A Bonus-Malus System as a Markov Set-Chain. Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics

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1 A Bonus-Malus System as a Markov Set-Chain Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics

2 Contents 1. Markov set-chain 2. Model of bonus-malus system 3. Example 4. Conclusions

3 1. Markov set-chain Definition 1* Let N 1 be a compact set of r r stochastic matrices. Let consider Markov chains with the state space S = {1, 2,, r}, having all their transition matrices in N 1. A Markov set-chain is the sequence N 1, N 2, N 3,..., where N k = {P: P =P 1 P 2 P k, where P i N 1 for all i = 1, 2,, k} for each k. *Hartfiel D. J. [1998], Markov Set Chains, Springer Verlag, New York

4 1. Markov set-chain Definition 2* A matrix interval is the interval [K, Q] ={P: K P Q}, where P=[p ij ] denotes a r r stochastic matrix and K=[k ij ] and Q=[q ij ] are nonnegative r r matrices such that K Q. Definition 3* A matrix interval [K, Q] is tight if for all i and j. k ij = min p and qij = max pij P [K, Q] ij P [ K, Q] *Hartfiel D. J. [1998], Markov Set Chains, Springer Verlag, New York

5 2. Model of bonus-malus system A system employed in automobile insurance is called a bonusmalus system when*: in each period (usually a year) all policyholders of a given tariff group are divided into a finite number of classes C i (i=1, 2,..., r) and their premium depends only on the class they belong to; the class of a policyholder for a given period is determined uniquely by the class in the preceding period and the number of claims reported in that period. *Lemaire J. [1995], Bonus malus systems in automobile insurance, Kluwer Academic Publishers, Boston

6 2. Model of bonus-malus system The classical model of a bonus-malus system a finite homogeneous Markov chain with the state space S = {1, 2,, r} and transition matrix* P( λ ) = [ p ( λ )] = p ( λ ) T, ij k= 0 where p k (λ) is the probability that the policyholder with claim frequency λ reports k claims in one period and T k - the matrix of transition rules. k k *Lemaire J. [1995], Bonus malus systems in automobile insurance, Kluwer Academic Publishers, Boston

7 2. Model of bonus-malus system Assumptions: 1. the number of claims of a policyholder characterised by λ conforms to a Poisson distribution; 2. λ (1) and λ (2) such that 0<λ (1) < λ (2) <1 (1) determine the interval [λ (1), λ (2) ] of the claim frequency variability. Under the above assumptions: p k (λ) [min{p k (λ (1) ); p k (λ (2) )}, max{p k (λ (1) ); p k (λ (2) )}], (2) for k = 0, 1, 2, and λ [λ (1), λ (2) ].

8 2. Model of bonus-malus system Since λ [λ (1), λ (2) ], the lower and upper bounds on the transition matrix interval are: K = = k 0 ( ) ( 2 ) ( 1 ) min { pk,pk } Tk = [ min{ pij,p 1 ( 2 ) ij }], (3) Q = = k 0 ( ) ( 2 ) ( 1 ) max { pk,pk } Tk = [ max{ pij,p 1 ( 2 ) ij where upper indices (1) and (2) indicate that a given probability is calculated for λ (1) and λ (2) respectively. }], (4) P ( λ) [ K, Q] for λ [ λ, λ ( 1 ) ( 2 ) ]

9 2. Model of bonus-malus system Theorem 1 Let a Markov set-chain be a model of a bonus-malus system under assumptions (1)-(2). Let [K, Q] be its transition matrix interval, where K and Q are given by formulas (3) and (4). Then the Markov set-chain is ergodic. Theorem 2 Let a Markov set-chain be a model of a bonus-malus system under assumptions (1)-(2). Let [K, Q] be its transition matrix interval, where K and Q are given by formulas (3) and (4). Then the interval [K, Q] is tight.

10 Class number A Bonus-Malus System as a Markov Set-Chain 3. Example Bonus-malus system of PZU SA (April 2003) Premium level (in percentage) Class number after or more claims

11 3. Example Claim frequency interval: [0.1; 0.2] Bounds on the interval of stationary probability distributions: l = h =

12 3. Example Lower bound on the interval of mean first passage times: l M =

13 3. Example Upper bound on the interval of mean first passage times: h M =

14 3. Example The difference between the upper and lower bounds on the interval of mean first passage times:

15 3. Example Lower bound on the interval of mean first passage times: l M =

16 3. Example Upper bound on the interval of mean first passage times: h M =

17 4. Conclusions The application of a Markov set-chain to the analysis of a bonus-malus system enables us to: broaden the scope of research in comparison with classical Markov chains; relax the assumption of the constant claim frequency of a policyholder; examine the consequences of claim frequency changes within a given interval; determine the variability range of stationary distribution as well as of mean first passage times.

18 Thank you Małgorzata Niemiec

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