1. Practical aspects of realistic valuations using a market consistent asset model Richard Waller & Michel Abbink

2. Agenda Regulatory background The realistic balance sheet Modelling approach Modelling issues Practical issues

3. Regulatory background CP143 (July 2002) Dear CEO letter (August 2002) FSA progress report (October 2002) Dear CEO letter (December 2002) Tiner speech (February 2003) Dear CEO letter (March 2003) CP??? (July 2003)

4. How it all fits together Realistic balance Sheets International Accounting Standards Pillar one Capital requirements Internal Capital Assessment Principles and Practices of Financial Management

5. How it all fits together Admissible assets Realistic assets Realistic assets Pillar one basis (twin peaks approach) Pillar two basis Free Capital RMM Resilience Reserve WP liabilities EU directive Free capital SCA ICA Realistic liability Free capital Market and credit risk capital Realistic liability

6. Agenda Regulatory background The realistic balance sheet Modelling approach Modelling issues Practical issues

7. The realistic balance sheet Total realistic assets -Non-profit statutory liabilities -Non-profit RMM Net with-profit assets Base asset shares / reserves +/-Misc surplus allocated to asset shares +/-Planned enhancements to/retentions from asset shares +/-Smoothing costs/benefits +Future guarantee costs -Future guarantee charges +/-Value of non-profit business +/-Future surrender profits +/-Other realistic liabilities/assets +Current liabilities Net with-profit liabilities

8. The realistic balance sheet Total realistic assets -Non-profit statutory liabilities -Non-profit RMM Net with-profit assets Base asset shares / reserves +/-Misc surplus allocated to asset shares +/-Planned enhancements to/retentions from asset shares +/-Smoothing costs/benefits +Future guarantee costs -Future guarantee charges +/-Value of non-profit business +/-Future surrender profits +/-Other realistic liabilities/assets +Current liabilities Net with-profit liabilities

9. The realistic balance sheet Total realistic assets -Non-profit statutory liabilities -Non-profit RMM Net with-profit assets Base asset shares / reserves +/-Misc surplus allocated to asset shares +/-Planned enhancements to/retentions from asset shares +/-Smoothing costs/benefits +Future guarantee costs -Future guarantee charges +/-Value of non-profit business +/-Future surrender profits +/-Other realistic liabilities/assets +Current liabilities Net with-profit liabilities

10. The realistic balance sheet Total realistic assets -Non-profit statutory liabilities -Non-profit RMM Net with-profit assets Base asset shares / reserves +/-Misc surplus allocated to asset shares +/-Planned enhancements to/retentions from asset shares +/-Smoothing costs/benefits +Future guarantee costs -Future guarantee charges +/-Value of non-profit business +/-Future surrender profits +/-Other realistic liabilities/assets +Current liabilities Net with-profit liabilities

11. The realistic balance sheet Total realistic assets -Non-profit statutory liabilities -Non-profit RMM Net with-profit assets Base asset shares / reserves +/-Misc surplus allocated to asset shares +/-Planned enhancements to/retentions from asset shares +/-Smoothing costs/benefits +Future guarantee costs -Future guarantee charges +/-Value of non-profit business +/-Future surrender profits +/-Other realistic liabilities/assets +Current liabilities Net with-profit liabilities

12. Agenda Regulatory background The realistic balance sheet Modelling approach Modelling issues Practical issues

13. Modelling approach Aim: – Market consistent valuation of insurance contracts including embedded guarantees and options Approaches – Deterministic – Black Scholes – Stochastic projection – Stochastic valuation

14. Deterministic Present value of projected cash flows Guarantee cost emerging in the single scenario – no allowance for optionality Implicit allowance via prudent margins Currently common (EV approach) Not market consistent

15. Black Scholes Deterministic “risk neutral” projection(s) of payouts and asset shares Identify replicating portfolio of options Black Scholes valuation of options Depending on approach difficult or impossible to allow for smoothing, path-dependency or dynamic nature of bonuses, asset allocation & policyholder behaviour

16. Stochastic projections Stochastic projection of guarantee / smoothing costs Scenarios based on “real world” distribution Take x th percentile Discount rate Not market consistent

17. Stochastic valuation Stochastic projection of guarantee / smoothing costs Scenarios based on market consistent models – risk neutral or deflator (Weighted) mean Most complex and most accurate,..but still subjective (incomplete markets, decision rules)

18. Comparison of results

19. Agenda Regulatory background The realistic balance sheet Modelling approach Modelling issues Practical issues

20. Market consistent modelling Theoretical issues – What is risk free? – Availability market data – Basis risk Stochastic asset models – Fit for purpose – Deflator or risk neutral – Asset classes, economies and interaction – Richness of calibration structure – Statistical features – Convergence

21. Market consistent modelling Practical issues – number of simulations – projection period – projection steps – ease of use Audit – relevance calibration to the risk that needs pricing – Check if prices are replicated from the output – Statistical features Stochastic Accreditation Working party

22. Liability modelling Asset share methodology Bonus philosophy Investment strategy Surrender value policy Policyholder behaviour

23. Asset share methodology What is the methodology? – Is it well documented and fit for this purpose? – Does it cover all classes of business? – Needs to be consistent with disclosures What about charges for guarantees? – Is this aspect well documented? – Does it comply with treating customers fairly? – When should charges be levied?

24. Asset share methodology Unsmoothed and smoothed asset shares? – Doubles up the projection code / calculations – Is the smoothing formula clear? – What to base payouts on? Are current asset shares available? – Is the historic data there to accumulate them? Extreme scenarios – Is the methodology robust enough?

25. Bonus philosophy Future reversionary bonus rates – Based on a reference such as gilt yields? – Maximum and minimum amounts? – Dependent on fund solvency level? – Dependent on existing level of guarantees? – Limits on size/frequency of changes? – How many different rates to model? – Needs to be consistent with disclosures

26. Bonus philosophy Future terminal bonus rates – What percentage of asset share targeted? – How to smooth payouts over time? – How to smooth across policy size? – Dependent on fund solvency level? – Limits on frequency of changes? – Needs to be consistent with disclosures? – Can’t model competitive influences

27. Investment strategy What asset mix? – Do long term guidelines really exist? – What about derivative positions? – Treatment of net cashflows? – Duration matching of fixed interest? – Dependent on fund solvency level? – What about manager bias/performance? – Needs to be consistent with disclosures

28. Surrender value policy Current surrender value bases – Can they be modelled accurately? – Should future changes be modelled? – How robust in extremes? MVR policy – Can it be modelled accurately? – It may still be evolving – Needs to be consistent with disclosures

29. Policyholder behaviour Take up of options – What to assume when guarantees are in-the- money – What to assume in preceding years – Do policyholders know value of guarantees – Surrender activity often not financially driven – Can’t model lifestyle influences

30. Agenda Regulatory background The realistic balance sheet Modelling approach Modelling issues Practical issues

31. Model point construction – Focus attention where costs are likely – Goodness of fit to actual policy data – Impacts on run times Run times – Can be very long! – Conflicts with accuracy and complexity – Efficient code and systems usage important

32. Practical issues Checking – How do you check stochastic results? – Investigate deterministic scenarios too? Model maintenance – Need strong control environment – Separate RBS and other ALM models? Guidance – Is more guidance necessary?

33. Contact details richard.waller@eu.watsonwyatt.com michel.abbink@eu.watsonwyatt.com