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Allianz Annual Report 2013

Annual Report 2013    Allianz Group108 Internal risk capital framework We define internal risk capital as the capital required to protect us against unexpected, extreme economic losses. On a quarterly basis, we calculate and aggregate internal risk capital across all business segments – providing a common standard for measuring and com- paring risks across the wide range of different activities that we undertake as an integrated financial services provider. General approach We utilize an internal risk capital model for the management of our risk and solvency position and are working towards meeting the forthcoming Solvency II internal model requirements. Our model is based on a best practice technical platform with an up-to-date meth- odology covering all modeled sources of quantifiable risks. This forms an integral part of our internal risk capital framework. The model framework is regularly assessed by the European College of supervisors in the course of the internal model pre-application pro- cess of Solvency II. Internal Risk Capital Model Our internal risk capital model is based on a Value-at-Risk (VaR) approach using a Monte Carlo simulation. Following this approach, we determine the maximum loss in the portfolio value of our busi- nesses in the scope of the model within a specified timeframe (“hold- ing period”) and probability of occurrence (“confidence level”). We assume a confidence level of 99.5 % and apply a holding period of one year. In the risk simulation, we consider market, credit, insurance and other business events (“sources of risk”) and calculate the port- folio value based on the net fair value of assets and liabilities under potentially adverse conditions. The required internal risk capital is defined as the difference between the current portfolio value and the portfolio value under adverse conditions dependent on the 99.5 % confidence level. Because we consider the impact of a negative or positive event on all sources of risks and covered businesses at the same time, all diversification effects across products and regions are taken into account. The results of our Monte Carlo simulation allow us to analyze our expo- sure to each source of risk, both separately and in aggregate. In addi- tion, for market risks we analyze several pre-defined stress scenarios based either on historically observed market movements or on hypo- thetical market movement assumptions. The modeling approach we apply therefore enables us to identify scenarios that have a positive impact on our solvency situation. Yield curve and liquidity premium assumptions When calculating the fair values of assets and liabilities, the assump- tions regarding the underlying risk-free yield curve are crucial in determining future cash flows and how to discount them. We apply the methodology as provided by the European Insurance and Occu- pational Pensions Authority (EIOPA) based on the latest guidance for theextensionoftherisk-freeinterestratecurvesbeyondthelastliquid tenor. In addition, we adjust the risk-free yield curves for the Life/ Health business segment to make allowance for a liquidity premium. Valuation assumption: replicating portfolios Since efficient valuation and advanced, timely analysis is desired, we replicate the liabilities of our Life/Health insurance business. This technique enables us to represent all options and guarantees, both contractual and discretionary, by means of standard financial instru- ments. Using the replicating portfolio we determine and revalue these liabilities under all potentially adverse Monte Carlo scenarios. Diversification and correlation assumptions Our internal risk capital model considers concentration, accumula- tion and correlation effects when aggregating results at Group level, in order to reflect the fact that not all potential worst-case losses are likelytomaterializeatthesametime.Thiseffectisknownasdiversifica­ tion and forms a central element of our risk management framework. We strive to diversify the risks to which we are exposed in order to limit the impact of any single source of risk and help increase the chances that the positive developments outweigh the negative. The degree to which diversification can be realized depends in part on the level of relative concentration of those risks as well as the joint move- ment of sources of risk. Where possible, we derive correlation parameters for each pair of market risks through statistical analysis of historical market data, considering weekly observations over several years. In case historical market data or other portfolio-specific observations are insufficient or not available, correlations are set according to a well-defined, Group-wide process. Correlations are determined by the Correlation Settings Committee, which combines the expertise of risk and busi- nessexperts.Ingeneral,wesetthecorrelationparameterstorepresent thejointmovementofrisksunderadverseconditions.Basedonthese correlations, we use an industry-standard approach, the Gaussian copula approach, to determine the dependency structure of quantifi- able sources of risk within the applied Monte Carlo simulation. Actuarial assumptions Our internal risk capital model also includes non-market assump- tions on claims trends, inflation, mortality, longevity, morbidity, policyholderbehavior,expense,etc.Weuseourowninternalhistorical data for actuarial assumptions wherever possible and also consider recommendations from the insurance industry, supervisory author- ities and actuarial associations. The derivation of our actuarial assumptions is based on generally accepted actuarial methods. Within our internal risk capital and financial reporting framework comprehensive processes and controls exist for ensuring the reliabil- ity of those assumptions.1 1 For additional information regarding our internal controls over financial reporting, please refer to the chapter Controls over Financial Reporting and Risk Capital from page 123 onwards.

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