Abstract not found

We provide maximum likelihood estimators of term structures of conditional probabilities of bankruptcy over relatively long time horizons, incorporating the dynamics of firm-specific and macroeconomic covariates. We find evidence in the U.S. industrial machinery and instruments sector, based on over 28,000 firm-quarters of data spanning 1971 to 2001, of significant dependence of the level and shape of the term structure of conditional future bankruptcy probabilities on a firm's distance to default (a volatility-adjusted measure of leverage) and on U.S. personal income growth, among other covariates. Variation in a firm's distance to default has a greater relative e#ect on the term structure of future failure hazard rates than does a comparatively sized change in U.S. personal income growth, especially at dates more than a year into the future.

Procyclicality has emerged as a potential drawback to adoption of risk-sensitive bank capital requirements. Systematic risk factors may result in increases (decreases) in bank capital requirements when the economy is depressed (overheated), thereby decreasing (increasing) bank lending capacity and exacerbating business cycle fluctuations. Procyclicality may result from systematic risk emanating from common macroeconomic influences or from interdependencies across firms as financial markets and institutions consolidate internationally. We survey the literature on cyclical effects on operational risk, credit risk and market risk measures. Incorporating Systemic Influences Into Risk Measurements: A Survey of the Literature Bank regulations focus on individual institutions. The Basel Capital Accords (both current and proposed) base international bank capital requirements on the measurement of risk for each individual bank. Aggregate capital levels are then obtained by simply adding each bank’s individual capital requirement. To the extent that there is any attention paid to aggregate capital levels at all, it is only as a means to calibrate the

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This paper provides a synthesis and further development of a global modelling approach introduced in Pesaran, Schuermann and Weiner (2004), where country speci…c models in the form of VARX * structures are estimated relating a vector of domestic variables, xit, to their foreign counterparts, x it, and then consistently combined to form a Global VAR (GVAR). It is shown that the VARX * models can be derived as the solution to a dynamic stochastic general equilibrium (DSGE) model where over-identifying long-run theoretical relations can be tested and imposed if acceptable. This gives the system a transparent long-run theoretical structure. Similarly, short-run over-identifying theoretical restrictions can be tested and imposed if accepted. Alternatively, if one has less con…dence in the short-run theory the dynamics can be left unrestricted. The assumption of the weak exogeneity of the foreign variables for the long-run parameters can be tested, where x it variables can be interpreted as proxies for global factors. Rather than using deviations from ad hoc statistical trends, the equilibrium values of the variables re‡ecting the long-run theory embodied in the model can be calculated. This approach has been used in a wide variety of contexts and for a wide variety of purposes. The paper also provides some new results. Keywords: Global VAR (GVAR), DSGE models, VARX*.

This paper introduces a novel approach for dealing with the ‘curse of dimensionality ’in the case of large linear dynamic systems. Restrictions on the coefficients of an unrestricted VAR are proposed that are binding only in a limit as the number of endogenous variables tends to infinity. It is shown that under such restrictions, an infinite-dimensional VAR (or IVAR) can be arbitrarily well characterized by a large number of finite-dimensional models in the spirit of the global VAR model proposed in Pesaran et al. (JBES, 2004). The paper also considers IVAR models with dominant individual units and shows that this will lead to a dynamic factor model with the dominant unit acting as the factor. The problems of estimation and inference in a stationary IVAR with unknown number of unobserved common factors are also investigated. A cross section augmented least squares estimator is proposed and its asymptotic distribution is derived. Satisfactory small sample properties are documented by Monte Carlo experiments.

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This paper examines the impact of neglected heterogeneity on credit risk. We show that neglecting heterogeneity in firm returns and/or default thresholds leads to underestimation of expected losses (EL), and its effect on portfolio risk is ambiguous. Once EL is controlled for, the impact of neglecting parameter heterogeneity is complex and depends on the source and degree of heterogeneity. We show that ignoring differences in default thresholds results in overestimation of risk, while ignoring differences in return correlations yields ambiguous results. Our empirical application, designed to be typical and representative, combines both and shows that neglected heterogeneity results in overestimation of risk. Using a portfolio of U.S. firms we illustrate that heterogeneity in the default threshold or probability of default, measured for instance by a credit rating, is of first order importance in affecting the shape of the loss distribution: including ratings heterogeneity alone results in a 20 % drop in loss volatility and a 40 % drop in 99.9 % VaR, the level to which the risk weights of the New Basel Accord are calibrated.

Abstract not found

A drawback of available portfolio credit risk models is that they fail to allow for default risk dependency across loans other than through common risk factors. Thereby, these models ignore that close ties can exist between companies due to legal, financial and business relations. In this paper, we integrate the insights from theoretical models of default correlation into a commonly used model of default and portfolio credit risk by explicitly allowing for dependencies between firm defaults through both common factors and industry specific disturbances in a duration model. An application using pooled data from two Swedish banks’ business loan portfolios over the period 1994-2000 shows that estimates of individual default risk are little affected by including industry specific errors. However, accounting for the within-industry correlation of defaults increases estimates of VaR by 50-200 percent. We show that the model we propose manages to follow both the trend in credit losses and produce industry driven, time-varying, fluctuations in losses around that trend. A conventional model that contains only systematic factors as drivers of default correlation, although able to fit the broad trends in credit losses, cannot match these fluctuations because it fails to