Risk Management Tools


We use a broad range of quantitative and qualitative methodologies for assessing and managing risks. As a matter of policy, we continually assess the appropriateness and the reliability of our quantitative tools and metrics in light of our changing risk environment. Some of these tools are common to a number of risk categories, while others are tailored to the particular features of specific risk categories. The advanced internal tools and metrics we currently use to measure, manage and report our risks are:

  • RWA equivalent. This is defined as total risk-weighted assets (“RWA”) plus a theoretical amount for specific allocated Common Equity Tier 1 capital deduction items if these were converted into RWA. RWA form the key factor in determining the bank’s regulatory Capital Adequacy as reflected in the Common Equity Tier 1 capital ratio. RWA equivalents are used to set targets for the growth of our businesses and monitored within our management reporting systems. As a general rule, RWA are calculated in accordance with the currently valid “Basel 2.5” European (CRD) and German legislation (SolvV). However, we also perform additional RWA equivalent calculations under pro-forma Basel 3 rules to support for use within our forward looking risk and capital planning processes.
  • Expected loss. We use expected loss as a measure of our credit and operational risk. Expected loss is a measurement of the loss we can expect induced by defaults within a one-year period from these risks as of the respective reporting date, based on our historical loss experience. When calculating expected loss for credit risk, we take into account credit risk ratings, collateral, maturities and statistical averaging procedures to reflect the risk characteristics of our different types of exposures and facilities. All parameter assumptions are based on statistical averages of up to nine years based on our internal default and loss history as well as external benchmarks. We use expected loss as a tool of our risk management process and as part of our management reporting systems. We also consider the applicable results of the expected loss calculations as a component of our collectively assessed allowance for credit losses included in our financial statements. For operational risk we determine the expected loss from statistical averages of our internal loss history, recent risk trends as well as forward looking estimates.
  • Return on risk-weighted assets (“RoRWA”). In times of regulatory capital constraints, RoRWA has become an important metric to assess our client relationships’ profitability, in particular for credit risk. RoRWA is currently the primary performance measure and as such attracts more attention than the previously used RARoC profitability measure based on economic capital.
  • Value-at-risk. We use the value-at-risk approach to derive quantitative measures for our trading book market risks under normal market conditions. Our value-at-risk figures play a role in both internal and external (regulatory) reporting. For a given portfolio, value-at-risk measures the potential future loss (in terms of market value) that, under normal market conditions, is not expected to be exceeded with a defined confidence level in a defined period. The value-at-risk for a total portfolio represents a measure of our diversified market risk (aggregated, using pre-determined correlations) in that portfolio.
  • Economic capital. Economic capital measures the amount of capital we need to absorb very severe unexpected losses arising from our exposures. “Very severe” in this context means that economic capital is set at a level to cover with a probability of 99.98 % the aggregated unexpected losses within one year. We calculate economic capital for the default, transfer and settlement risk elements of credit risk, for market risk including trading default risk, for operational risk and for business risk.
  • Stress testing. Credit, market and operational risk as well as liquidity risk are subject to a program of regular stress tests. The stress testing framework at Group level comprises regular group-wide stress tests based on a series of benchmark and more severe macroeconomic global downturn scenarios (provided by dbResearch) consistently applied across all risk types, annual reverse and capital plan relevant stress test as well as ad-hoc scenarios. The hot spots of the downturn scenarios are changing over time according to the changes in economic and political environment around the globe. Prior to the assessment of the stress impact the scenarios are discussed and approved by the appropriate governance committees. In addition, since 2012, the stress program feeds into our Living Wills recovery planning project by assessing the stress impact on specifically defined recovery triggers for a range of near-default scenarios before and after the application of recovery measures. In detail, we assess a suite of recovery triggers under stress (Common Equity Tier 1 (“CET1”) capital ratio, Internal Capital Adequacy (“ICA”) ratio, Net Liquidity Position (“NLP”) within the regularly performed benchmark and more severe group-wide stress tests and compare them to the Red-Amber-Green (“RAG”) levels as defined in our risk appetite. The respective RAG levels in normal, warning, crisis situation are defined for CET1 capital ratio [>6.5 %, 6.5 % … 5 %, <5 %], for ICA [>135 %, 135 % … 120 %, <120 %] and for NLP [> € 5 bn, € 5 bn … € 0 bn, < € 0 bn]. For all of the above stress tests, we were able to return to ‘green’ RAG levels for all recovery triggers after the application of recovery measures.
  • We also supplement our risk type specific analysis of credit, market, operational and liquidity risk with stress testing. For credit risk management purposes, we perform stress tests to assess the impact of changes in general economic conditions or specific parameters on our credit exposures or parts thereof as well as the impact on the creditworthiness of our portfolio. For market risk management purposes, we perform stress tests because value-at-risk calculations are based on relatively recent historical data, only purport to estimate risk up to a defined confidence level and assume good asset liquidity. Therefore, they only reflect possible losses under relatively normal market conditions. Stress tests help us determine the effects of potentially extreme market developments on the value of our market risk sensitive exposures, both on our highly liquid and less liquid trading positions as well as our investments. The correlations between market risk factors used in our current stress tests are estimated from historic volatile market conditions and proved to be consistent with those observed during recent periods of market stress. We use stress testing to determine the amount of economic capital we need to allocate to cover our market risk exposure under the scenarios of extreme market conditions we select for our simulations. For operational risk management purposes, we perform stress tests on our economic capital model to assess its sensitivity to changes in key model components, which include external losses. For liquidity risk management purposes, we perform stress tests and scenario analysis to evaluate the impact of sudden stress events on our liquidity position.