Xtreme Credit Risk Models: Implications for Bank Capital Buffers

Allen, David E.; Kramadibrata, Akhmad R; Powell, Robert; Singh, Abhay Kumar

doi:10.2139/ssrn.1773363

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…De plus, ces paramètres sont obtenus principalement à partir de données comptables, ce qui veut dire qu'ils ne tiennent pas compte des informations de marché (qui incluent les anticipations des investisseurs) et ne sont donc pas prospectifs. -L'absence de sous-additivité du RWA 6 incite les banques à concentrer leurs expositions sur certaines catégories d'actifs, notamment sur ceux qui ont une pondération de risque RW sous-estimée, ce qui peut potentiellement accroître leur « vrai » risque de crédit.…”

Section: Dédicaceunclassified

Évolution des méthodes de gestion des risques dans les banques sous la réglementation de Bale III : une étude sur les stress tests macroprudentiels en Europe

2020

Action Publique

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Chapitre 2. La gestion des risques bancaires sous la réglementation de Bâle II………………………………………………………………………………..33 2.1. Les trois piliers de Bâle II……………………………...………………..33 2.2. Les méthodes utilisées et la définition des paramètres dans le cadre du risque de crédit………………………………………………………………....35 2.3. Les distinctions à faire sous Bâle II………………………….

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Section: Dédicaceunclassified

Évolution des méthodes de gestion des risques dans les banques sous la réglementation de Bale III : une étude sur les stress tests macroprudentiels en Europe

2020

Action Publique

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“…Using actual returns provides us with only a limited number of extreme returns with which to model the quantiles. To increase the richness of the data we use Monte Carlo simulation to generate 20,000 simulated asset returns for every company in our dataset (see Allen, Kramadibrata, Powell & Singh, 2010, 2011aand Allen, Boffey & Powell, 2011. This is done by generating 20,000 random numbers based on the standard deviation and mean of historical asset returns.…”

Section: Quantile Regressionmentioning

confidence: 99%

Are credit ratings a good measure of capital adequacy?

Allen

Kramadibrata

Powell

et al. 2011

Chan, F., Marinova, D. And Anderssen, R.S. (Eds) MODSIM2011, 19th International Congress on Modelling and Simulation.

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Focus on capital adequacy intensified since the onset of the Global Financial Crisis (GFC), with many US and other global banks experiencing capital shortages over this time. The Basel standardised approach uses credit ratings as a determinant for corporate capital adequacy requirements. A problem with credit ratings is that they were designed to be a measure of relative, as opposed to absolute credit risk, and do not ratchet up or down with changes in economic circumstances. This paper examines how credit risk as indicated by credit ratings (and their associated capital requirement) changed pre and post Global Financial Crisis for US firms, as compared to market measures of credit risk including credit default swaps and fluctuating market asset values. The increases in credit risk shown by the credit ratings and the market indicators are compared to actual bad debt levels of banks.We use an extensive database comprising investment as well as speculative grade US firms. In order to measure the fluctuations in market asset values of these firms, we apply quantile regression and Monte Carlo simulation to the Merton structural credit model. This model uses asset fluctuations in conjunction with balance sheet structure to estimate Distance to Default (DD) and Probability of Default (PD). The use of quantile regression allows modelling of the extreme quantiles of a distribution which facilitates measurement of PDs at the most extreme points of downturn, when companies are most likely to fail. The study shows how the quantile analysis can be used to estimate capital buffers required by banks to deal with increases in credit risk.We find that changes in capital requirements over the GFC as measured by credit ratings are very small in relation to the increase in credit risk identified by market based measures and our quantile regression analysis. These findings can be important to banks and regulators in determining capital adequacy in volatile economic circumstances.

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“…As noted in Section 1, this model recognises that customers of equal credit rating may transition differently depending on their industry risk, but a study by APRA (1999) showed that banks did not favour using macroeconomic factors in their modelling due to complexities involved. Our own iTransition model (Allen, Kramadibrata, Powell, & Singh, 2011;Allen & Powell, 2009 CreditPortfolioView, but incorporates equity VaR instead of macroeconomic variables to derive industry adjustments. This is done by calculating market VaR for each industry, then calculating the relationship between market VaR and credit risk for each industry, using the Merton model to calculate the credit risk component.…”

Section: Itransition Modelmentioning

confidence: 99%

Innovative transition matrix techniques for measuring extreme risk: an Australian and U.S. comparison

Allen

Powell

Singh

2011

Chan, F., Marinova, D. And Anderssen, R.S. (Eds) MODSIM2011, 19th International Congress on Modelling and Simulation.

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Comparing Australia and the U.S. both prior to and during the Global Financial Crisis (GFC), using a dataset which includes more than six hundred companies, this paper modifies traditional transition matrix credit risk modelling to address two important issues. Firstly, extreme credit risk can have a devastating impact on financial institutions, economies and markets as highlighted by the GFC. It is therefore essential that extreme credit risk is accurately measured and understood. Transition matrix methodology, which measures the probability of a borrower transitioning from one credit rating to another, is traditionally used to measure Value at Risk (VaR), a measure of risk below a specified threshold. An alternate measure to VaR is Conditional Value at Risk (CVaR), which was initially developed in the insurance industry and has been gaining popularity as a measure of extreme market risk. CVaR measures those risks beyond VaR. We incorporate CVaR into transition matrix methodology to measure extreme credit risk. We find significant differences in the VaR and CVaR measurements in both the US and Australian markets, as CVaR captures those extreme risks that are ignored by VaR. We also find a greater differential between VaR and CVaR for the US as compared to Australia, reflecting the more extreme credit risk that was experienced in the US during the GFC.

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Xtreme Credit Risk Models: Implications for Bank Capital Buffers

Cited by 5 publications

References 27 publications

Évolution des méthodes de gestion des risques dans les banques sous la réglementation de Bale III : une étude sur les stress tests macroprudentiels en Europe

Évolution des méthodes de gestion des risques dans les banques sous la réglementation de Bale III : une étude sur les stress tests macroprudentiels en Europe

Are credit ratings a good measure of capital adequacy?

Innovative transition matrix techniques for measuring extreme risk: an Australian and U.S. comparison

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