swiss solvency test n.
Skip this Video
Loading SlideShow in 5 Seconds..
Swiss Solvency Test PowerPoint Presentation
Download Presentation
Swiss Solvency Test

Swiss Solvency Test

228 Views Download Presentation
Download Presentation

Swiss Solvency Test

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Swiss Solvency Test Philipp Keller, Federal Office of Private Insurance Brussels, 14 October 2005

  2. Contents • Designing a Solvency System • Basic Requirements • Principles vs Rules • Concept of the Swiss Solvency Test • The SST Standard Model • Experiences from the Field Tests • Internal Models • Group Aspects

  3. Designing a Solvency Test • Basic requirements of a risk-based solvency system: • Requirements and definitions have to follow from regulatory purpose (e.g. what is the risk margin for, what purpose does the solvency capital requirement (SCR) have, etc.) • If internal models are to be used, the solvency system should be defined by underlying principles • Building blocks have to fit together (SCR has to be linked to risk margin to avoid double counting of risks, the valuation of assets and liabilities has to be consistent, …) • If the solvency system has to be embedded within insurance companies, it has to be founded on economic principles • Avoid nontransparent mix of quantification of risks, limited eligibility of capital, limits and implicit safety margins which would make interpretation of SCR impossible

  4. Designing a Solvency Test • A risk based solvency system has to address the needs of different stakeholders: • Regulators: policyholder protection, setting capital requirements, incentive for risk management, obtaining a view on companies’ risk culture and risk awareness, achieving a competitive market place, … • Policyholders,Investors, Shareholders, Brokers,…: Comparison of the financial situation of different companies, information on the efficiency of risk management, ALM etc. • Insurers (CEO, CRO, CFO, Actuaries): Comparison with peers, use of internal models, analysis of contribution of different risk types, … A risk based solvency system has many purposes and needs to be a reasonable compromise to satisfy the needs of many stakeholders

  5. Designing a Solvency Test Define risk bearing capital, e.g. value of assets less best-estimate of liabilities, define which forms of capital are eligible Definition of Capital Define valuation methodology of assets and liabilities Valuation Decide over which time horizon risk capital needs to cover risks Time Horizon Define risk typology, decide which risks are quantified and which treated qualitatively Risks to Quantify Definition of Ruin Define risk margin  in SST: risk margin = lowest acceptable level of risk bearing capital Risk Measure Define measurement of risks, e.g. Value at Risk, Expected Shortfall, etc. In the last stage, define standard model (if necessary), details of the system, e.g. correlations vs copulas, etc. Operational Implementation

  6. Implications of Principles vs Rules Principle-based standards describe the objective sought in general terms and require interpretation according to the circumstance. Companies tailor approach such that clearly stated objective is attained Objective can be attained if companies interpret principles faithfully. The objective is defined by a (theoretically) correct quantification and allocation of group diversification Principle-based Objective Risk Based Capital Requirement = Rule-based Objective Rule based approach does not allow truly company specific risk assessment Attained result depends on how well rules capture the situation of the insurer

  7. Contents • Designing a Solvency System • Concept of the Swiss Solvency Test • Principles • Quantified Risks • Risk Measure • Valuation • Risk Margin • Change in Risk Bearing Capital • Capital • The SST Standard Model • Experiences from the Field Tests • Internal Models • Group Aspects

  8. The SST Concept: Principle-Based The more laws and order are made prominent, the more thieves and robbers there will be, Lao-tzu Core of the Solvency Test Principles Definitions Glossary Guidelines Standard Model The SST is defined not by the Standard Model but by underlying principles • Principles define concisely the objectives • Definition of terms and concepts so that meaning and possible interpretation of principles become clear • Guidelines help in interpretation • Standard Model allows use of Solvency Test also by small companies

  9. Principles defining the SST • All assets and liabilities are valued market consistently • Risks considered are market, credit and insurance risks • Risk-bearing capital is defined as the difference of the market consistent value of assets less the market consistent value of liabilities plus the risk margin • Target capital is defined as the sum of the Expected Shortfall of change of risk-bearing capital within one year at the 99% confidence level plus the risk margin • The SST defines an insurer’s capital adequacy if its target capital is less than its risk bearing capital • The scope of SST is legal entity and group / conglomerate level domiciled in Switzerland • Scenarios defined by the regulator as well as company specific scenarios have to be evaluated and, if relevant, aggregated within the target capital calculation • All relevant probabilistic states have to be modeled probabilistically • Partial and full internal models can and should be used • The internal model has to be integrated into the core processes within the company • SST Report to supervisor such that a knowledgeable 3rd party can understand the results • Disclosure of methodology of internal model such that a knowledgeable 3rd party can get a reasonably good impression on methodology and design decisions • Senior Management is responsible for adherence to principles Defines Output Defines How-to Transpar-ency Responsi-bility

  10. Risk Measures: Expected Shortfall The Expected Shortfall of a random variable X to the confidence level 1- (ES) is given by ES[X] =1/  · E[ max( X- VaR[X], 0 )] + VaR[X] Expected Shortfall is a coherent risk measure Shareholder: Only default or non-default is relevant not how bad the state of the insurer is in case of default as shareholders have a put-option on the insurer (Merton)  Value-at-Risk is appropriate Policy Holder: In case of default, it matters how much capital is left  Expected Shortfall is more appropriate than VAR • From the perspective of an insurance regulator, Expected Shortfall has advantages compared to Value at Risk • For an insurer, Expected Shortfall has advantage of being coherent: • Allocation of risk and risk management of subunits is possible • ES is easier to explain to management: • ES1%=average one-in-a-hundred-years loss • VaR1% = the loss that is in 99-out-of-a-100-years not exceeded

  11. The SST Concept: The economic view How to measure risks? • Accounting risk or economic risk? Reported earnings follow the rules and principles of accounting. The results do not always create measures consistent with underlying economics. However, corporate management’s performance is generally measured by accounting income, not underlying economics. Therefore, risk management strategies are directed at accounting, rather than economic performance. Enron in-house risk-management handbook For a risk-based solvency system, risks need to be measured objectively and consistently → economic risk rather than accounting risk →Market Consistent Valuation of Assets and Liabilities

  12. The SST Concept: The economic view Market Consistent Assets Liabilities Wherever possible, market-consistent valuation is based on observable market prices (marking to market) If such values are not available, a market-consistent value is determined by examining comparable market values, taking account of liquidity and other product-specific features, or on a model basis (marking to model) Market-consistent means that up to date values are used for all parameters Best-estimate = Expected value of liabilities, taking into account all up to date information from financial market and from insurance. All relevant options and guarantees have to be valued. No explicit or implicit margins Discounting with risk-free interest rate Best-Estimate Provisions Market consistent provisions Risk Margin Risk bearing capital Risk Margin for inherent risk in liability portfolio Valuation of policyholder-options: Assume realistic behavior of policy holders, but option exercise depends on financial market parameters One approach to value options is using replicating portfolio of traded financial instruments

  13. The SST Concept: The SST in a Nutshell Expected Shortfall Target Capital Risk Margin Change of Risk-Bearing Capital Covers risks emanating during a one-year time horizon Risk margin should be sufficient to allow a run-off or portfolio transfer

  14. The SST Concept: Risk Margin Risk Margin: to cover policyholders against risks emanating beyond 1 year The Risk Margin and SCR/ES cover different risks: • SCR / ES: To cover risks which emanate during a 1-year time horizon • Risk Margin: To cover risks during the whole run-off of the portfolio There should not be double-counting between SCR and Risk Margin • Possible Approaches: • Statutory: Taking undiscounted reserves, using prudent assumptions, adding a simple factor on best-estimate etc. • Quantile: Taking e.g. the 75% quantile of the ultimate loss distribution of the liabilities: Used by APRA for P&C liabilities, discussed within Solvency 2. Risk Margin Best Estimate Provisions • Market Value Margin: the additional amount on top of the best estimate which is required by a willing buyer in an arms-length transaction to assume the liabilities the loss reserves are held to meet: Discussed within Fair Value Accounting, used (partly) within the SST.

  15. The SST Concept: Risk Margin Definition:The risk margin is the smallest amount of capital which is necessary in addition to the best-estimate of the liabilities, so that a buyer would be willing to take over the portfolio of assets and liabilities. Idea: A buyer (or a run-off company) needs to put up regulatory capital during the run-off period of the portfolio of assets and liabilities  a potential buyer needs to be compensated for the cost of having to put up regulatory capital Risk Margin = cost of capital of the present value of future regulatory risk capital associated with the portfolio of assets and liabilities Problem: How to determine future regulatory capital requirement during the run-off of the portfolio of assets and liabilities? -> Assumptions on the evolution of the asset portfolio are necessary

  16. The SST Concept: Risk Margin Key Idea: • The insurer setting up the risk margin should not be penalized if, after the transfer, the insurer taking over the portfolio does not minimize the regulatory risk capital requirements as fast as possible. • The insurer taking over the portfolio of assets and liabilities should be compensated if the insurer setting up the risk margin invested in an illiquid asset portfolio. Assets: Assume that initial asset portfolio is rebalanced such that it matches optimally the liabilities. The speed of the rebalancing is constrained by liquidity of assets (it takes longer to liquidate for real estate than for government bonds). The time until the optimal replicating asset portfolio is achieved depends on the asset mix. Liabilities: Assume no new business

  17. The SST Concept: Risk Margin ES at t=0 does not enter calculation of the risk margin necessary at t=0  risks taken into account for 1-year risk capital and risk margin are completely disjoint and there is no double-counting ES with portfolio converging from actual to replicating portfolio taking into account illiquidity of assets  Sequence of Achievable Replicating Portfolios ES with optimally replicating asset portfolio Achievable Replicating Portfolio has converged to Replicating Portfolio t=0 t=1 t=2 t=3 Years ES: 1-Period (e.g. 1 year) risk capital = Expected Shortfall of risk-bearing capital Future ES entering calculation of risk margin at t=0

  18. Risk Margin Risk Margin / Best Estimate vs Risk Margin / ES[RBC], based on provisional data of Field Test 2005 Life companies writing predominately risk products Life companies writing predominately savings products

  19. Change in Risk Bearing Capital State 1.1 known / deterministic State 31.12 unknown / stochastic Assets Liabilities Assets Liabilities New business during one year (deterministic) Changes in value of liabilities + claims during 1 year (stochastic) RBC(0) RBC(1) Investment profit (above risk-free) + known payoffs (deterministic) Changes in value of assets (stochastic) Year 0 Year 1 The SST requires the quantification of the randomness of risk bearing capital in one year (the probability distribution of RBC). From this follows the determination of target capital RBC(0) should be such that at the end of the year, even when a large loss with P<1% occurs, the insurer‘s available RBC covers (on average) still the risk margin

  20. Change in Risk Bearing Capital RBC(0) RBC(1) Year 0 Year 1

  21. Change in Risk Bearing Capital ALM risk Expected asset return over risk-free Expected insurance (technical) result Insurance risk (deviation of technical result from expectation) Current Year Risk Previous Year Risk

  22. Expected Shortfall of Risk Bearing Capital Definition. An insurer satisfies the SST when: ES[RBC(1)  0]≥ rm; where rm denotes the risk margin. RBC(1) in function of terms known at t=0: RBC(1) = (rbc(0) + r(0) + p - k)(1 + RI ) - S(1) - R(1) rbc(0)=a(0) - R(0): Risk-bearing capital at t=0, a(0): assets at t=0, R(0): liabilities at t=0 (best-estimate) p: Expected premium during [0,1] r(0): Liabilities at t=0 (Current year) r0: risk-free rate k: Expected costs during current year upr: unearned premium reserve S(1): Claim payments during current year RI: Asset return R(1): Liabilities at t=1 Notation simplified

  23. Expected Shortfall of Risk Bearing Capital RBC(1) = (rbc(0) +r (0) + p - k)(1 + RI ) - S(1) - R(1) ES[RBC(1)  0]=ES[(rbc(0) + r(0) + p - k)(1+RI) - S(1) - R(1)] = ES[(rbc(0) + r(0) + p - k)(1 + r0-r0+RI) - S(1) - R(1)] rbc(0)(1+r0)+ES[r(0)+p-k)(1+r0-r0+RI ) +rbc(0)(RI-r0)-S(1)-R(1)] ≥rm rbc(0) ≥-ES[(r(0)+p-k)(1+r0-r0+RI ) +rbc(0)(I-r0)-S(1)-R(1)] ≥rm

  24. Contents • Designing a Solvency System • Concept of the Swiss Solvency Test • The SST Standard Model • Market Risk • Nonlife Risk • Current Year Risk • Previous Year Risk • Life Risk • Scenarios • Experiences from the Field Tests • Internal Models • Group Aspects

  25. The SST Concept: General Framework Standard Models or Internal Models Mix of predefined and company specific scenarios SST Concept Models Scenarios Asset-Liability Model Insurance Risks Financial Risks Credit Risks Aggregation Method Target Capital SST Report

  26. The SST Concept: Standard Models • Market Risk • For many companies this is the most important risk (up to 80% of total target capital emanating from market risk) • Needs to be modeled with particular care • Most relevant are interest rate risk, real estate risk, spread risk, equity risk • Market risk model needs to take into account ALM • Credit Risk • Credit risk is becoming more important as companies go out of equity and into corporate bonds • Many smaller and mid-sized companies do not yet have much experience in modeling credit risk • Insurance Risk (Life) • For many life companies with predominantly savings product, pure life insurance risk is not too important • Life insurance risk is substantial for companies selling more risk products / disability • Model needs to capture optionalities and policyholder behavior • Insurance Risk (Nonlife) • Premium-, reserving- and cat risk are important • A broad consensus on modeling exists among actuaries More information under:

  27. Standard Model: Market Risk Financial market risk often dominates for insurers  adequate modeling of interest rate-, equity-,.. risks is key Interest rate risk can not be captured solely by a duration number Financial instruments have to be segmented sufficiently fine else arbitrage opportunities might be created Regulatory requirements shouldn’t force companies to disinvest totally from certain investment classes (e.g. shares, private equity) Scenarios • Historical • Share crash (1987) • Nikkei crash (1990) • European FX-crisis (1992) • US i.r. crisis (1994) • Russia crisis / LTCM (1998) • Share crash (2000/2001) • Default of Reinsurer • Financial Distress • Equity drop • Lapse = 25% • New business = -75% • Deflation For SST, RiskMetrics type model with given risk factors and associated volatilities and correlation matrix is used together with scenarios

  28. Standard Model: Market Risk • 75 Risk Factors: • 4*13 interest rate • 4 spreads • 4 FX • 5 shares • 4 real estate • 1 hedge fund • 1 private equity • 1 participations • 3 implied volas • FX • EUR • USD • GBP • JPY • Spreads • AAA • AA • A • BAA CHF EUR USD GBP • Equity • Shares • CHF • EUM • USD • GPB • JPY • Real Estate • IAZI • Commercial • Rüd Blass • WUPIX A • Hedge Funds • Private Equity i.r. time buckets:1,…,10, 15,20,30+

  29. The SST Concept: Cash Flow Based Example: Sensitivity to 2 Year CHF Yield Asset Cash Flows - = Year Present Value von Asset - Liabilities Liability Cash Flows Year Netto Cash Flows A-L 3 Change of present value of net cash flow (assets-liabilities) due to change in the 2 year CHF yield Stressed 2Y Yield 2 CHF Yield Curve 1 0

  30. Standard Model: Nonlife Premium risk Market risk Normal claims Large claims Discounted cash flows Assets: bonds, equity, … Lines of Business For each LoB, Pareto distribution with specified or company specific parameters … … For each LoB, moments are derived by parameter- and stochastic risks (coefficients of variation) Method of Moments with prescribed correlation matrix CF -> i.r. sensitivities Asset Model: Covariance/Riskmetrics approach First two moments of premium risk (normal claims) and reserving risk are aggregated using correlation -> two moments defining lognormal Compound Poisson Normal Reserving Risk Method of Moments with prescribed correlation matrix … Further aggregation with scenarios Aggregation by Convolution Aggregation by Convolution Lognormal

  31. Standard Model: Insurance Part Assumed deterministic Current Year Risk Previous Year Risk • Claims which occur during 1 year: • Within each Line of Business: • Normal claims and large claims • Catastrophes which affect different LoBs simultaneously • Reserving risks due to: • Randomness (stochastic risk) • Reassessment of reserves (parameter risk)

  32. Standard Model: CY Risk Normal Claims • Normal Claims: “High frequency claims”, different for each LoB. • Split Normal / Large claims is in standard-model defined, companies can adjust to their specific situation • For each LoB: • Estimate Parameter & Stochastic Risk due to normal claims • Then aggregate using correlation matrix (  first two moments define a Gamma distribution for normal claims ) For each LoB i: Expected number of claims Yi,j in LoB j

  33. Standard Model: CY Risk Normal Claims Within the Standard Model: CoeffVari for parameter risk and coeffvar(Yi,j) for single normal claims for two different splits normal / large claims is given • For each LoB, each company has to: • Define a split into normal and large claims • Estimate the expected number of normal claims • Estimate the expected normal claim amount • If split normal/large is 1 or 5 Mio, then standard values can be used

  34. Standard Model: CY Risk Large Claims Large claims: Large single claims and accumulation of claims due to a single event For each LoB j: Total amount due to large claims is modeled as Compound Poisson with single claims Yi,j being Pareto distributed and number of claim Nj being Poisson. Then Further assumption: SCYj are independent  Total amount due to large claims over all LoB is again Compound Poisson

  35. Standard Model: CY Risk Large Claims Pareto Distribution: Smallest large claim comsidered: β threshold parameter Shape parameter α: The smaller , the more heavy-tailed. If ≤k, k-th moment does not exist anymore For numerical calculations the cut-off point of the distribution is very important Table with standard parameters for companies lacking sufficient data

  36. Current Year Risk: Large Claims Aggregation: Use the property that for each LoB, the loss S is compound Poison distributed Consider the class of claim frequency distributions N with pn/pn-1=a+b/n, n=1,2,3,.. Where pn=P (N=n). For this class with claim sizes Y defined on the positive integers, the following recursive formula for the distribution of total claims holds: For Compound Poisson distributions, efficient algorithms exist to numerically evaluate the distribution function, e.g. Panjer Recursion See Insurance Risk Models, Panjer&Willmot

  37. Standard Model: Previous Year Risk PY Risk: Reserving Risk, due to uncertainty of run-off result Assumption: CPY*RPY(0) lognormal, with expectation RPY(0) E[CPY]=1 Randomness of CPY due to parameter and stochastic risk Stochastic Risk: due to randomness of single claims  company specific estimation from historical run-off result. Determine for each LoB (where all historical data is on best-estimate basis) Parameter Risk: Estimates of parameters are uncertain which affect all provisions of a LoB  level of total provisions incorrectly chosen. For parameter risk: predefined values are given For stochastic risk: company has to determine based on experience, e.g. using Mack Parameter Risk: Parameters given for standard model:

  38. Standard Model: Life Assumptions: The risk factors are normal distributed with given volatilities. Change of risk bearing capital in function of the risk factors is linear  The distribution over all risk factors is again (multivariate) normal distributed Volatility: Describes changes of risk factors within one year due to parameter-uncertainty Stochastic risk will be included using company specific data if relevant The volatilities have been set during discussions with specialist and represent a best-guess • Risk Factors: Volatility • Indiv. Group • Mortality 5% 5% • Longevity (trend) 10% 10% • Disability 10% 20% • Reactivation 10% 10% • Lapse 25% 25% • Option Exercise 10% 10%

  39. Standard Model: Life Correlations between risk factors for field test 2005: Split into individual and group business. Full correlation between individual and group business risk factors, except for lapse

  40. Standard Model: Life The model is simple and transparent: the company has to determine sensitivities with respect to life insurance risk factors and then can use correlation matrix and volatilities to arrive at distribution for life insurance risk The normality assumption allows easy aggregation with market risk Correlations between market risk factors Correlations between market risk factors and insurance risk factors (individual and group business) For field test 2005, CM,IG=0, but in future correlation between market risk and insurance risk can easily be included (e.g. correlation between lapse and interest rate) Correlations between life insurance risk factors (within individual (I), within group (G) and between individual and group CI,G

  41. The SST Concept: Scenarios “Ersatz experience is a better guide to the future than the real past and present”, Hermann Kahn in On Thermonuclear War Scenarios can be seen as thought experiments about possible future states of the world. Scenarios are not forecasts, in that they need not predict the future development, but rather should illuminate possible but perhaps extreme situations. Scenarios are also different from sensitivity analysis where the impact of a (small) change of a single variable is evaluated. Alternate states of the world Current state of the world

  42. Standard Model: Aggregation with Scenarios Assumption: During normal years, analytical models without scenarios are valid (described by a probability distribution F0), during exceptional years, additional losses due to events described in scenarios occur, causing a shift in the ‘pre-scenario’ distribution. Scenario i, i=1,…,n occurs with probability pi and causes an additional loss of ci (ci<0) The probability of a normal year is p0=1-p1-…-pn The probability distribution of the risk during a year with aggregated scenarios is: F(x) is the weighted mean of shifted distributions F0(.-ci), i=1,…,n, where c0=0.

  43. The SST Concept: Scenarios Historical Scenarios: Stock Market Crash 1987, Nikkei Crash 1989, European Currency Crisis 1992, US Interest Rates 1994, Russia / LTCM 1998, Stock Market Crash 2000 Financial Distress: Increase of i.r., lapse, no new business, downgrading of company,… Deflation: decrease of i.r. Pandemic: Flu Pandemic with given parameters (e.g. number of death, sick-days, etc.) Longevity Reserving: Provisions have to be increased by 10% Hail (Swiss specific): Given footprints Default of Reinsurer: Reinsurer to which most business has been ceded defaults Industrial Accident: Accident at chemical plant Personal Accident: large accident during company outing or mass panic in soccer stadium Anti-selection for Health Insurers: all insured with age < 45 lapse Collapse of a dam (Swiss specific) Terrorism Global Scenarios (for groups&reinsurers) Property Cats (earthquake, windstorm) Special Line Cats: Aviation (2 planes collide, marine event, energy event, credit&surety event

  44. Contents • Designing a Solvency System • Concept of the Swiss Solvency Test • The SST Standard Model • Experiences from the Field Tests • Experiences • Comments • Some Results • Internal Models • Group Aspects

  45. Results of Field Test • It is a challenge to stay principle-based, since explicit rules are desired by some of those who have to implement the SST • The possibility of analyzing the contributions of different risks to required capital are seen as a big advantage in particular for companies not yet using a full internal model • A risk based solvency framework entails close cooperation and communication of different sections within insurance companies • Substantial simplification are not perceived to be feasible if explanatory power of SST is to be kept • Solvency 1 (statutory view) and SST are not yet compatible  Solvency 1 will have to be made more consistent so as not to send out conflicting signals • Modelling of participations and contingent risk and capital transfer solutions will be challenging

  46. Impressions from the Industry Some have a somewhat reluctant attitude: SST will favour large companies that have already sophisticated risk-based management systems in place …’ ‘Small companies without internal model will be punished by the Standard Approach of SST…’ ‘SST may call for a complete overhaul of risk management …’ ‘Technical implementation can become a problem …’ ‘… transparency and fair values will further increase the volatility of earnings …’ ‘… complexity of internal models will allow companies to game the system …’ ‘SST leads to complexity where simplicity is required …’ ‘SST will increase the minimum Solvency level …’ We would like to thank Andreas Kull (Ernst&Young) for the permission to use this slide

  47. Impressions from the Industry Some see it in a positive light: ‘…facilitates more efficient use of risk capital …‘ ‘Facilitates company wide risk culture and dialogue…’ ‘… will reward companies that have a comprehensive risk management in place…’ ‘… internal models are an excellent management tool and can be a competitive advantage…’ ‚Rating dependent premiums will gain acceptance.‘ ‘Increased transparency in the insurance sector may reduce cost of capital for the sector as a whole…’ ‘… will lead to increased transparency in an insurer's financial strength/weakness…’ ‘… is an effective regulatory instrument to prevent insolvencies…’ We would like to thank Andreas Kull (Ernst&Young) for the permission to use this slide

  48. Impressions from the Industry Implementation of the SST for small to midsized companies: • „Wir haben diesen Sommer viel gelernt über unser Versicherungsgeschäft und über die Bedeutung von einzelnen Zahlen. Es gab viele Diskussionen über Kennziffern usw. welche zu einem Wissensaufbau in unserer Geschäftsführung führten und dazu beitragen werden, dass wir die Gesellschaft mit noch besseren Entscheidungsgrundlagen führen können. Die Ergebnisse aus dem SST-Testlauf nutzen wir auch für Diskussionen mit dem Verwaltungsrat (es gibt eine zusätzliche Sicht auf den Vermögensstand und den Geschäftsverlauf). Ich bin überzeugt, dass der SST die Führung von unserer Gesellschaft zukünftig unterstützen wird. Die Aufsicht lieferte uns dementsprechend ein weit ausgebautes Führungshilfsmittel.“ • Comment by Martin Rastetter from the ‘Metzgerversicherung’ a small-to-midsized nonlife company Days of worked used approximately: Internal: 40-50 days, External: 15-20 days

  49. Results of Field Tests • Market risk is often dominating (50%-80%) • The risk margin is between 10%-40% of target capital rsp. 1%-8% of best-estimate provisions • Diversification Effect between Insurance and Market Risk: between -5% and -30% • Effect of Scenarios on ES[RBC]: Between 5% and 50% • Seems too high and some scenarios will be adjusted • ‘Market consistent value of assets / Statutory value of assets’: between 90% and 120% but in most cases market consistent value is higher than statutory • ‘Market consistent value of liabilities / Statutory value of liabilities’: between 70% and 100% • While SCR/Target Capital requirement is in many cases higher than statutory capital requirement, the economics solvency ratio can be better or worse than the statutory solvency ratio (Solvency 1), since economic capital is in most cases substantially higher than statutory capital

  50. Results of Field Tests: Target Capital Credit Risk: In the standard model additiv add-on based on Basel II, but portfolio models can be used Diversification between market and insurance risk Target capital 1 year risk capital Market risk, taking into account diversification between different asset classes Effect of scenarios Risk Margin Expected technical & financial result Insurance Risk, taking into account diversification between risk factors and lines of business and branches Relative length of bars correspond approximatively to actual median values