To evaluate loan applicants, banks increasingly use credit scoring models. The objective of such models typically is to minimize default rates or the number of incorrectly classified loans. Thereby they fail to take into account that loans are multiperiod contracts, for which reason it is important for banks not only to know if but also when a loan will default. In this paper a bivariate tobit model with a variable censoring threshold and sample selection effects is estimated for (1) the decision to provide a loan or not and (2) the survival time of granted loans. The model proves to be an effective tool to separate applicants with short and with long survival times. The bank's loan provision process is shown to be inefficient: loans are granted in a way that conflicts with both default risk minimization and survival time maximization. There is thus no trade-off between higher default risk and higher return in the lending policy.

This paper builds on the credit-scoring literature and proposes a method to calculate portfolio credit risk. Individual default risk estimates are used to compose a value-at-risk (VaR) measure of credit risk. In general, credit-scoring models suffer from a sample-selection bias. The starting point is therefore to estimate an unbiased scoring model using the bivariate probit approach. The paper uses a large data set with Swedish consumer credit data that contains extensive financial and personal information on both rejected and approved applicants. We study how marginal changes in a default-risk-based acceptance rule would shift the size of the bank’s loan portfolio, its VaR exposure and average credit losses. Finally, we compare the risk in the sample portfolio with that in an efficiently provided portfolio of equal size. The results show that the size of a small consumer loan does not affect associated default risk, implying that the bank provides loans in a way that is not consistent with default-risk minimization. VaR calculations indicate that an efficient selection (by means of a default-risk-based rule) of loan applicants can reduce credit risk by up to 80%.

In this paper we empirically study interactions between real activity and the financial stance. Using aggregate data we examine a number of candidate measures of the financial stance of the economy. We find strong evidence for substantial spillover effects on aggregate activity from our preferred measure. Given this result, we use a large micro-data set for corporate firms to develop a macro–micro-model of the interaction between the financial and real economy. This approach implies that the impulse responses of a given aggregate shock will depend on the portfolio structure of firms at any given point in time.

This paper re-examines the empirical relationship between financial and economic development while (i) taking into account their dynamics and (ii) differentiating between stock market and banking sector development. We study the cointegration and causality between finance and growth for 22 advanced economies. Our time series analysis suggests that causality patterns depend on whether countries’ financial development stems from the stock market or the banking sector. We show that stock market development tends to cause economic development, while a reverse causality is mostly present between banking sector development and output growth. These findings indicate that the direction of causality between finance and growth is likely to be different at high levels of development.

This paper aims at improving our understanding of internal risk rating systems (IRS) at large banks, of the way in which they are implemented, and at verifying if IRS produce consistent estimates of banks’ loan portfolio credit risk. An important property of our work is that the size of our data set allows us to derive measures of credit risk without making any assumptions about correlations between loans, by applying Carey’s [Carey, Mark, 1998. Credit risk in private debt portfolios. Journal of Finance LIII (4), 1363–1387] non-parametric Monte Carlo re-sampling method. We find substantial differences between the implied loss distributions of two banks with equal “regulatory” risk profiles; both expected losses and the credit loss rates at a wide range of loss distribution percentiles vary considerably. Such variation will translate into different levels of required economic capital. Our results also confirm the quantitative importance of size for portfolio credit risk: for common parameter values, we find that tail risk can be reduced by up to 40% by doubling portfolio size. Our analysis makes clear that not only the formal design of a rating system, but also the way in which it is implemented (e.g. a rating grade composition; the degree of homogeneity within rating classes) can be quantitatively important for the shape of credit loss distributions and thus for banks’ required capital structure. The evidence of differences between lenders also hints at the presence of differentiated market equilibria, that are more complex than might otherwise be supposed: different lending or risk management “styles” may emerge and banks strike their own balance between risk-taking and (the cost of) monitoring (that risk).

A bank that lends money to a household faces two types of risk. Frequently mentioned is the risk of default. Seldom referred to is the risk of an early redemption of the loan – leading to dormancy. In this paper, we model the transition of consumer loans from an active to a dormant state. To this end, we use data on 4786 individuals who were granted credit by a Swedish lending institution between September 1993 and August 1995 and estimate a semi-parametric duration model. We analyze the factors that determine the time to maturity on consumer loans and investigate the ability of the model to match the maturities observed in the data. Moreover, we derive the distribution of conditional expected durations of loans and show how a loan application can be evaluated by calculating its expected profit.

In this paper, we use credit rating data from two large Swedish banks to elicit evidence on banks’ loan monitoring ability. For these banks, our tests reveal that banks’ internal credit ratings indeed include valuable private information from monitoring, as theory suggests. Banks’ private information increases with the size of loans. However, our tests also reveal that publicly available information from a credit bureau is not efficiently impounded in the bank ratings: credit bureau ratings predict future movements in bank ratings and improve forecasts of both bankruptcy and loan default. The inefficiency of bank ratings is greater for smaller loans. We investigate possible explanations for these findings. Our results are consistent with bank loan officers placing too much weight on their private information, a form of overconfidence. Risk analyses of the loan portfolios in our data could thus be improved by combining the bank credit ratings with public credit bureau ratings. The methods we use represent a new basket of straightforward techniques that enable both financial institutions and regulators to assess the performance of credit rating systems.

Despite a surge in the research efforts put into modeling credit and default risk during the past decade, few studies have incorporated the impact that macroeconomic conditions have on business defaults. In this paper, we estimate a duration model to explain the survival time to default for borrowers in the business loan portfolio of a major Swedish bank over the period 1994–2000. The model takes both firm-specific characteristics, such as accounting ratios and payment behaviour, loan-related information, and the prevailing macroeconomic conditions into account. The output gap, the yield curve and consumers’ expectations of future economic development have significant explanatory power for the default risk of firms. We also compare our model with a frequently used model of firm default risk that conditions only on firm-specific information. The comparison shows that while the latter model can make a reasonably accurate ranking of firms’ according to default risk, our model, by taking macro conditions into account, is also able to account for the absolute level of risk.

A unique legal reform in 2004 in Sweden redistributed collateral rights from banks holding floating liens to unsecured creditors without changing the value of assets on firms’ balance sheets. Using a country-wide panel of all incorporated firms, we document that a zero-sum redistribution of collateral rights and the resulting reduction in collateral capacity towards banks contracts the amount and maturity of corporate debt and leads firms to slow investment and forego growth. Altering their allocation of assets, firms reduce particularly those assets with a low collateralizable value for banks and also hoard more cash. However, the reform has no impact on corporate capital intensity or efficiency, suggesting that under these newly binding credit constraints firms simply shrink their operations.