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Practical stochastic modelling for life insurers Philippe Guijarro Mike White

abcd. Practical stochastic modelling for life insurers Philippe Guijarro Mike White. 1 December 2003 The Glasgow Moat House. To provide an understanding of : 1) The importance of stochastic modelling for Life Insurers

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Practical stochastic modelling for life insurers Philippe Guijarro Mike White

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  1. abcd Practical stochastic modelling for life insurersPhilippe GuijarroMike White 1 December 2003 The Glasgow Moat House

  2. To provide an understanding of : 1) The importance of stochastic modelling for Life Insurers 2) The method by which UK insurers can create and use stochastic modelling functionality, and the numerous practical issues which will need to be managed. Purpose of this presentation

  3. Part One Overview of stochastic modelling Uses of stochastic modelling for UK Life Insurers Part Two Likely direction of stochastic modelling Practical aspects of building stochastic models Introduction

  4. Part One

  5. What is stochastic modelling? How does it work? Why is it so important? Background to Stochastic Modelling

  6. Valuing one-sided payoffs (e.g. with profits business) Setting strategy (e.g. assessing investment strategies) Managing the business (e.g. measuring performance in fair manner) Regulatory (e.g. ICAS) Reasons for stochastic modelling

  7. Considering distributions of variables Different approaches Numerical methods (e.g. Monte Carlo modelling) Closed form solutions (e.g. Black-Scholes) For different purposes: Market consistent Realistic future experience What is stochastic modelling?

  8. Better to describe as Asset Liability Modelling (ALM) Stochastic element comes from selection of economic scenario generator And certain demographics (e.g. interaction between lapses and market falls) Building Asset Liability Model accounts for vast majority of development work And involves many practical issues Background to Stochastic Modelling

  9. Why do we need simulation?(example for with profits guarantees) Value of guarantees Solvency Economy Investment strategy Bonus strategy Lapse rates Complex interactions!!

  10. Worked example

  11. Unit linked endowment: 10 year term Premium £1,000 pa Death benefit is £10,000 Guaranteed maturity value £10,000 On survival, gets higher of guaranteed maturity value and unit fund Unit funds invested in well diversified portfolio of equities Product details

  12. Investment returns onequity index

  13. Probability of guarantee biting

  14. Deterministic (EV) vs stochastic (FV)

  15. Part Two

  16. Major UK With Profit offices expected to produce RBS results using asset liability modelling Other needs (IFRS, Twin Peaks, Internal capital/risk management) Various stages of development (some very far advanced, most adapting to rapid change in regulation) Modelling platform selected Building/extending functionality Smaller insurers approach (incl. NP/UL offices) Where UK insurers are

  17. All major UK companies using ALM for With Profit valuation Also for other financial options/guarantees (e.g. GAO on Unit Linked) Convergence/acceptance of certain economic scenario generators Individual companies to justify their use of assumptions Output from model used to improve internal management of risks (development versions) Also production versions used to produce regular reporting results (Realistic Balance Sheet, International Financial Reporting Standards) Likely direction of asset-liability modelling (ALM)

  18. Defining the required structure Building an asset liability model Using asset-liability functionality Application of the model Link with industry requirements (PSB, IAS, Realistic Balance Sheet) Practical aspects

  19. Initial questions What is the purpose of the model? What existing systems/processes should be re-used? What level of accuracy is required? Which parts of the business should be modelled? Which are the essential deliverables? How much flexibility on resources, budget and timetable? Defining the required structure

  20. Defining the required structure

  21. Systems, data and support Specification and decision rules Stochastic assumptions Adapting to unexpected issues Testing and Reasonableness Interpreting, explanation and reporting Building an asset liability model

  22. Introducing the economic scenario generator Lots of different models – which one to use? Likely that position will continue to develop Essential to have flexibility to use different ESG Also need to consider building in stochastic functionality for certain demographics (lapses, mortality?) Using stochastic functionality

  23. Market consistency? Arbitrage free? Mean reversion / Fat tails What assets to model? Auditability / easy to explain? Continuous / discrete? .............. depends on liabilities and purpose Considerations in choice of generator

  24. Stochastic capability adds extra dimension Unlimited reports/results Effective communication essential Application of the model 95th percentile

  25. Market consistent modelling vs real world modelling – different ESGs? Timetable Flexibility Documentation & use of model results in managing the business Link with industry requirements (PSB, IAS, Realistic Balance Sheet)

  26. Stochastic modelling is a big investment Major insurance companies must have functionality Provided designed and managed properly, can cover a number of reporting requirements Understanding and communication of results critical Conclusion

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