2021 ◽  
Vol 1725 ◽  
pp. 012081
Author(s):  
N A Anggraini ◽  
S Nurrohmah ◽  
S F Sari

1982 ◽  
Vol 13 (2) ◽  
pp. 133-134 ◽  
Author(s):  
Hans U. Gerber

Let u(x) be a utility function, i.e., a function with u′(x)>0, u″(x)<0 for all x. If S is a risk to be insured (a random variable), the premium P = P(x) is obtained as the solution of the equationwhich is the condition that the premium is fair in terms of utility. It is clear that an affine transformation of u generates the same principle of premium calculation. To avoid this ambiguity, one can standardize the utility function in the sense thatfor an arbitrarily chosen point y. Alternatively, one can consider the risk aversionwhich is the same for all affine transformations of a utility function.Given the risk aversion r(x), the standardized utility function can be retrieved from the formulaIt is easily verified that this expression satisfies (2) and (3).The following lemma states that the greater the risk aversion the greater the premium, a result that does not surprise.


2017 ◽  
Vol 54 (1) ◽  
pp. 286-303 ◽  
Author(s):  
Claude Lefèvre ◽  
Philippe Picard ◽  
Matthieu Simon

AbstractIn this paper we aim to apply simple actuarial methods to build an insurance plan protecting against an epidemic risk in a population. The studied model is an extended SIR epidemic in which the removal and infection rates may depend on the number of registered removals. The costs due to the epidemic are measured through the expected epidemic size and infectivity time. The premiums received during the epidemic outbreak are measured through the expected susceptibility time. Using martingale arguments, a method by recursion is developed to calculate the cost components and the corresponding premium levels in this extended epidemic model. Some numerical examples illustrate the effect of removals and the premium calculation in an insurance plan.


2020 ◽  
Vol 9 (3) ◽  
pp. 182
Author(s):  
MIFTAAHUL JANNAH ◽  
AGUS SUPRIATNA ◽  
RIAMAN RIAMAN

Joint life insurance is life insurance with an amount of more than one person, where the benefits are paid when one of the insured dies. The possibility of insurance companies will suffer losses if the claims that occur are more than predicted, so the premium reserve calculation is required. In this study, reserves were calculated using the Fackler method based on the Indonesian Mortality Table 2011 and the Makeham Assumption Mortality Table. The Indonesian Mortality Table 2011 was analyzed for the estimated parameters contained in the Makeham Assumption Mortality Table. Then the premium calculation and premium reserve calculation are done using the Fackler method based on the Makeham Assumption Mortality Table and the comparison uses the Indonesian Mortality Table 2011. The results of the calculation of the premiums based on the Makeham Assumption Mortality Table are greater than using the Indonesia Mortality Table 2011, while the premium reserve results are greater using the Indonesian Mortality Table 2011 than using the Makeham Assumption Mortality Table. This is because the chances of survival based on the Makeham Assumption Mortality Table are smaller than the Indonesian Mortality Table 2011.


1985 ◽  
Vol 15 (2) ◽  
pp. 89-101 ◽  
Author(s):  
Hans Bühlmann

This paper is intended to show how premiums are related to the stability criterion imposed on a portfolio of risks and to the dividend requirements for the capital invested into the insurance operation. The point is that premium calculation should be seen as a consequence of the strategic concepts adopted by the insurance carrier.


1982 ◽  
Vol 13 (2) ◽  
pp. 135-149 ◽  
Author(s):  
Franco Moriconi

A great attention has been devoted, in the actuarial literature, to premium calculation principles and it has been often emphasized that these principles should not only be defined in strictly actuarial terms, but should also take into account the market conditions (Bühlmann (1980), de Jong (1981)).In this paper we propose a decision model to define the pricing policy of an insurance company that operates in a market which is stratified in k risk classes .It is assumed that any class constitutes a homogeneous collective containing independent risks Sj(t) of compound Poisson type, with the same intensity λj. The number nj of risks of that are held in the insurance portfolio depends on the premium charged to the class by means of a demand function which captures the concept of risk aversion and represents the fraction of individuals of , that insure themselves at the annual premium xj.With these assumptions, the return Y on the portfolio is a function of the vector x = (x1, x2, …, xk) of the prices charged to the single classes (and of the time) and x is therefore the decision policy instrument adopted by the company for the selection of the portfolio, whose optimal composition is evaluated according to a risk-return type performance criterion.As a measure of risk we adopt the ultimate ruin probability q(w) that, in the assumptions of our model, can be related to a safety index τ, by means of Lundberg-de Finetti inequality. Even though it has been widely debated in the actuarial field, the use of q(w) offers undeniable operational advantages. In our case the safety index τ can be expressed as a function of x and therefore, in the phase of selecting an efficient portfolio, it becomes the function to be maximized, for a given level M of the expected return.


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