W W W . W A T S O N W Y A T T . C O M
Real life ALMRussell Beaumont
16 April 2004
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Agenda
Why life companies use ALM
Different approaches used
Practical considerations
Example for CEE markets
Two further examples
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Why companies use ALM
Understand financial dynamics of business
Helps identify and quantify risks
Improved – capital allocation– investment strategy– liability management (products, bonuses, guarantees)
Better informed decision-making
Enhanced business performance
For (future) regulatory requirements
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Reasons for growth in ALM
Economic conditions– poor equity performance / low interest rates– cost of guarantees– impact on solvency levels
Competitive pressure– product innovation– maturity value out-performance– if others are, why don’t we?
Increased systems power / capacity
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ALM applications
Investment strategy
Product development
Statutory reserving
Business planning
Risk management
Realistic reporting
Capital allocation
Performance measurement
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Deterministic approach
Deterministic approach involves choosing individual economic scenarios to investigate, eg– best estimate– market crash– high / low interest rates
+ Advantages+ consider scenarios specific to circumstances+ useful first step to gain basic understanding+ quicker to generate results
– Disadvantages– scenarios chosen are limited and subjective– no indication of weight for each scenario– may overlook key scenarios
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Stochastic approach
Model determines future economic parameters using mathematical relationships between them
+ Advantages+ many scenarios can be randomly generated+ mathematical relationships based on empirical evidence+ can be calibrated to current market conditions
- Disadvantages- practical considerations may limit number of scenarios
that can be investigated- harder to analyse results to check accuracy
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What approach in practice?
Start with current programs e.g. valuation, embedded value
Investigate a number of deterministic scenarios, including extreme scenarios to increase understanding of model
Develop management decision rules based on above
Add stochastic asset model
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Some practical considerations
What are the objectives of the ALM study?
Choice of asset model
Model point creation
Decision rules
Projection period
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Objectives of the ALM study
Clear and agreed objectives for the study
Understanding the objectives will help determine the answers to the other practical issues
Don't try to answer too many questions with one study - confuses the message and the modelling!
Choice of asset model depends on the objectives– econometric– stochastic
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Choice of asset model
Econometric– long and short term
models– uses historical data– aims for best estimate
‘realistic’ probability distribution
– used for investigating extreme scenarios
– used for– business planning– asset allocation– risk management
Market consistent– uses current stock market
and derivative data– aims to replicate market
prices– gives average values,
based on likelihood of each scenario
– used for– pricing guarantees– liability hedges e.g.
guaranteed annuities– realistic balance sheets
The choice of asset model depends on the objective
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Long-term econometric models Assess risk and reward for different asset allocations over
the long term
Theory, history and judgement combined
Expected returns relate to views of the future
Uncertainty (volatility) judged with an eye to the past
Investment manager views also included
Example structure– Inflation drives bond yields, which drive equity total returns– Inflation & bond yields autoregressive– Equities random walk– Returns are log-normally distributed
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Short-term econometric models Derives scenarios to stress test the free capital Concentrates on events that occur at the tail over short term
horizons Covers a wide range of economic and non-economic risks to
which a life company is exposed, including– asset and interest rate levels and volatility– credit spread levels– persistency levels– mortality levels– operational risk losses
Some risk factors need estimating for the model. Once calibrated, the model is run to produce 10,000
scenarios of monthly risk factor information for 1 year
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Key requirements for market consistent model
Ability to replicate today’s market prices Simple calibration process Efficiency (convergence to market prices) Transparency Replicating portfolios an alternative approach What happens when the market is not complete?
– length of options– away from strike prices– options not available
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Model point creation
Compromise between number of model points and model run times
What are your objectives, eg are you: – investigating future cashflows; or – investigating future solvency position
Reconciliation to full data runs
Appropriate criteria for grouping business– criteria for being 'in the money'
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Decision rules Need to reflect what would happen in practice
– management decisions– policyholders' decisions
Need to be objective, not subjective Should be realistic and achievable, e.g. in line with
policy documentation, projections, etc. Examples for asset mix:
– equity proportion = 40% if solvency level > 10%– else equity proportion = 20%
Withdrawal rates– surrender rates 5%– rates increase to 50% if guarantee 'in the money'
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Projection period
What are the objectives, e.g.– long term asset allocation– short term solvency cover
Need to cover significant future events, e.g. maturity of large block of business
The longer the projection period the bigger the ‘funnel of results’ (econometric model)
Availability of financial instruments for calibration of market consistent model
More scenarios needed for market consistency
Impact on run times
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Current issues for CEE markets
Decreasing interest rate environment
Some relatively high technical interest rates
EU accession speeds the process
Multinational head offices already developing techniques
Asset models for CEE markets?
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Example stress test: Hungary
Yield curve falls immediately to Euro-zone rates
Simple deterministic run on representative policy
What additional reserves required?
0%
2%
4%
6%
8%
10%
12%
0 5 10 15
Term
Yie
ld
Yield curves
Eurozone
Hungary
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Stress test results
0
20
40
60
80
100
120
140
160
180
Res
erve
s(H
UF
'000
s)
3.5 4.5 5.5
Techical interest rate (%)
Base reserve Cost of guarantee
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Example 1: Group wide study
Study in mid-2001 for UK insurer with bank, life and non-life subsidiaries, and final salary pension scheme
Long-term econometric model to help asset allocation
Key findings– highlighted conflict between short term statutory solvency
and long term policyholder performance– asset model does not assume market recovery– buy protection from equity falls and monitor closely– tax and risk/reward moves equities out of pension fund– proposed asset allocations and close matching principles
for different parts of business
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Example 2: FSA calibration
Help FSA in calibration of enhanced capital requirement Method
– Develop an "average" realistic balance sheet– Develop a stochastic model of multiple risk factors – Project the RBS over one year stochastically– Derive required capital at a range of confidence levels– Generate risk capital margin stress tests that require this
level of capital– Carry out sensitivities to the assumptions made– Comment on the appropriate structure of the RCM stress
tests Results expected to be published in Spring 2004
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