Actuarial Modelling of Claim Counts: Risk Classification, by Michel Denuit, Xavier Marechal, Sandra Pitrebois,

By Michel Denuit, Xavier Marechal, Sandra Pitrebois, Jean-Francois Walhin

There are quite a lot of variables for actuaries to contemplate whilst calculating a motorist’s assurance top class, comparable to age, gender and kind of car. extra to those components, motorists’ charges are topic to adventure ranking structures, together with credibility mechanisms and Bonus Malus structures (BMSs).

Actuarial Modelling of declare Counts offers a complete remedy of many of the adventure ranking structures and their relationships with possibility type. The authors summarize the newest advancements within the box, offering ratemaking structures, when making an allowance for exogenous information.

The text:

  • Offers the 1st self-contained, useful method of a priori and a posteriori ratemaking in motor insurance.
  • Discusses the problems of declare frequency and declare severity, multi-event platforms, and the combos of deductibles and BMSs.
  • Introduces contemporary advancements in actuarial technological know-how and exploits the generalised linear version and generalised linear combined version to accomplish threat classification.
  • Presents credibility mechanisms as refinements of business BMSs.
  • Provides useful functions with actual information units processed with SAS software.

Actuarial Modelling of declare Counts is vital analyzing for college students in actuarial technological know-how, in addition to training and educational actuaries. it's also superb for execs taken with the assurance undefined, utilized mathematicians, quantitative economists, monetary engineers and statisticians.

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Extra resources for Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems

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1 z − 1 exp 2 z−1 = exp 1+ 2 z−1 Mixed Poisson Models for Claim Numbers 17 The sum of two independent Poisson distributed random variables is also Poisson distributed, with parameter equal to the sum of the Poisson parameters. e. the convolution of Poisson distributions is still Poisson). 7 Poisson Process Definition Recall that a stochastic process is a collection of random variables N t t ∈ indexed by a real-valued parameter t taking values in the index set . Usually, represents a set of observation times.

But how can the actuary encapsulate information about their properties relative to each other? As explained above, the key idea is to think of X1 X2 Xn T taking Xn as being components of a random vector X = X1 X2 n values in rather than being unrelated random variables each taking values in . As was the case for random variables, each random vector X possesses a distribution function FX that describes its stochastic behaviour. The distribution function of the random vector X, denoted as FX , is defined as FX x1 x2 xn = Pr X −1 − x1 × − = Pr X1 ≤ x1 X2 ≤ x2 x2 × · · · × − xn Xn ≤ x n x1 x2 xn ∈ .

The phenomena of excesses of zeros as well as heavy upper tails in most insurance data can be seen as an implication of unobserved heterogeneity (Shaked’s Two Crossings Theorem will make this clear). It is customary to allow for unobserved heterogeneity by superposing a random variable (called a random effect) on the mean parameter of the Poisson distribution, yielding a mixed Poisson model. In a mixed Poisson process, the annual expected claim frequency itself becomes random. 3 Maximum Likelihood Estimation All the models implemented in this book are parametric, in the sense that the probabilities are known functions depending on a finite number of (real-valued) parameters.

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