bank regulators worldwide. The BCBS (2006) has specifically included an asset correlation factor in the computation of credit risk capital requirement by banks adopting the Internal Ratings Based Approach. We estimate default correlation in the credit portfolio of banks. These correlation estimates will help the regulator in India to understand the linkage between bank's portfolio default risks with the systematic factors. We also derive default and asset correlations of Indian corporate and compare it with global scenario. The work tries to find the relationship of the correlation to the default probability as specified by the Basel committee. The findings of this paper could be used further in estimating portfolio credit risk, economic capital and risk adjusted returns on economic capital for large corporate exposures of banks.

Impact Assessment Study of NPAs and Rate of Recovery: Are Private Sector Banks in India Better off?

“…We consider NNPA over GNPA because it is considered to be a better indicator of default risk and represents the true risk of the banks in terms of actual loss after adjusting for provisions and guarantees. On the other hand, the recovery ratio indicates recovery of outstanding default amount of a particular year as a percentage of 3-year average GNPA (Bandyopadhyay & Ganguli, 2013). Due to the lack of facility-wise recovery data, the study considers data on reduction in NPAs (Misra et al, 2016).…”

Predicting Co-Movement of Banking Stocks Using Orthogonal GARCH

“…If a portfolio consists of assets related to one another, there would be no diversification benefits. Hence, a lower correlation between assets constructing portfolios resulted in a more diversified portfolio with a lower risk (Bandyopadhyay and Ganguly 2012). According to the idiosyncratic risk hypothesis, diversification eliminates the specific (idiosyncratic) risk (Atahau 2014).…”

“…In practice, each stock will have different characteristics, although they tend to have the same movement (co-movement) that is due to similar factors influencing them. According to Bandyopadhyay and Ganguly (2012), the economic cycle affects the mutual dependence or the same co-movement of firms' stocks. When a country experiences a recession, it will affect many firms simultaneously.…”

“…The application of multivariate GARCH in the banking sector has been studied by Elyasiani and Mansur (2004) in measuring bank stock return sensitivities to long-term and short-term interest rates. Bandyopadhyay and Ganguly (2012) reviewed the co-movement study in large corporations and the banking sector and found bank stock portfolio return sensitivities that were due to changes in both long-term and short-term interest rates. Furthermore, Byström (2004) examined the co-movement between the Nordic stock markets during the Asian Financial Crisis using the OGARCH method.…”