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Financial Markets and Portfolio Management

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01-11-2014

Stress testing German banks against a global credit crunch

Authors: Klaus Düllmann, Thomas Kick

Published in: Financial Markets and Portfolio Management | Issue 4/2014

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Abstract

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.

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See Committee on the Global Financial System (2000).

See Duellmann and Masschelein (2007).

The homogeneity of the market plays a crucial role for the econometric results. Even if the aggregated data of two markets are quite similar, the results of a stress scenario can differ significantly between them according to the level of their homogeneity. Performing stress tests on the aggregated data of a heterogenous market can thus lead to biased results. Considering the real estate market as an example, one would expect very different effects from a stress scenario on the relatively homogenous German market compared to the US market (including the subprime market).

On combining macroeconomic scenario analysis and portfolio model stress tests, see Segoviano and Padilla (2006).

For estimation of VAR models, see, e.g., Mojon and Peersman (2001) and De Graeve et al. (2008).

All production indices were retrieved from the time-series database of the Deutsche Bundesbank. More information on the variables used in the VAR model is found in Table 1.

The production index for industrial goods is not available according to the sector definition in the portfolio model. Therefore, this index is proxied by three major sub-indices: metal production and processing, electrical engineering, and machine building. The sub-indices are weighted according to the gross value added of each sub-sector.

The investment premium is the wedge between the Bank of America Merrill Lynch Monetary Corporate BBB 7–10 Years and the (“risk-free”) Synthetic Euro Benchmark Bond from Each Monetary Union Member (weighted).

The maximum correlation among the exogenous variables in the VAR model is below 13 %.

With a share of 35 % of the total corporate loan volume of the banks in the sample, the industrial goods sector is the second largest sector, after financial services, in the portfolio model.

The baseline scenario reflects the changes in the production indices under “normal” (i.e., expected) circumstances.

Furthermore, we do not consider a capital charge for any remaining systematic risk in the LGD of defaulted exposures, which would have to be added to UL capital; see Basel Committee on Banking Supervision (2006), para. 471.

NACE codes are the statistical classification of economic activities in the European Community.

The use of borrower units requires a rule to cope with two special cases. First, the case of different default probabilities assigned to different borrower entities and, second, the case of different sector assignments. In the first case, the default probability of the borrower unit is calculated as a credit-volume weighted average. In the second case, the sector of the borrower with the largest loan volume is assigned.

See, e.g., Duellmann and Masschelein (2007) or Heitfield et al. (2006).

There are cases where no default probability is available, for example, because the lender invokes “partial use.” Here, we take the average default probability of all the bank’s borrowers that are in the same industry sector.

The assumption of constant intra-sector correlations can be relaxed. The results, however, are quite robust against this simplifying assumption (Duellmann and Erdelmeier 2009).

See Hahnenstein (2004).

The analysis includes two banks that do not have an approved IRB approach, but that report their PDs nonetheless.

See Sect. 3.3.

In the portfolio model, the Basel II assumption of a LGD of 45 % is applied.

The EBA minimum capital requirement in the exercise had been a core Tier 1 capital ratio of 9 % after deduction of the “sovereign capital buffer.” Note that, the EBA Capital Exercise led to an increase of European banks’ capital positions of more than € 200bn; see EBA Press Release of October 3, 2012.

Basel Committee on Banking Supervision: International convergence of capital measurement and capital standards. A revised framework. Comprehensive version (2006). https://doi.org/www.bis.org/publ/bcbs128.pdf

Bonti, G., Kalkbrener, M., Lotz, C., Stahl, G.: Credit risk concentrations under stress. J. Credit Risk 2(3), 115–136 (2006)CrossRef

Breuer, T., Jandacka, M., Rheinberger, K., Summer, M.: How to find plausible, severe, and useful stress scenarios. Int. J. Cent. Bank. 5(3), 205–224 (2009)

Committee on the Global Financial System: Stress testing of large financial institutions: current practice and aggregation issues. Working paper (2000). https://doi.org/www.bis.org/publ/cgfs14.pdf

De Graeve, F., Kick, T., Koetter, M.: Monetary policy and financial (in)stability: an integrated micro-macro approach. J. Financ. Stab. 4(3), 205–231 (2008)CrossRef

Duellmann, K., Erdelmeier, M.: Crash testing German banks. Int. J. Cent. Bank. 5(3), 139–174 (2009)

Duellmann, K., Masschelein, N.: A tractable model to measure sector concentration risk in credit portfolios. J. Financ. Serv. Res. 32(1–2), 55–79 (2007)CrossRef

Elsinger, H., Lehar, A., Summer, M.: Using market information for banking system risk assessment. Int. J. Cent. Bank. 1, 137–165 (2006)

Hahnenstein, L.: Calibrating the CreditMetrics correlation concept—empirical evidence from Germany. Financ. Mark. Portf. Manag. 18(4), 358–381 (2004)CrossRef

Hanson, S., Pesaran, M., Schuermann, T.: Firm heterogeneity and credit risk diversification. J. Empir. Financ. 15(4), 583–612 (2008)CrossRef

Heitfield, E., Burton, S., Chomsisengphet, S.: Systematic and idiosyncratic risk in syndicated loan portfolios. J. Credit Risk 2(3), 3–31 (2006)CrossRef

Mojon, B., Peersman, G.: A VAR description of the effects of monetary policy in the individual countries of the Euro Area, European Central Bank Working Paper Series, No. 92 (2001)

Schönbucher, P.J.: Factor models: portfolio credit risks when defaults are correlated. J. Risk Financ. 3(1), 45–56 (2001)CrossRef

Segoviano, M., Padilla, P.: Portfolio credit risk and macroeconomic shocks: application to stress testing under data-restricted environments. IMF Working Paper No. 283 (2006)

Title: Stress testing German banks against a global credit crunch
Authors: Klaus Düllmann
Thomas Kick
Publication date: 01-11-2014
Publisher: Springer US
Published in: Financial Markets and Portfolio Management / Issue 4/2014
Print ISSN: 1934-4554
Electronic ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-014-0236-y

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