Daniel Rösch

A Multi-Factor Approach for Systematic Default and Recovery Risk

Banks face the challenge of forecasting losses and loss distributions in relation to their credit risk exposures. Most banks choose a modular approach in line with the current proposals of the Basel Committee on Banking Supervision (2004), where selected risk parameters such as default probabilities, exposures at default and recoveries given default are modelled independently. However, the assumption of independence is questionable. Previous studies have shown that default probabilities and recovery rates given default are negatively correlated [Carey (1998), Hu and Perraudin (2002), Frye (2003), Altman et al. (2005), or Cantor and Varma (2005)]. A failure to take these dependencies into account will lead to incorrect forecasts of the loss distribution and the derived capital allocation.

Default and Recovery Risk Dependencies in a Simple Credit Risk Model

This paper provides evidence for the relationship between credit quality, recovery rate, and correlation. The paper finds that rating grade, rating shift, and macroeconomic factors provide a highly significant explanation for default risk and recovery risk of US bond issues. The empirical data suggest that default and recovery processes are highly correlated. Therefore, a joint approach is required for estimating time-varying default probabilities and recovery rates that are conditional on default. This paper develops and applies such a model.

Forecasting Probabilities of Default and Loss Rates Given Default in the Presence of Selection

This paper offers a joint estimation approach for forecasting probabilities of default and loss rates given default in the presence of selection. The approach accommodates fixed and random risk factors. An empirical analysis identifies bond ratings, borrower characteristics and macroeconomic information as important risk factors. A portfolio-level analysis finds evidence that common risk measurement approaches may underestimate bank capital by up to 17% relative to the presented model.

Capital Incentives and Adequacy for Securitizations

This paper analyzes the capital incentives and adequacy of financial institutions for asset portfolio securitizations. The empirical analysis is based on US securitization rating and impairment data. The paper finds that regulatory capital rules for securitizations may be insufficient to cover implied losses during economic downturns such as the Global Financial Crisis. In addition, the rating process of securitizations provides capital arbitrage incentives for financial institutions and may further reduce regulatory capital requirements. These policy-relevant findings assume that the ratings assigned by rating agencies are correct and can be used to build a test for the ability of Basel capital regulations to cover downturn losses.

Cure events in default prediction

This paper evaluates the resurrection event regarding defaulted firms and incorporates observable cure events in the default prediction of SME. Due to the additional cure-related observable data, a completely new information set is applied to predict individual default and cure events. This is a new approach in credit risk that, to our knowledge, has not been followed yet. Different firm-specific and macroeconomic default and cure-event-influencing risk drivers are identified. The significant variables allow a firm-specific default risk evaluation combined with an individual risk reducing cure probability. The identification and incorporation of cure-relevant factors in the default risk framework enable lenders to support the complete resurrection of a firm in the case of its default and hence reduce the default risk itself. The estimations are developed with a database that contains 5930 mostly small and medium-sized German firms and a total of more than 23000 financial statements over a time horizon from January 2002 to December 2007. Due to the significant influence on the default risk probability as well as the bank’s possible profit prospects concerning a cured firm, it seems essential for risk management to incorporate the additional cure information into credit risk evaluation.

Risikofaktoren und Korrelationen für Bonitätsveränderungen

SummaryOne of the greatest challenges in modeling credit portfolio risk is the issue of correlations between borrowers. Up to now no consistent methodology for identifying correlations exists. In general two approaches are employed: “direct” and “indirect” modeling. While the former specify correlation parameters themselves, indirect models assume that correlations between credit qualities or defaults are due to exposures to common risk factors. Given the values of the risk factors borrowers are assumed to be conditionally independent. However, the identity of these risk factors is still ambiguous. We present a new dynamic approach which identifies these common factors and tests the assumption of conditional independence. Our empirical study supports this assumption. This considerably facilitates Value-at-Risk analyses. Furthermore the results indicate that a dynamic modeling of credit risk should be favored against the prevalent static setting.

Asset portfolio securitizations and cyclicality of regulatory capital

This paper analyzes the level and cyclicality of regulatory bank capital for asset portfolio securitizations in relation to the cyclicality of capital requirements for the underlying loan portfolio as under Basel II/III. We find that the cyclicality of capital requirements is higher for (i) asset portfolio securitizations relative to primary loan portfolios, (ii) Ratings Based Approach (RBA) relative to the Supervisory Formula Approach, (iii) given the RBA for a point-in-time rating methodology relative to a rate-and-forget rating methodology, and (iv) under the passive reinvestment rule relative to alternative rules. Capital requirements of the individual tranches reveal that the volatility of aggregated capital charges for the securitized portfolio is triggered by the most senior tranches. This is due to the fact that senior tranches are more sensitive to the macroeconomy. An empirical analysis provides evidence that current credit ratings are time-constant and that economic losses for securitizations have exceeded the required capital in the recent financial crisis.

Credit Portfolio Loss Forecasts for Economic Downturns

Recent studies find a positive correlation between default and loss given default rates of credit portfolios. In response, financial regulators require financial institutions to base their capital on ‘Downturn’ loss rates given default which are also known as Downturn LGDs. This article proposes a concept for the Downturn LGD which incorporates econometric properties of credit risk as well as the information content of default and loss given default models. The concept is compared to an alternative proposal by the Department of the Treasury, the Federal Reserve System and the Federal Insurance Corporation. An empirical analysis is provided for US American corporate bond portfolios of different credit quality, seniority and security.

Multiyear Dynamics for Forecasting Economic and Regulatory Capital in Banking

The determination of future credit loss distributions constitutes a fundamental challenge in many credit risk applications such as the calculation of economic and regulatory capital as well as the pricing of loans, portfolios or derivatives thereof. Currently, best practice is to assume a one-year risk horizon for the derivation of the credit loss distribution. However, the maturities of most credit risky products exceed one year and the credit loss of the whole product life has to be taken into account. The present article investigates the impact of multi-year forecasts of credit risk parameters such as probabilities of default and correlations on the distribution of future losses to a credit portfolio. Moreover, the implications are demonstrated for collateralized debt obligations.

Accuracy of mortgage portfolio risk forecasts during financial crises

This paper explores whether factor based credit portfolio risk models are able to predict losses in severe economic downturns such as the recent Global Financial Crisis (GFC) within standard confidence levels. The paper analyzes (i) the accuracy of default rate forecasts, and (ii) whether forecast downturn percentiles (Value-at-Risk, VaR) are sufficient to cover default rate outcomes over a quarterly and an annual forecast horizon. Uninformative maximum likelihood and informative Bayesian techniques are compared as they imply different degrees of uncertainty.