reinsurance portfolio optimization horse chasing algorithm

Reinsurance portfolio optimization

Horse chasing algorithm

Xuyan (Frank) Wang, PhD M.M

Validus Research

www.validusre.bm

July 2008

2

Reinsurance portfolio optimizationHorse chasing algorithm

Outline

• Problem setting

• Our approach

• Horse chasing algorithm

• Search strategy

• Concluding remarks

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Problem setting

• Input data – simulated yearly (sequence of) losses for cat events for contract

• Objective: maximize expected profit

E( ) = measure of expected value

P, Pi = expected profits of the portfolio and the ith contract

wi = participation or position (i.e. amount of risk taken) of the ith contract

• Constraints:

• Key risk measures do not exceed specific thresholds

ρk = risk function, ck = threshold for the kth constraint

• Realistic ranges of wi

)()( ii

i PEwPE ∑=

kii

ikk cPwP ≤= ∑ )()( ρρ

Two observations about horse chasing

algorithm and simplified goal

• Two observations about horse chasing

• Difference of chasing forward and backward

• Permissible range of cross numbers before speed change

• Simplified goal

• Continuously improve objective function

• Search strategy

• Do the substitution that makes the most improvement


Portfolio construction example

Reinsurance portfolio optimization

Concluding remarks

• Robust

• Our simpler goal is insensitive or tolerant to horse chasing algorithm logical

flaws or errors

• Whether it is also insensitive to input simulation data variations remained to

be studied

• Jointly linear assumption

• Caused minor fluctuation in portfolio risk measure

• Can try more jointly linear assumption

• Limitations

• Is beat by good human judgment and intuition


reinsurance portfolio optimization horse chasing algorithm

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