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.     

IRB法下公司敞口组合的风险驱动因子计量研究

巴塞尔银行监管委员会针对防范信贷组合信用风险所需要的资本制定的内部评级法,通过风险驱动因子的变化来反映组合回报的变化,并根据风险权重函数,通过风险加权资产转化为与每一项信用风险敞口更准确匹配的资本要求.本文对违约概率、违约损失率、违约敞口、期限因素以及违约相关性等信贷组合信用风险的风险驱动因子的度量进行了综合研究.     

Financial stability and Basel II

The Basel II Advanced Internal Ratings (AIRB) approach is compared to capital requirements set using an equilibrium structural credit risk model. Analysis shows the AIRB approach undercapitalizes credit risk relative to regulatory targets and allows wide variation in capital requirements for a given exposure owing to ambiguity in the definitions of loss given default and exposure at default. In contrast, the Foundation Internal Ratings Based (FIRB) approach may over-capitalize credit risk relative to supervisory objectives. It is unclear how Basel II will buttress financial sector stability as it specifies the weakest regulatory capital standard for large complex AIRB banks.      

A simulation study of Basel II expected loss distributions for a portfolio of credit cards

Credit scoring models have been used traditionally as the basis of decisions to reject or accept credit applications. They are also used to categorize applicants or existing accounts into risk groups. Based on estimates of probability of default (PD), the risk groups may seem well separated. However, by considering distributions on risk elements such as model estimation uncertainty, exposure at default and loss given default, a simulation approach is used to compute Basel II expected loss distributions for a portfolio of credit cards. These show that discrimination between risk groups is not as clear as is immediately suggested simply by PD estimates. Based on these distributions, we also show that measuring extreme credit risk with Value at Risk can lead to considerable underestimation if distributions on these risk elements are not entered into the computation.     

Credit derivatives, capital requirements and opaque OTC markets

In this paper we study the optimal design of credit derivative contracts when banks have private information about their ability in the loan market and are subject to capital requirements. First, we prove that when banks are subject to a maximum loss capital requirement the optimal signaling contract is a binary credit default basket. Second, we show that if credit derivative markets are opaque then banks cannot commit to terminal-date risk exposure, and therefore the optimal signaling contract is more costly. The above results allow us to discuss the potential implications of different capital adequacy rules for the credit derivative markets.     

Stress testing German banks against a global credit crunch

This paper investigates the impact of a global credit crunch on the corporate credit portfolios of large German banks using a two-stage approach. First, a macroeconometric simulation model (NiGEM) is used to forecast the impact of a substantial increase in the cost of business capital for firms worldwide in three particularly export-oriented industry sectors in Germany. Second, the impact of this economic multi-sector stress on bank credit portfolios is captured by a state-of-the-art Credit Metrics-type portfolio model with sector-dependent unobservable risk factors as drivers of the systematic risk. In our assessment of capital ratios, we confirm that both the increase of the capital charge for the unexpected loss and the increase in banks’ expected losses need to be considered. We also find that the availability of granular information at the level of borrower-specific probabilities of default has a significant impact on the stress test results.     

The credit quality channel: Modeling contagion in the interbank market

We propose an algorithm to model contagion in the interbank market via what we term the “credit quality channel”. In existing models on contagion via interbank credit, external shocks to banks often spread to other banks only in case of a default. In contrast, shocks are transmitted also via asset devaluations and deteriorations in the credit quality in our algorithm. First, the probability of default (PD) of those banks directly affected by some shock increases. This increases the expected loss of the credit portfolios of the initially affected banks’ counterparties, thereby reducing the counterparties’ regulatory capital ratio. From a logistic regression we estimate the increase in the counterparties’ PD due to a reduced capital ratio. Their increased PDs in turn affect the counterparties’ counterparties, and so on. This coherent and flexible framework is applied to the bilateral interbank credit exposure of the entire German banking system in order to examine policy questions. For that purpose, we propose to measure the potential cost of contagion of a given shock scenario by the aggregated regulatory capital loss computed in our algorithm.     

The Interaction of the IFRS 9 Expected Loss Approach with Supervisory Rules and Implications for Financial Stability

This paper examines the interaction of the International Financial Reporting Standard (IFRS) 9 expected credit loss (ECL) model with supervisory rules and discusses potential implications for financial stability in the European Union. Compared to the incurred loss approach of IAS 39, the IFRS 9 ECL model incorporates earlier and larger impairment allowances and is more closely aligned with regulatory expected loss. The earlier recognition of credit losses will reduce the build-up of loss overhangs and the overstatement of regulatory capital. In addition, extended disclosure requirements are likely to contribute to more effective market discipline. Through these channels IFRS 9 might enhance financial stability. However, due to the reliance on point-in-time estimates of the main input parameters (probability of default and loss given default) IFRS 9 ECLs will increase the volatility of regulatory capital for some banks. Furthermore, the ECL model provides significant room for managerial discretion. Bank supervisors might play an important role in the implementation of IFRS 9, but too much supervisory intervention bears the risk of introducing a prudential bias into loan loss accounting that compromises the integrity of financial reporting. Overall, the potential benefits of the standard will crucially depend on its proper and consistent application across jurisdictions.     

Macroprudential stress testing of credit risk: A practical approach for policy makers

Drawing on the lessons from the global financial crisis and especially from its impact on the banking systems of Eastern Europe, the paper proposes a new practical approach to macroprudential stress testing. The proposed approach incorporates: (i) macroeconomic stress scenarios generated from both a country specific statistical model and historical cross-country crises experience; (ii) indirect credit risk due to foreign currency exposures of unhedged borrowers; (iii) varying underwriting practices across banks and their asset classes based on their relative aggressiveness of lending; (iv) higher correlations between the probability of default and the loss given default during stress periods; (v) a negative effect of lending concentration and residual loan maturity on unexpected losses; and (vi) the use of an economic risk weighted capital adequacy ratio as the relevant outcome indicator to measure the resilience of banks to materializing credit risk. The authors apply the proposed approach to a set of Eastern European banks and discuss the results.     

Differential default risk among traditional and non-traditional mortgage products and capital adequacy standards

We develop a framework to quantify credit risks of non-traditional mortgage products (NMPs). Ex ante probabilities of default are caused by willingness-to-pay and ability-to-pay problems and the high default rates for NMPs confirm that payment shock is a critical default risk indicator. Monte Carlo simulations are conducted using three correlated stochastic variables (mortgage interest rate, home price, and household income) under normal and stressed economies. Results confirm that the default risk of 2/28 and option ARM contracts requiring a minimum monthly interest payment have a greater probability of default than other mortgage products in all economic scenarios. Additionally, the credit risk of NMPs is primarily systematic risk, suggesting that these products should require higher risk-based capital. Due to the non-linear distribution of credit risk, even the advanced internal-based rating approach of the Basle II framework can understate the risk involved in these NMPs.     

Prototype risk rating system

This paper explores the traditional and prevalent approach to credit risk assessment – the rating system. We first describe the rating systems of the two main credit rating agencies, Standard & Poor's and Moody's. Then we show how an internal rating system in a bank can be organized in order to rate creditors systematically. We suggest adopting a two-tier rating system. First, an obligor rating that can be easily mapped to a default probability bucket. Second, a facility rating that determines the loss parameters in case of default, such as (i) “loss given default” (LGD), which depends on the seniority of the facility and the quality of the gurantees, and (ii) “usage given default” (UGD) for loan commitments, which depends on the nature of the commitment and the rating history of the borrower.     

Modeling severity risk under PD–LGD correlation

In this article, a generic severity risk framework in which loss given default (LGD) is dependent upon probability of default (PD) in an intuitive manner is developed. By modeling the conditional mean of LGD as a function of PD, which also varies with systemic risk factors, this model allows an arbitrary functional relationship between PD and LGD. Based on this framework, several specifications of stochastic LGD are proposed with detailed calibration methods. By combining these models with an extension of CreditRisk+, a versatile mixed Poisson credit risk model that is capable of handling both risk factor correlation and PD–LGD dependency is developed. An efficient simulation algorithm based on importance sampling is also introduced for risk calculation. Empirical studies suggest that ignoring or incorrectly specifying severity risk can significantly underestimate credit risk and a properly defined severity risk model is critical for credit risk measurement as well as downturn LGD estimation.     

Pricing Sovereign Credit Risk of Poland: Evidence from the CDS Market

We analyze the market assessment of sovereign credit risk using a reduced-form model to price the credit default swap (CDS) spreads, thus enabling us to derive values for the probability of default (PD) and loss given default (LGD) from the quotes of sovereign CDS contracts. We compare different specifications of the models allowing for both fixed and time-varying LGD, and we use these values to analyze the sovereign credit risk of Polish debt throughout the period of a global financial crisis. Our results suggest the presence of a low LGD and a relatively high PD during a recent financial crisis.     

A guide to choosing absolute bank capital requirements

Resampling implementation of a stress-scenario approach to estimating portfolio default loss distributions is proposed as the basis for estimates of the appropriate absolute level of economic capital allocations for portfolio credit risk. Estimates are presented for stress scenarios of varying severity and implications of different time horizons are analyzed. Results for a numeraire portfolio are quite sensitive to such variations. Although the analysis is framed in terms of recent proposals to revise regulatory capital requirements for banks, the arguments and results are also relevant for bankers making capital structure decisions.     

Risk-based capital requirements for mortgage loans

We contribute to the debate over the reform of the Basel Accord by developing risk-based capital requirements for mortgage loans held in portfolio by financial intermediaries. Our approach employs simulation of both economic variables that affect default incidence and conditional loss probability distributions. Results indicate that appropriate capital charges for credit risk vary substantially with loan characteristics and portfolio geographic diversification. Hence, rules that offer little risk differentiation, including the current Basel I regime and “standardized” approach proposed in Basel II result in significant divergence between regulatory and economic capital. These results highlight the incentive problems inherent in simplified methods of capital regulation.     

Credit risk assessment of fixed income portfolios using explicit expressions

We propose a model to assess the credit risk features of fixed income portfolios assuming they can be characterized by two parameters: their default probability and their default correlation. We rely on explicit expressions to assess their credit risk and demonstrate the benefits of our approach in a complex leveraged structure example. We show that using expected loss as a proxy for credit risk is misleading as it does not capture the dispersion effects introduced by correlation. The implications of these findings are relevant for improving current risk management practices and for regulation purposes.     

Should bank capital regulation be risk sensitive?

We present a screening model of the risk sensitivity of bank capital regulation. A banker funds a project with uninsured deposits and costly capital. Capital resolves a moral hazard problem in the choice of the probability of default (PD). The project’s loss given default (LGD) is the banker’s private information. The regulator receives a noisy signal about the LGD and imposes a minimum capital requirement. We show that the optimal sensitivity of capital regulation is non-monotonic in the accuracy of risk assessment. If the signal is inaccurate, the regulator should use risk-insensitive capital requirements. Given sufficient accuracy, the regulator should separate types via risk-sensitive capital requirements, reducing the risk-sensitivity of bank capital as accuracy improves.     

Macroeconomic Conditions and the Puzzles of Credit Spreads and Capital Structure

I build a dynamic capital structure model that demonstrates how business cycle variation in expected growth rates, economic uncertainty, and risk premia influences firms' financing policies. Countercyclical fluctuations in risk prices, default probabilities, and default losses arise endogenously through firms' responses to macroeconomic conditions. These comovements generate large credit risk premia for investment grade firms, which helps address the credit spread puzzle and the under‐leverage puzzle in a unified framework. The model generates interesting dynamics for financing and defaults, including market timing in debt issuance and credit contagion. It also provides a novel procedure to estimate state‐dependent default losses.     

Incorporating Collateral Value Uncertainty in Loss Given Default Estimates and Loan-to-value Ratios

We present a model of risky debt in which collateral value is correlated with the possibility of default. The model is then used to study the expected loss given default, primarily as a function of collateral. The results obtained could prove useful for estimating losses given default in many popular models of credit risk which assume them constant. We also examine the problem of determining sufficient collateral to secure a loan to a desired extent. In addition to bank practitioners, regulators might find our analysis useful in reviewing banks’ lending standards relative to current collateral values. In particular, the current proposals for The New (Basel) Capital Accord involve options for the use of banks’ own loss given default estimates which might benefit from the analysis in this paper.     

Credit Derivatives and the Default Risk of Large Complex Financial Institutions

This paper proposes and implements a multivariate model of the coevolution of the first and second moments of two broad credit default swap indices and the equity prices of sixteen large complex financial institutions. We use this empirical model to build a bank default risk model, in the vein of the classic Merton-type, which utilises a multi-equation framework to model forward-looking measures of market and credit risk using the credit default swap (CDS) index market as a measure of the conditions of the global credit environment. In the first step, we estimate the dynamic correlations and volatilities describing the evolution of the CDS indices and the banks’ equity prices and then impute the implied assets and their volatilities conditional on the evolution and volatility of equity. In the second step, we show that there is a substantial ‘asset shortfall’ and that substantial capital injections and/or asset insurance are required to restore the stability of our sample institutions to an acceptable level following large shocks to the aggregate level of credit risk in financial markets.