JON HOLTAN. if P&C Insurance Ltd., Oslo, Norway ABSTRACT


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1 OPTIMAL INSURANCE COVERAGE UNDER BONUSMALUS CONTRACTS BY JON HOLTAN if P&C Insurance Lt., Oslo, Norway ABSTRACT The paper analyses the questions: Shoul or shoul not an iniviual buy insurance? An if so, what insurance coverage shoul he or she prefer? Unlike classical stuies of optimal insurance coverage, this paper analyses these questions from a bonusmalus point of view, that is, for insurance contracts with iniviual bonusmalus (experience rating or noclaim) ajustments. The paper outlines a set of new statements for bonusmalus contracts an compares them with corresponing classical statements for stanar insurance contracts. The theoretical framework is an expecte utility moel, an both optimal coverage for a fixe premium function an Pareto optimal coverage are analyze. The paper is an extension of another paper by the author, see Holtan (21), where the necessary insight to an concepts of bonusmalus contracts are outline. KEYWORDS Insurance contracts, bonusmalus, optimal insurance coverage, euctibles, utility theory, Pareto optimality. 1. INTRODUCTION Shoul or shoul not an iniviual buy insurance? An if so, what insurance coverage shoul he or she prefer? These funamental questions are of main practical interest within the fiel of insurance purchasing, an have been extensively stuie uner varying conitions in insurance economics. Classical references are e.g. Mossin (1968), Arrow (1974) an Raviv (1979). A common factor of all these stuies is their straightforwar focus on insurance contracts without iniviual bonusmalus (experience rating or noclaim) ajustments. However, both from a customer s point of view an from a theoretical point of view, insurance contracts with bonusmalus ajustments, like e.g. motor insurance contracts, are usually much more complex to consier with regar to optimizing the insurance coverage. The increase complexity is cause by the bonus hunger mechanism of the customers; that is, ASTIN BULLETIN, Vol. 1, No. 1, 21, pp
2 176 JON HOLTAN the tenency for insurance customers to carry small losses themselves in orer to avoi an increase of future premium costs. The aim of this paper is to take this bonus hunger mechanism into account within the framework of optimal insurance coverage uner bonusmalus contracts. The paper is an extension of another paper by the author, Holtan (21), where the necessary insight to an concepts of bonusmalus contracts are outline. The paper is organize as follows: Sections 2 an escribe the general insurance contract an an expecte utility approach to the problem. Sections 46 outline some new propositions of the fiel of optimal insurance coverage particularly for bonusmalus contracts. These propositions are compare to their corresponingly classical propositions for stanar insurance contracts. Section 5 treats optimal coverage for a fixe premium function, while section 6 treats pareto optimal coverage. Section 7 gives a summary of the conclusions of the paper. 2. THE GENERAL INSURANCE CONTRACT We recapitulate briefly the main features of a general bonusmalus insurance contract as outline in Holtan (21). Consier an insurance buyer representing a risk of loss X, where X is a stochastic variable with probability ensity function f(x) an x. The amage sie of the contract is characterize by a contractual compensation c(x) an a true compensation c*(x) if loss X = x occurs, where c*( x) = * cx () z if c(x) > z if c(x) z. (1) The true compensation function c*(x) is the actual compensation function because of its bonus hunger component z, while the contractual compensation function c(x) is no more than the loss amount minus the contractual euctible. The fixe amount z is the excess point of the optimal choice of selffinancing generate by the customer s bonus hunger strategy after the loss occurrence, or in other wors, the present value of the loss of bonus, an is efine as  z = # e lt ( p1( s+ t)  p( s+ t)) t, (2) where l is the nonstochastic market rate of interest of selffinancing, p 1 (s + t) is the premium pai at time t after a loss occurrence at time s if the loss is reporte to the insurer, an p (s + t) is the corresponingly premium if the loss is not reporte. The premium processes p (s + t) an p 1 (s + t) are assume to be continuous nonstochastic for all t >. See Holtan (21) for a more etaile an complete escription of bonusmalus effects on an insurance contract.
3 OPTIMAL INSURANCE COVERAGE UNDER BONUSMALUS CONTRACTS 177 An important statement which partly follows from (1) an (2) is that inepenent of the contractual compensation function, the true compensation function has always an iniviual euctible; see proposition 2 in Holtan (21). As we iscuss later, this statement explains much of the optimal coverage characteristics of bonusmalus contracts outline in this paper.. AN EXPECTED UTILITY APPROACH The existence of a true compensation function obviously influences the iniviual in his or her choice of insurance coverage within bonusmalus insurance contracts. Recall hereby our introuctory questions in section 1: Shoul or shoul not an iniviual buy insurance? An if so, what insurance coverage shoul he or she prefer? Or more precisely: What is the optimal insurance coverage for the iniviual? As pointe out earlier these questions have traitionally been treate within the framework of insurance economics; in general see e.g. Borch (199), chapter 2.1, 2.9, 6. an 6.4, or a more upate overview in Aase (199), chapter 8. A brief summary of this classical treatment is as follows: Consier the insurance customer an the insurance contract escribe in section 2. Assume w to be the certain initial wealth of the customer. Assume the risk taking preference of the customer to be represente by expecte utility Eu( ), that is, facing an uncertain choice the customer is assume to maximize his expecte utility of wealth. Or more precisely: The customer will prefer an uncertain wealth W 1 to another uncertain wealth W 2 if Eu(W 1 ) Eu(W 2 ). The preference perio of the customer is assume to be oneperio, which is the usual contractual perio in nonlife insurance. Note that even if the loss of bonus is accumulate over many years, the customers act on the present value of the loss of bonus, an hence the oneperio preference perio is a consistent assumption in this context. Classical optimal conition: For the moment consier the classical point of view where the insurance contract has no bonusmalus ajustments. Thus the necessary conition for the customer to purchase a coverage c( ) for a premium p is: Eu ( w  X + c( X)  p) $ Eu ( w  X). () In other wors; the customer prefers to buy an insurance coverage c( ) if the expecte utility of the coverage is greater than or equivalent to the expecte utility of not buying insurance at all. Note that within this framework the ranom variable X represents the total risk exposure of the customer, which not only inclues the uncertain loss amount, but also the uncertain probability of loss occurrence. The probability istribution of X, f(x), is hereby a mixe istribution, containing the probability that no accient occurs at the mass point x = an, conitional on one or more accients, a continuous loss size istribution for x >.
4 178 JON HOLTAN There may of course exist more than one coverage which satisfies (). Hence the optimal choice of insurance coverage is the one which maximizes the left han sie of () with respect to the function c( ) an the function p, where p in this context obviously must epen on c( ). Bonusmalus optimal conition: Let us now consier the situation where the insurance contract contains bonusmalus ajustments. Thus () is not longer a vali purchasing conition for the customer. The correcte optimal conition is rather influence by the generalize true compensation function which was efine by (2). More precisely, the necessary conition for the customer to purchase a contractual coverage for a premium p is simply: Eu ( w  X + c*( X) p) $ Eu ( w  X). (4) In (4) p follows the rules of a general bonusmalus system an is also a function of c( ). If (4) hols for at least one contractual coverage c(x), then the bonusmalus optimal choice of coverage is simply the one which maximizes the left han sie of (4). Within the framework of bonusmalus insurance contracts conition (4) will obviously influence a wie specter of classical propositions an statements within the theory of optimal insurance coverage. In sections 46 some of these classical propositions are presente an thereafter correcte by the effect of the true compensation function within a bonusmalus framework. 4. THE INDIFFERENT PREMIUM Classical proposition I: Assume the classical framework of a stanar insurance contract with no bonusmalus ajustments. The maximum premium the customer will pay for the insurance coverage is the premium p = p max which generates a = instea of a in (). The premium p max is hence the premium where the customer is inifferent between buying an not buying the insurance coverage, an is therefore also calle the inifferent premium. The existence of such a premium is actually one of the axioms of the von Neumann Morgenstern utility theory. The utility function u( ) is usually assume to be concave an monotonically increasing, i.e. u ( ) > an u ( ) <, which means that the customer is assume to be risk averse. Hence, by trivial use of Jensen s inequality, we may fin that p > Ec( X), max (5) which is one of the key propositions in insurance economics. A practical interpretation of (5) is that a risk averse customer is willing to participate in an unfair game (p max = Ec(X) is a fair game).
5 OPTIMAL INSURANCE COVERAGE UNDER BONUSMALUS CONTRACTS 179 Bonusmalus proposition I: Inifferent premium Within the framework of a bonusmalus contract the inifferent premium satisfies pmax > Ec*( X), (6) where c*( ) is efine by (1). Proof: From Jensen s inequality it follows that u(w EX)>Eu(w X) since u ( ) <. Hence the equality sign in (4) will hol for some p max > Ec*(X). The practical interpretation of (6) is in fact the same as for (5), that is, a risk averse customer is willing to participate in an unfair game, but the unfair premium limit (the inifferent premium) is ifferent between (5) an (6). 5. OPTIMAL COVERAGE FOR A FIXED PREMIUM FUNCTION The two introuctory questions in section 1 concern the problem of rational insurance purchasing for a fixe set of bonusmalus contracts offere by the insurer. In other wors, the terms of the insurance contract are assume to be exogenously specifie an impose on the insurance customer. This approach reflects a realistic purchasing situation in an insurance mass market, where the customers just within certain limits have possibilities to influence the terms of the insurance contract. The next proposition give attention to a classical statement an to a corresponingly bonusmalus statement within such an exogenous point of view. The contractual compensation assumes to be on excess of loss form, which is probably the most common contractual compensation form in the worl wie insurance market. Classical proposition II: Assume the classical framework of a stanar insurance contract with no bonusmalus ajustments. Assume the contractual compensation to be c(x) = max[x,], where is the contractual excess point, an the premium to be p() = (1 + g)ec(x)+k, where g is a safety loaing factor an k is a flat cost fee. If g = (an w > p()+) an k is not too high, it is always optimal to buy maximal contractual coverage, that is, = is the optimal choice of insurance coverage. If k is too high, the only alternative is not to buy insurance at all. This classical statement is e.g. outline in Borch (199), pp. 4. As we will fin, this statement of maximal coverage is also vali uner bonusmalus contracts. The point is, however, that the specification of maximal coverage is ifferent uner bonusmalus contracts.
6 18 JON HOLTAN Bonusmalus proposition II: Within the framework of a bonusmalus contract assume the contractual compensation to be c(x) = max[x,], where is the contractual excess point, an the premium to be p() = (1 + g)ec*(x)+k, where g is a safety loaing factor an k is a flat cost fee. If g = (an w > p()++) an k is not too high, it is always optimal to buy maximal true coverage, that is, a value of which gives z () = 1 is the optimal choice of insurance coverage. If k is too high, the only alternative is not to buy insurance at all. Remark: It is not obvious that insurance companies explicitly calculate Ec*(X) in the premium expression p() = (1 + g)ec*(x)+k. However, implicitly they o because they use the actual reportet claims which are affecte by the bonus hunger of the customers as ata input to the risk premium estimation. Proof: From (1) the true compensation is c*(x) = max[x,], where the bonus hunger excess point obviously is a function of since p() epens on. The optimal coverage maximizes the left han sie of (4). Hence we have: U () = Euw 6  X+ c*( X)  p + = uw 6 x p fxx () + uw 6 z () p fxx (). (7) # # + The first orer conition for a maximum is U () =. Hence by straightforwar calculus we fin: + U'( ) =p () # u 6 f() x x We have: ( 1 + z () + p ()) u 6 wz( ) p( f( x) x. (8) # + p () = ( 1+ g) Ec*( X) + k= ( 1+ g) # ( x ) f() x x+ k, (9) which gives: + p ( ) = ( 1+ g)( 1+ z ( )) # f( x) x, (1) +
7 OPTIMAL INSURANCE COVERAGE UNDER BONUSMALUS CONTRACTS 181 an hereby: R+ V S W 1 + z () + p () = ( 1 + z ()) # f() x x g # f() x x. S + W T X (11) Hence, by substituting (1) an (11) into (8), followe by straightforwar calculus, we fin: U () = 61 + z f() x x: # + R + S ( 1 + g) # 6 u ( wp( ) x)u ( wp( )  z( f( x) x+ gu ( wp( )  S T V W  z ()) # fxx () + W X (12) Since u ( ) > an u ( ) <, we observe from (12) that if g = an w > p() + +, we have From (12) we also have generally U () = if an only if z () = 1. U () > if z () < 1 U () < if z () > 1, which implies that z () = 1 is a maximum point of U(), as shown illustratively in figure 1: U() U () > U () < z () 2 1 FIGURE 1
8 182 JON HOLTAN Hence, if g = an w > p()++, then a value of which gives z () = 1 generates an optimal coverage solution (maximal expecte utility) for the customer. If D() = + = the true euctible, then D () = 1 + z (), an hence D () = if z () = 1. Since D () < when z () < 1 an D () > when z () > 1, then z () = 1 represents a minimum point of D(). This minimum is greater than zero because > for all. From (1) we have corresponingly p () = if an only if z () = 1. Since p () > when z () < 1 an p () < when z () > 1, then z () = 1 represents a maximum point of p(). Hence we conclue: z () = 1 generates a maximum value of the premium p() an a minimum value of the true euctible +, which together gives maximal true coverage. In other wors, maximal true coverage gives maximal expecte utility for the customer, given that g = in the assume premium function. Note that there may exist more than one value of satisfying z () = 1; call them max. All other values ifferent from max give lower expecte utility from the customers point of view. Figure 2 gives an illustrative interpretation of this result by illustrating the existence of a tangent line z () = 1 touching in the maximum expecte utility point max. For simplicity the figure assumes the existence of just one maximum point max satisfying z () = 1 an a bonusmalus contract with ecreasingly premium reuction generate by the euctible. FIGURE 2 The bonusmalus rules of the contract, the market rate l an the iniviual premium level at the purchasing time, ecie the iniviual value(s) of max as well as the ecrease expecte utility for values of ifferent from max. This quite complex an iniviual epenent conclusion reflects to some extent the practical purchasing situation: Both the insurance company an the insurance
9 OPTIMAL INSURANCE COVERAGE UNDER BONUSMALUS CONTRACTS 18 customers fin it ifficult to recommen an choose an iniviual contractual euctible uner bonusmalus contracts. An, if the premium reuction generate by the contractual euctible has an upper limit (as most insurers have), there may not for some customers exist iniviual value(s) of max at all. Hence, these customers shoul probably not buy the insurance coverage either. These customers are typically customers with low bonus level, high premium level an har malus rules in an economic market with low market rate l. On the other han, customers with high bonus level, low premium level an nice malus rules in an economic market with high market rate l, shoul obviously buy the insurance coverage an choose max as the contractual euctible. To summarize this section we conclue within our bonusmalus moel: Given an excess of loss contractual compensation an a premium function without a safety loaing factor, then there exists a specific choice of contractual coverage which gives maximum expecte utility compare to other choices of coverage. This optimal contractual coverage is efine when the true insurance coverage is maximal, that is, when z () = 1. This conclusion is in accorance with the corresponingly stanar insurance contract without bonusmalus ajustments, where maximal (contractual) coverage is optimal for the customers. Note that even if we in our moel have efine the true euctible as a net present value base on an infinitehorizon consieration of the loss of bonus, the above conclusions will also hol for other consierations an assumptions of. The only conition is that epens on in some way. 6. PARETO OPTIMAL COVERAGE The conclusion in section 5 leas to a more general approach of eriving the optimal insurance coverage uner bonusmalus contracts. A reverse key question is hereby: What is the optimality of a bonusmalus contract in an insurance market? An even more critical: Does there exist such an optimality at all? These problems involve Pareto optimal analysis techniques, where both the insurance customer an the insurer is analyze from a risksharing point of view. Hence consier a general insurance contract with bonusmalus ajustments. The necessary conition for the insurer to offer the true compensation c*(x) = max[c(x)z(p), ] for a premium p is obviously Eu ( w  c*( X) + p) $ u ( w ), (1) where u ( ) is the utility function of the insurer satisfying u ( ) > an u ( ), w is the initial wealth of the insurer an p follows the rules of a general bonusmalus system. In orer for a bonusmalus contract to be acceptable to both the insurer an the customer, both (1) an (4) have to be satisfie. If such a contract exists at all, then the Pareto optimal contract is the one which maximizes the total riskexchange utility for the insurer an the insure, that is, the contract which maximizes the left han sie of (4) an (1). This
10 184 JON HOLTAN simple riskexchange moel is hereafter referre to as the stanar riskexchange moel, which is e.g. part of Borch s classical 196theorem of Pareto Optimality. Within this framework Borch s theorem says in fact that a sufficient conition that our (bonusmalus) contract is Pareto Optimal is that there exist positive constants k an k such that ku ( w+ p  c*( X)) = ku ( w p  X + c*( X)), which mathematically expresses a common linear maximizing of the left han sie of both (4) an (1). See Borch (199), chapter 2.5 or Aase (199), chapter, for a more etaile presentation. We have: Bonusmalus proposition III: A bonusmalus contract can not be Pareto Optimal within the stanar riskexchange moel. Proof: A irect application of Borch s Theorem gives the first orer conition for the Pareto optimal sharing rule between the insurer an the customer u ( w + p c*( X)) = bk k l u ( wp X+ c*( X)), (14) where k an k are arbitrary positive constants. Following Aase (199), chapter 8, a ifferentiating of (14) with respect to X leas to ( *( )) X c *( X Rw p X c X ) =, R ( w + p c*( X)) + R( wp X+ c*( X)) (15) where R an R are the ArrowPratt measures of absolute risk aversions for the customer an the insurer. If both the customer an the insurer are risk averse, then irectly from (15) we establish the general Pareto optimal criteria < 2 2 X c *( X )< 1 for all X. (16) On the other han, uner bonusmalus contracts we have c*(x) = max[c(x) z(p), ]. Hence *( ) 2 2 X c X = for c(x) z(p), an hence quite generally the Pareto optimal criteria (16) will not hol for all X. Uner stanar insurance contracts without bonusmalus ajustments the corresponing proposition is as follows; see Aase (199), chapter 8, for a general proof which follows the same lines as the above proof: Classical proposition III: The Pareto optimal sharing rule of a stanar insurance contract without bonusmalus ajustments involves a positive amount of coinsurance within the stanar riskexchange moel. A contractual compensation with a euctible can, however, not be Pareto optimal within the stanar riskexchange moel.
11 OPTIMAL INSURANCE COVERAGE UNDER BONUSMALUS CONTRACTS 185 Proposition 2 in Holtan (21) states that inepenent of the contractual compensation function, the true compensation function has always an iniviual euctible uner bonusmalus contracts. Hence, given the stanar riskexchange moel, it is intuitively correct that the Pareto optimal statement for stanar contracts with a euctible is vali in general for bonusmalus contracts. As conclue in Aase (199), chapter 8, stanar insurance contracts with a euctible can only be Pareto optimal in moels where one or more of the following are inclue; costs, moral hazar, asymmetric information, nonobservability or alternative preferences (e.g. starshape utility). Stanar references within this context are: Arrow (1974), who inclue a fixe percentage (cost)loaing to show optimality of euctibles, Raviv (1979), who foun that a euctible is Pareto optimal if an only if the insurance costs epens on the insurance coverage, Rothschil & Stiglitz (1976), who inclue asymmetric information an foun that lowrisk iniviuals woul choose high euctibles, an Holmstrøm (1979), who foun that moral hazar gives rise to euctibles. These expane moel assumptions are in general in accorance with the main intentions of a bonusmalus system in an insurance market: 1) Averse selection: Measure an smooth out asymmetric information by iniviual a posteriori tariffication. 2) Moral hazar: Reuce the claim probability by economic punishment. ) Costs: Reuce the aministrative costs generate by claims hanling. Hence, since no one of these intentions was inclue in the moel in this paper, we put forwar the following conjecture: Conjecture: A bonusmalus contract can only be Pareto optimal if the riskexchange moel inclues one or more of the bonusmalus intentions 1. Proposition an 4 in Holtan (21) state that the compensation function of a bonusmalus contract without a contractual euctible is equivalent to the compensation function of a stanar insurance contract with an iniviual euctible. Hence it shoul be easy to formally prove the existence of the conjecture for bonusmalus contracts without a contractual euctible. On the other han, if we o not restrict a bonusmalus contract in this way, then the size of the loss of bonus euctible epens on the iniviual choice of the contractual euctible, cf. the iscussion in section 5. This epenency complicates the Pareto optimal analysis, an hence also the proof of the above conjecture. As a concluing remark to the above iscussion, we may point out that orinary euctibles are usually use in the insurance market as the main instrument to reuce the claim probability (moral hazar) an to reuce the costs generate by claims hanling. Therefore, the main intention of a bonusmalus system is to hanle the problem of averse selection generate by iniviual asymmetric information (even if Holtan (1994) outlines a moel with high euctibles finance over a perio of time as an averse selection alternative to bonusmalus systems). Hence, as a general rule bonusmalus systems shoul only be use if iniviual loss experience is a significant risk parameter within the insurance market.
12 186 JON HOLTAN 7. SUMMARY The paper outlines some new statements of optimal insurance coverage uner bonusmalus contracts an compares them with corresponing classical statements uner stanar insurance contracts. The theoretical framework is an expecte utility moel, but neither averse selection, moral hazar nor costs are part of the moel. Uner the assumption of an excess of loss contractual compensation an a premium function without a safety loaing factor, it is outline that maximal true coverage gives maximal expecte utility for the customers. This result is in accorance with classical theory of stanar contracts without bonusmalus ajustments. On the other han an within the same expecte utility moel, it is outline that bonusmalus contracts are not optimal to both the customers an the insurer at the same time, that is, Pareto optimal. The conjecture in section 6, which is not formally prove, states as a natural consequence that bonusmalus contracts can only be Pareto optimal if averse selection, moral hazar an/or costs are inclue in the analysis moel. ACKNOWLEDGMENT This paper was presente at the th International ASTIN Colloquium Tokyo, Japan, August REFERENCES AASE, K.K. (199) Equilibrium in a reinsurance synicate; Existence, uniqueness an characterization. ASTIN Bulletin 2, ARROW, K.J. (1974) Optimal insurance an generalize euctibles. Skaninavisk Aktuarietisskrift, BORCH, K.H. (199) Economics of Insurance. Avance Textbooks in Economics Series. (Eitors: Knut K. Aase an Agnar Sanmo), North Hollan; Amsteram, New York, Oxfor, Tokyo. HOLMSTRÖM, B. (1979) Moral hazar an observability. The Bell Journal of Economics 1, HOLTAN, J. (1994) Bonus Mae Easy. ASTIN Bulletin 24, HOLTAN, J. (21) Optimal Loss Financing uner BonusMalus Contracts. This volume of ASTIN Bulletin. MOSSIN, J. (1968) Aspects of rational insurance purchasing. Journal of Political Economy 76, RAVIV, A. (1979) The esign of an optimal insurance policy. American Economic Review 69, ROTHSCHILD, M. an STIGLITZ, J. (1976) Equilibrium in competitive insurance markets. An essay on the economics of imperfect information. Quarterly Journal of Economics 9, JON HOLTAN if P&C Insurance Lt. P.O. Box 14 Vika N114 Oslo, Norway
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