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Financial Planning and Risk-return ProfilesStefan Graf, Alexander Kling, Jochen Ru

Stefan Graf AFIR ColloquiaMadrid | June 2011


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Financial Planning and Risk-Return Profiles Stefan Graf, Alexander Kling, Jochen RuPage 2

Agenda

Motivation

Assessing products risk-return profiles existing methodologies

Risk-return profiles by means of stochastic modeling

Conclusion


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Motivation

Government-run pay-as-you-go systems suffer from demographic

changes

demand for private old age provision increases

How to choose optimal products?

Vast body of literature on determining optimal (often dynamic) asset

allocations mostly using expected utility approaches

Really practicable for typical client?


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Motivation

Contribution

Introduce methodology on how to derive risk-return profiles and compare

them to existing approaches

Quantitative analyses of common products with and without investment

guarantees


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Products under consideration

Products without embedded guarantees

Investment in equity and balanced fund

Products with money back guarantee

Static option-based product (underlying + put)

(Dynamic) CPPI strategy on a client individual basis (iCPPI)

Products with savings premium guarantee

(Dynamic) CPPI strategy implemented in a mutual fund and thus

managed on a collective basis (CPPI high watermark fund)


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Agenda

Motivation


Risk-return profiles by means of stochastic modelling

Conclusion


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Assessing the risk-return profile

Sample illustration

Underlying investment vehicle is projected assuming some (constant)

deterministic performance

Products maturity benefit is then calculated accordingly


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Sample illustration *, e.g. equity fund vs. CPPI high watermark fund

*12 year-single premium investment

Equity Fund

0

50,000

100,000

150,000

200,000

250,000

0% 3% 6% 9%

CPPI high watermark fund

0

50,000

100,000

150,000

200,000

250,000

0% 3% 6% 9%


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Sample illustration - pitfalls

Asset allocation neglected

Lack of volatility conceals path-dependant effects

e.g. reallocation of risky and riskless assets in CPPI products


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Backtesting

Underlying investment vehicle is projected assuming it had been invested

in the past (according to some time series)

Products maturity benefit is then calculated accordingly


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Backtesting *, e.g. equity fund vs. CPPI high watermark fund

*Single premium investment in MSCI World and German Government Bonds from 1998-2009

Equity Fund

0

50,000

100,000

150,000

200,000

250,000

Equity exposure

CPPI high watermark fund

0

50,000

100,000

150,000

200,000

250,000

Bond exposure Equity exposure


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Agenda

Motivation


Risk-return profiles by means of stochastic modeling

Conclusion


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Risk-return profiles

Stochastic modeling of

Equity (modified (Heston, 1993))

Interest rates (Cox et al., 1985)

Derive maturity benefit by

Generating equity and interest rate scenarios

Modeling fund management decisions

Modeling products investing in various funds

Assess products risk-return profile by estimating the probability

distribution of maturity benefits


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Risk-return profiles

Quantitative results

Single premium Regular premium

Internal rate of return

-5.00%

-3.00%

-1.00%

1.00%

3.00%

5.00%

7.00%

9.00%

11.00%

13.00%

15.00%

EquityFund

iCPP I O ption BasedProduct

BalancedFund

CPPIhigh watermark

fund

50% 90% Median Expected return Sample Illustration

Internal rate of return

-5.00%

-3.00%

-1.00%

1.00%

3.00%

5.00%

7.00%

9.00%

11.00%

13.00%

15.00%

EquityFund

iCPPI Opt ion BasedProduct

BalancedFund

CPPIhigh watermark

fund

50% 90% Median Expected return Sample Illustration


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Agenda

Motivation


Risk-return profiles by means of stochastic modelling

Conclusion


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Conclusion and further research

Conclusion

Existing approaches provide insufficient information and may lead to

misselling

Introduced methodology provides appropriate assessment of products

risk-return profile

Further research

Extension to retirement phase products?

Model risk?

e.g. modeling of inflation,

How to communicate the information appropriately (to financial advisors

and clients)?


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Thanks for your attention

Contact details

Stefan Graf [email protected]

Alexander Kling [email protected]

Jochen Ru [email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]

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