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The purpose of this paper is to build an easy to implement, pragmatic and parsimonious yet accurate model to determine an exposure at default (EAD) distribution for CCL (contingent credit lines) portfolios.

Using an algorithm similar to the basic CreditRisk+ and Fourier Transforms, the authors arrive at a portfolio level probability distribution of usage.

The authors perform a simulation experiment which illustrates the convolution of two portfolio segments to derive an EAD distribution, chosen randomly from Moody's Default Risk Service (DRS) database of CCLs rated as of 12/31/2008, to derive an EAD distribution. The standard deviation of the usage distribution is found to decrease as we increase the number of puts used, but the mean value remains relatively stable, as the extreme points converge towards the mean to produce a shrinkage in the spread of the distribution. The authors also observe, for the sample portfolio, that an increase in the additional usage rate level also increases the volatility of the associated exposure distribution.

This model, in conjunction with internal bank financial institution research, can be used for banks' EAD estimation as mandated by Basel II for bank CCL portfolios, or implemented as part of a Solvency II process for insurers exposed to credit sensitive unfunded commitments. Apart from regulatory requirements, distributions of stochastic exposure generated can be inputs for different economic capital models and stress testing procedures used to capture an accurate risk profile of the portfolio, as well as providing better insights into the problem of managing liquidity risk for a portfolio of CCLs and similar exposures.

In‐spite of the large volume of CCLs in portfolios of financial institutions all (for commercial banks holding these as well as for insurance companies having analogous exposures), paucity of EAD models, unsuitability of external data and inconsistent internal data with partial draw‐downs have been a major challenge for risk managers as well as regulators in managing CCL portfolios.

This paper aims to analyse the exposure at default (EAD) in the event of multiple banking relationships to understand the differences with respect to solo banking relationships and forecast the banks risk exposure.

The paper uses a unique database provided by the Italian public credit register representative of the full Italian market before the financial crisis. The analysis compares different EAD risk proxies for debtors with unique and multiple banking relationships to underline the main differences among the two groups.

Results show that EAD forecast could be improved considering the existence of exposures with other lenders and banks that consider such type of information can reduce the risk of underestimating the risk exposure of a debtor.

The paper is the first attempt to model the EAD on the basis of the existence of multiple lending exposures. Results demonstrate a different lender’s risk exposure for debtors with multiple credit risk exposure and show the usefulness of the information about the overall system exposure in evaluating the risk exposure related to this type of customers.

The purpose of this paper is to verify recovery risk management capabilities by lessors. It tests several hypotheses and finds out interesting specific results for lessors.

The approach is empirical: two different database of leasing contracts are analysed with econometric methodologies.

There is clear evidence that: lessors are ex ante able to balance the probability of default and the loss given default case by case, using proper contract structures; and they carefully manage recovery procedures and strategies according to operations' characteristics.

The data used are large enough, but come from institutions concentrated in Italy. Future research could be extended to other relevant countries.

Results presented are verified in leasing companies which made a limited use of rating systems and credit risk model: they have been achieved by the continuous improvements of traditional lending practices. The development of modern reliable systems can enhance risk management capabilities; our findings can help building more structured and advanced credit risk management tools.

The paper adds to the literature in the sense that gives clear evidence of a neglected but important fact of real world credit markets: financial intermediaries have the capability of properly assessing risk components and manage loss given default (LGD) in order to control overall credit risk.

A credit risk model suitable for agricultural lenders is identified. The model incorporates sector correlations and is applied to the loan portfolio of an agricultural credit association to create a distribution of loan losses. The distribution is used to derive the lender’s expected and unexpected losses. Results of the analysis indicate that the association is more than adequately capitalized based on 1997S2002 data. Since the capital position of the association is lower than that of most other associations in the Farm Credit System, this raises the issue of overcapitalization in the System.

The purpose of this paper is to provide a method that can better evaluate the credit risk (CR) under PPP project finance.

The principle to evaluate the CR of PPP projects is to calculate three critical indicators: the default probability (DP), the recovery rate (RR) and the exposure at default (EAD). The RR is determined by qualitative analysis according to Standard & Poor’s Recovery Scale, and the EAD is estimated by NPV analysis. The estimation of the DP is the focus of CR assessment because the future cash flow is not certain, and there are no trading records and market data that can be used to evaluate the credit condition of PPP projects before financial close. The modified CreditMetrics model and Monte Carlo simulation are applied to evaluate the DP, and the application is illustrated by a PPP project finance case.

First, the proposed method can evaluate the influence of the project’s cash flow uncertainty on the potential loss of the bank. Second, instead of outputting a certain default loss value, the method can derive an interval of the potential loss for the bank. Third, the method can effectively analyze how different repayment schedules and risk preference of banks influence the evaluating result.

The proposed method offers an approach for the bank to value the CR under PPP project finance. The method took into consideration of the uncertainty and other characteristics of PPP project finance, adopted and improved the CreditMetrics model, and provided a possible loss range under different project cash flow volatilities through interval estimation under certain confident level. In addition, the bank’s risk preference is considered in the CR evaluating method proposed in this study where the bank’s risk preference is first investigated in the CR evaluating process of PPP project finance.

This paper aims to identify factors that affect agricultural mortgage default and prepayment.

Using a sample of farm credit system loans, prepayment and default are modeled as competing risks with potentially non‐stationary covariates using a statistical/econometric technique called survival snalysis (SA).

The analysis suggests that the primary drivers of prepayment and default are the rate of interest charged by the lender at origination and the borrower's current ratio at origination. Tests of the existence of a geographic effect indicate that despite bank management belief to the contrary, branches may not be homogeneous.

This analysis would be improved if more data were available in an easily obtainable manner to control for unobserved heterogeneity. Unobserved heterogeneity or incomplete specification within a model can be problematic. Inferences among regression coefficients can be problematic in that the estimates have inflated variances and unreliable test statistics. In addition, more frequent measures of the time‐varying covariates could be obtained to improve upon the SA models presented above. Future analyses could also incorporate other sections of the agricultural credit association portfolio, as well as a comparison to variable rate notes. One other logical next step would be to obtain loan collateral values to obtain estimates of the exposure at default, and the loss given default, or the estimates needed for the advanced internal ratings based approach described in the Basel Accords.

This paper provides a method for lenders to measure and model mortgage termination, an important consideration for risk managers when determining capital adequacy described in the Basel Accords.

I. INTRODUCTION A typical shortcoming of most current credit portfolio models is the lack of a stochastic modeling of risk factors, such as interest rates or credit spreads, during the revaluation process at the risk horizon. For example, fixed income instruments, such as bonds or loans, are revalued at the risk horizon using the current forward rates and (rating class specific) forward credit spreads for discounting future cash flows. Hence, the stochastic nature of the instrument's value in the future which results from changes in factors other than credit quality is ignored, and the riskiness of the credit portfolio at the risk horizon is underestimated. A further consequence is that correlations between changes of the debtor's default probability and changes of market risk factors and, hence, the exposure at default cannot be integrated into the credit portfolio model. This drawback is especially relevant for portfolios of defaultable market‐driven derivatives. One reason why risk factors not directly related to credit risk are neglected in most current credit portfolio models is that there is still no commonly accepted approach for modeling the credit quality of a debtor and the dependencies between the credit quality changes of different debtors. Hence, it might be over‐ambitious to incorporate correlations between market risk factors and credit quality changes. Even empirical evidence on the sign of the correlation remains inconclusive. Additionally, introducing stochastic market risk factors and modeling the correlation between these risk factors and credit quality changes would significantly increase the computational burden for calculating robust risk measures of credit portfolios.

The Iranian banking industry has been greatly affected by dramatic changes in macroeconomic conditions over the past several decades owing to volatile oil revenues, changing fiscal and monetary policies, and the imposition of US sanctions. The main objective of this paper is to estimate potential credit losses in the Iranian banking sector due to macroeconomic shocks and assess the minimum economic capital requirements under the baseline and distressed scenarios. The paper also contrasts the applications of linear and nonlinear models in estimating the impacts of macroeconomic shocks on financial institutions.

The paper uses a multistage approach to derive the portfolio loss distribution for banks. In the first step, the dynamic relationship between the selected macroeconomic variables are estimated using a VAR model to generate the stress scenarios. In the second step, the default probabilities are estimated using a quantile regression model and the results are compared with those of the conventional linear models. Finally, the default probabilities are simulated for a one-year time horizon using Monte-Carlo method and the portfolio loss distribution is calculated for hypothetical portfolios. The expected loss includes the loss given default for loans drawn randomly and uniformly distributed and exposed at default values when loans are assigned a fixed value.

The results indicate that the loss distributions under all scenarios are skewed to the right, with the linear model results being very similar to those of quantile at the 50% quantile, but very unlike those at the 10% and 90% quantiles. Specifically, the quantile model for the 90% (10%) quantile generates estimates of minimum economic capital requirement that are considerably higher (lower) than those using the linear model.

The study has focused on credit risk because of lack of data on other types of risk at individual bank level. The future studies can estimate the aggregate economic capital using a risk aggregation approach and a panel data (not presently available), which could further improve the accuracy of the estimates.

The fiscal and monetary authorities in developing countries, specially oil-exporting countries, can follow the risk assessment approach to assess the health of their banking system and adapt policies to mitigate the impacts of large macroeconomic shocks on their financial markets.

This is the first paper estimating the portfolio loss distribution for the Iranian banks under turbulent macroeconomic conditions using linear and nonlinear models. The case study can be applied to other developing and emerging countries, particularly those highly dependent on natural resources, prone to extreme macroeconomic shocks.