Answers to the European Commission on the ... - Eiopa - Europa

• sensitivity analyses that have <strong>the</strong> goal <strong>to</strong> test <strong>the</strong> influence of
certain model assumptions and quantify weaknesses of <strong>the</strong> model.
11.34 A number of possible techniques could be used for performing
sensitivity analysis, including, for example:
• analysis of <strong>the</strong> relationship between a full valuation using
scenarios and an approximation using sensitivities;
• analysis of <strong>the</strong> effect of <strong>the</strong> inclusion or deletion of risk drivers;
• analysis of <strong>the</strong> effect of different estimation procedures;
• analysis of <strong>the</strong> effect of <strong>the</strong> observation period of risk drivers; or
• analysis of <strong>the</strong> effect of alternative model assumptions.
Approval & ongoing supervision
Testing <strong>the</strong> actuarial model
11.35 The aim of <strong>the</strong> 'statistical quality test' is <strong>to</strong> ensure that <strong>the</strong> actuarial
internal model has sufficient accuracy and reliability <strong>to</strong> support internal
risk management and computation of <strong>the</strong> SCR. The statistical quality
test includes <strong>the</strong> evaluation of <strong>the</strong> internal consistency of <strong>the</strong>
modelling, <strong>the</strong> reliability and quality of input data, <strong>the</strong> quality of <strong>the</strong>
forecasts provided by <strong>the</strong> model and whe<strong>the</strong>r <strong>the</strong> modelling is in line
with widely accepted minimum standards of actuarial science and
ma<strong>the</strong>matical statistics. Evaluation of forecast performance can be
based on general statistical methodology for <strong>the</strong> evaluation of <strong>the</strong>
quality of distributional forecasts. 106
Testing <strong>the</strong> SCR estimate
11.36 Supervisors should not expect an unattainable degree of precision in
<strong>the</strong> models developed by insurance undertakings. The aim of <strong>the</strong>
106 Statistical methodology for <strong>the</strong> evaluation of forecasts has been developed in a very general context by
Dawid, in <strong>the</strong> context of econometric forecasting (Diebold, Berkowitz) and in <strong>the</strong> context of <strong>the</strong> evaluation
of VaR forecasts (Berkowitz, O’Brien, Finger, Stahl, Overbeck):
Dawid (1986) 'Probability forecasting' – Encyclopedia of Statistical Sciences
Berkowitz (2000) 'Testing density forecasts, with applications <strong>to</strong> risk management' – Journal of Business
and Economic Statistics, Oct 2001.
Berkowitz & O’Brien (2002) 'How accurate are Value-at-Risk models at commercial banks?' – Journal of
Finance 57.
Diebold, Gun<strong>the</strong>r & Tay (1998) 'Evaluating density forecasts with applications <strong>to</strong> financial risk
management' – International Economic Review 39(4).
Chris<strong>to</strong>pher Finger (2005) 'Back <strong>to</strong> Backtesting' – RiskMetrics Group Research Monthly, May 2005.
Overbeck & Stahl (2000) 'Backtesting: Allgemeine Theorie, Praxis und Perspektiven' – Handbuch
Risikomanagement.
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