79,249 research outputs found

    Forecasting Credit Portfolio Risk

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    The main challenge of forecasting credit default risk in loan portfolios is forecasting the default probabilities and the default correlations. We derive a Merton-style threshold-value model for the default probability which treats the asset value of a firm as unknown and uses a factor model instead. In addition, we demonstrate how default correlations can be easily modeled. The empirical analysis is based on a large data set of German firms provided by Deutsche Bundesbank. We find that the inclusion of variables which are correlated with the business cycle improves the forecasts of default probabilities. Asset and default correlations depend on the factors used to model default probabilities. The better the point-in-time calibration of the estimated default probabilities, the smaller the estimated correlations. Thus, correlations and default probabilities should always be estimated simultaneously. --asset correlation,bank regulation,Basel II,credit risk,default correlation,default probability,logit model,probit model

    Macro-economy in models for default probability.

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    We inspect the question how to adapt to macro-economical variables those probability of default (PD) estimates where Merton's model assumptions cannot be used. The need for this is to obtain trustworthy estimates of PD from a given economical situation. The structure of a known market-credit risk model is adapted. The key concept in this adaptation is the assumption of a different probabilistic situation for a firm before and at (first) default. If a corporate firm defaults we use a different probabilistic relation between macro-economical and market risk than in a firm's normal not default operation. We found a remarkable resemblance between relativity of physical space-time and the economical framework of variables. This means a solution of the calibration problem without using a Gaussian distribution estimates of the default probability.English

    Single and joint default in a structural model with purely discontinuous assets

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    Structural models of credit risk are known to present both vanishing spreads at very short maturities and a poor spread fit over longer maturities. The former shortcoming, which is due to the diffusive behavior assumed for asset values, can be circumvented by considering discontinuous assets. In this paper we resort to a pure jump process of the Variance-Gamma type. First we calibrate the corresponding Merton type structural model to single-name data for the DJ CDX NA IG and CDX NA HY components. By so doing, we show that it circumvents also the diffusive structural models difficulties over longer horizons. In particular, it corrects for underprediction of low risk spreads and overprediction of high risk ones. Then we extend the model to joint default, resorting to a recent formulation of the VG multivariate model and without superimposing a copula choice. We fit default correlation for a sample of CDX NA names, using equity correlation. The main advantage of our joint model with respect to the existing non diffusive ones is that it allows calibration without the equicorrelation assumption, but still in a parsimonious way. As an example of the default assessments which the calibrated model can provide, we price a FtD swap.credit risk, structural models, LĂ©vy asset prices, default probability, joint default.

    Responding to the Eurozone Crisis - Applying the Shadow Rating Approach to Determine Economic Capital for Sovereign Exposures

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    The recent European sovereign-debt crisis has made it clear that exposures towards sovereigns contain credit risk. However, according to the Basel framework's standardized approach banks are not required to hold any regulatory capital for highly rated sovereigns. In response, this thesis develops a shadow rating approach model for sovereign probability of default estimation, subsequently determining economic capital for sovereign exposures within a foundation internal ratings-based framework. Furthermore, the empirical Bayes estimator is utilized for low-default portfolio probability of default calibration. The model is tested on ve homogeneous sub-segments in addition to the entire dataset at hand. Empirical ndings suggest that the full dataset performs adequately overall. Nonetheless, model performance is superior for accurately constructed sub-segments. In addition, economic, monetary and political indicators as well as banking sector health are found to best replicate S&P's sovereign long-term issuer credit ratings

    Affine term structure models : a time-changed approach with perfect fit to market curves

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    We address the so-called calibration problem which consists of fitting in a tractable way a given model to a specified term structure like, e.g., yield or default probability curves. Time-homogeneous jump-diffusions like Vasicek or Cox-Ingersoll-Ross (possibly coupled with compounded Poisson jumps, JCIR), are tractable processes but have limited flexibility; they fail to replicate actual market curves. The deterministic shift extension of the latter (Hull-White or JCIR++) is a simple but yet efficient solution that is widely used by both academics and practitioners. However, the shift approach is often not appropriate when positivity is required, which is a common constraint when dealing with credit spreads or default intensities. In this paper, we tackle this problem by adopting a time change approach. On the top of providing an elegant solution to the calibration problem under positivity constraint, our model features additional interesting properties in terms of implied volatilities. It is compared to the shift extension on various credit risk applications such as credit default swap, credit default swaption and credit valuation adjustment under wrong-way risk. The time change approach is able to generate much larger volatility and covariance effects under the positivity constraint. Our model offers an appealing alternative to the shift in such cases.Comment: 44 pages, figures and table

    IMF-Supported Adjustment Programs: Welfare Implications and the Catalytic Effect

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    The author studies the welfare implications of adjustment programs supported by the International Monetary Fund (IMF). He uses a model where an endogenous borrowing constraint, set up by international lenders who will never lend more than a debt ceiling, forces the borrowing economy to always choose repayment over default. The immediate potential welfare cost of joining a program is driven by IMF conditionality: to be able to borrow from the IMF, the country has to submit to limits on the consumption of public goods. The benefits derive from the additional borrowing from the IMF (at a lower interest rate) and/or through a "catalytic effect" on private loans, which facilitates consumption smoothing over time. Simulations of the dynamic model in two institutional environments -- with and without the IMF -- are compared. Results indicate that when conditionality forces the country to save more, at a cost that does not prevent it from joining an IMF program, the resulting lower probability of default can induce private lenders to relax their borrowing constraints. Based on a calibration of the model for the Brazilian economy, the overall welfare gains associated with IMF programs are relatively small.International topics

    An extension of Davis and Lo's contagion model

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    International audienceThe present paper provides a multi-period contagion model in the credit risk field. Our model is an extension of Davis and Lo's infectious default model. We consider an economy of n firms which may default directly or may be infected by other defaulting firms (a domino effect being also possible). The spontaneous default without external influence and the infections are described by not necessarily independent Bernoulli-type random variables. Moreover, several contaminations could be required to infect another firm. In this paper we compute the probability distribution function of the total number of defaults in a dependency context. We also give a simple recursive algorithm to compute this distribution in an exchangeability context. Numerical applications illustrate the impact of exchangeability among direct defaults and among contaminations, on different indicators calculated from the law of the total number of defaults. We then examine the calibration of the model on iTraxx data before and during the crisis. The dynamic feature together with the contagion effect seem to have a significant impact on the model performance, especially during the recent distressed period

    Macro-economy in models for default probability.

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    We inspect the question how to adapt to macro-economical variables those probability of default (PD) estimates where Merton's model assumptions cannot be used. The need for this is to obtain trustworthy estimates of PD from a given economical situation. The structure of a known market-credit risk model is adapted. The key concept in this adaptation is the assumption of a different probabilistic situation for a firm before and at (first) default. If a corporate firm defaults we use a different probabilistic relation between macro-economical and market risk than in a firm's normal not default operation. We found a remarkable resemblance between relativity of physical space-time and the economical framework of variables. This means a solution of the calibration problem without using a Gaussian distribution estimates of the default probability
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