PurposeThis paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to present methods for directly estimating corporate probability of default (PD) using financial as well as non‐financial variables.Design/methodology/approachMultiple Discriminate Analysis (MAD) is used for developing Z‐score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default.FindingsThe new Z‐score model developed in this paper depicted not only a high classification power on the estimated sample, but also exhibited a high predictive power in terms of its ability to detect bad firms in the holdout sample. The model clearly outperforms the other two contesting models comprising of Altman's original and emerging market set of ratios respectively in the Indian context. In the logit analysis, the empirical results reveal that inclusion of financial and non‐financial parameters would be useful in more accurately describing default risk.Originality/valueUsing the new Z‐score model of this paper, banks, as well as investors in emerging market like India can get early warning signals about the firm's solvency status and might reassess the magnitude of the default premium they require on low‐grade securities. The default probability estimate (PD) from the logistic analysis would help banks for estimation of credit risk capital (CRC) and setting corporate pricing on a risk adjusted return basis.

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PurposeThe primary objective of the paper is to demonstrate the importance of borrower‐specific characteristics as well as local situation factors in determining the demand prospect as well as the risk of credit loss on residential housing loan repayment behavior in India.Design/methodology/approachUsing 13,487 housing loan accounts (sanctioned from 1993‐2007) data from Banks and Housing Finance Cos (HFCs) in India, this paper attempts to find out the crucial factors that drive demand for housing and its correlation with borrower characteristics using a panel regression method. Next, using logistic regression, housing loan defaults and the major causative factors of the same are examined.FindingsIn analyzing the housing demand pattern, some special characteristics of the Indian residential housing market (demographic and social features) and the housing loan facility structure (loan process, loan margin, loan rate, collateral structure etc.), that have contributed to the safety and soundness of the Indian housing market have been deciphered. The empirical results suggest that borrower defaults on housing loan payments is mainly driven by change in the market value of the property vis‐à‐vis the loan amount and EMI to income ratio. A 10 percent decrease in the market value of the property vis‐à‐vis the loan amount raises the odds of default by 1.55 percent. Similarly, a 10 percent increase in EMI to income ratio raises the delinquency chance by 4.50 percent. However, one cannot ignore borrower characteristics like marital status, employment situation, regional locations, city locations, age profile and house preference which otherwise may inhibit the lender to properly assess credit risk in home loan business, as the results show that these parameters also act as default triggers.Originality/valueThis study contributes on the micro side of the housing market in India, since it uses unique and robust loan information data from banks and HFCs. The empirical results obtained in this paper are useful to regulators, policy makers, market players as well as the researchers to understand housing market demand and risk characteristics in an emerging market economy such as India.

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Credit risk is the risk resulting from the uncertainty that a borrower or a group of borrowers may be unwilling or unable to meet their contractual obligations as per the agreed terms. It is the largest element of risk faced by most banks and financial institutions. Potential losses due to high credit risk can threaten a bank's solvency. After the global financial crisis of 2008, the importance of adopting prudent risk management practices has increased manifold. This book attempts to demystify various standard mathematical and statistical techniques that can be applied in measuring and managing portfolio credit risk in the emerging market in India. It also provides deep insights into various nuances of credit risk management practices derived from the best practices adopted globally, with case studies and data from Indian banks.

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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.

Purpose -The purpose of this article is to discuss a Black-Scholes-Merton (BSM)-based market approach to quantify the default risk of publicly-listed individual companies. Design/methodology/approach -Using the contingent claim approach, a framework is presented to optimally use stock market and balance sheet information of the company to predict its probability of failure as well as ordinal risk ranking over a horizon of one year. Findings -By applying the methodology, yearly estimates of the risk neutral and real probability of default for 150 Indian corporates from 1998 to 2005 were constructed, that give up-to-date point-in-time perspective of their risk assessment. It was found that option model can provide ordinal ranking of companies on the basis of their default risk which also has good early warning predictability. Originality/value -The option-based default probability estimation may be an innovative approach for measuring and managing credit risk even in the emerging market economy. The asset value model developed in this paper based on the BSM model can facilitate the Indian banks as well as investors to get an early warning signal about the company's default status.