Pricing and capital allocation for unit

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Transcript Pricing and capital allocation for unit

Pricing and capital allocation for unit-linked life insurance contracts with minimum death guarantee C. Frantz, X. Chenut and J.F. Walhin Secura Belgian Re

The problem

1,2

Capital sous risque dans une garantie plancher

Sum at risk 1 Insurer’s liability for a death at time t: max(

K

,

S t

) 

S t

 max(

K

S t

, 0 ) 0,8 0 1 2 3 4 5 6 7 8 9 10 • How to price it ?

• Capital allocation ?

Two approaches …

 

The financer: it is a contingent claim Solution: hedging on the financial market Black-Scholes put pricing formula

 

The actuary: it is an insurance contract Solution: equivalence principle Expected value of future losses

… and two risk managements

Financial approach : hedging on financial markets

Actuarial approach : reserving and raising capital

Agenda

   

Actuarial vs financial pricing Monte Carlo simulations Cash flow model Open questions

First question: actuarial or financial pricing?

Hypotheses :

– Complete and arbitrage-free financial market – Constant risk-free interest rate – Financial index follows a GBM:

dS t

 

S t dt

 

S t dW t

Simple expressions for the single pure premium in both approaches

Single pure premiums

Actuarial pricing :

SPP Act

k T

  1

Ke

rk

 ( 

d

2

Act

( 0 ,

k

))

k p x q x

k

S

0

k T

  1

e

 (  

r

)

k

 ( 

d

1

Act

( 0 ,

k

))

k p x q x

k

Financial pricing :

SPP Fi

k T

  1

Ke

rk

 ( 

d

2

Fi

( 0 ,

k

))

k p x q x

k

S

0

k T

  1  ( 

d

1

Fi

( 0 ,

k

))

k p x q x

k

with

d

2

Act

(

t

,

T

) 

d

1

Act

(

t

,

T

)  log(

S t

/

K

)  (

r

d

2

Act

(

t

,

T

)  

T T

  

t

t

2 / 2 )(

T

t

)

d

2

Fi

(

t

,

T

)

d

1

Fi

(

t

,

T

)  log(

S t

/

K

)  

d

2

Fi

(

t

,

T

)    ( 

T T

  2 

t

t

/ 2 )(

T

t

)

Monte Carlo simulations

 

Goal : distribution of the future costs 3 processes to simulate :

– Financial index – Death process – Hedging strategy (financial approach only)

1 0,8 0,6 0,4 0,2 0 0

Probability distribution functions

10 20 30

Discounted future costs

40 50 Actuarial Financial 60

Sensitivity analysis

1,00 0,80 0,60 0,40 0,20 0,00 0

Distribution of DFC - variation of

-

10 20 30 40

DFC Act

50 60 70 80 20% 15% 10% 8,5% 5% 0% -5% -10% -15% -20% No Stock

1 0,8

FI

0,6 0,4 0,2 0 6

Sensitivity analysis Distribution of DFC Fi - variation of

-

7 8 9 10

DFC Fi

11 12 13 -10% -5% 0% 5% 8,50% 10% 15% 20% 14

Conclusion

  

Difficult to put into practice (especially for the reinsurer)

Financial approach is better BUT only makes sense if the hedging strategy is applied !

Conclusion : actuarial approach has to be used

Second question : How to fix the price ?

 

Base : single pure premium + Loading for « risk »

Answer : cash flow model

Cash flow model

Insurance contract = investment by the shareholders

Investment decision: cash flow model t 1 2

 

5 … P C t

R t

K t r t (R) r t (K) Taxes

Price P fixed according to the NPV criterion

Open questions

  

How much capital to allocate?

How to release it through time?

What is the cost of capital?

Risk measures and capital allocation

 

Coherent risk measures (Artzner et al.) Conditional tail expectation (CTE):

CTE

 (

X

)   [

X X

V

 (

X

) ]

where

V α

(

X

)  inf 

V

:  

X

V

    

Capital to be allocated at time t:

k t

CTE

 (

DFC t

) 

p t

One-period vs multiperiodic risk measures

Problem: intermediate actions during development of risk

 

Addressed recently in by Artzner et al.

Capital at time t :

– to cover all the discounted future losses?

– to pay the losses for x years and set up provisions at the end of the period?

We applied the one-period risk measure to the distribution of future losses at each time t

Simulation of provisions and capital

Two possibilities:

– Independent trajectories

P

(

t

)

K

(

t

)  

E

DFC E

DFC

(

t

(

t

) ) 

DFC

(

t

) 

V

 (

DFC

(

t

))  – Tree simulations

P

(

t

) 

K

(

t

)  

E E E

 

E E

 

DFC DFC

DFC

(

t

) (

t

(

t

) )

S t DFC DFC

,

N

(

t t

)   

E

DFC

(

t

)  , (

t

 ) 

V

 (

V

 (

DFC DFC

(

t

)) (

t

.

 )),

S t

,

N t

 

Independent trajectories

P(t) K(t) t = 1

Tree simulations

P 1 (t) K 1 (t) P N (t) K N (t) t = 1

P

(

t

) 

i N

  1

P i

(

t

)

N K

(

t

) 

i N

  1

K i

(

t

)

N

Comparison with non-life reinsurance business

   

Number of claims : Poisson(

l

) Severity of claim : Pareto(A,

) Let

Fix

l

vary so that we obtain the same pure premium

 

Compare premium with both models For usual values of

 (1,52,5)

, results not significantly different

Cost of capital

CAPM :

COC

r

 b (

r m

r

) 

What is the

b

for this contract?

– Same b for the whole company?

– Specific b for this line of business?

How to estimate it?

Conclusions

 

Actuarial approach Pricing and capital allocation using simulations

Other questions:

– Asset model: GBM, regime switching models, (G)ARCH, …?

– Risk measure? Threshold  ?

– Capital allocation and release through time?