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Advantages And Disadvantages Of Stochastic Model

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3.1 Deterministic models There are two types of model that we are going to look at, firstly the deterministic model and then the stochastic model. [23]A deterministic model is used in a situation where the result can be established straightforwardly from a series of conditions. It has no stochastic elements and both the input and the outputs are determined conclusively. On the other hand a stochastic model is one where the cause and effect relationship is stochastically or randomly determined. Therefore the system having stochastic element is generally not solved analytically and hence there are several cases for which it is difficult to build an intuitive perspective. When simulating a stochastic model a random number is usually generated …show more content…

Multiple runs are used to estimate probability distributions. Conditional simulations combine stochastic modelling and geo-statistics to improve characterization of geospatial phenomena. The behaviour of these can be described by different types of processes such as Poisson and renewal, discrete-time and continuous time Markov processes, Brownian processes and diffusion. 3.1.3 Deterministic VS Stochastic models With Deterministic models the outcome is always assumed to be certain if the input is fixed then regardless of the number of times one may re-calculate its always going to generate the same result. This is similar to a simple coin toss and a roll of a die. On the other hand the stochastic model is arguable more informative than the deterministic model since it accounts for the uncertainty due to varying behavioural characteristics. Generally speaking, a deterministic model is one whose parameters are known or assumed. They describe behaviour on the basis of some physical laws and are usually developed by statistical techniques such as linear regression, or non-linear curve fitting procedures which essentially model the average system behaviours of and equilibrium or steady state …show more content…

This is illustrated in figure 3 below. The chain ladder method explicitly relies on the assumption that the expected cumulative losses settled up to and including the development year divided by the expected cumulative claims losses settled up to and including the previous development year hold for all claim occurrence years. 3.1.4. A Loss development data Let us consider a range of risks and assume that each claim of the portfolio is settled either in the accident year or in the following n development years. The data can be modelled by cumulative losses and incremental losses. 3.1.4. B Incremental losses Let CI,J where i, j Ɛ{1.2...n} (a) represent incremental losses of accident year i which is settled with a delay of j years and therefore in development year j. Let us also assume that incremental losses C I, j are observable for calendar years i + J ≤ n and are non-observable for calendar years i + J ≥ n + 1. The runoff triangle below shows the incremental losses for accident years 2000 developing over 10 years. In this case the incremental loss for 2000 development year 5 (C2000,5) is given by 89837.06 1 2 3 4 5 6 7 8 9 10 2000 24698 58384 112485 61605 89837 36174 22525 48206 19747

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