975 resultados para Ruin Probability


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In this work, we present a risk theory application in the following scenario: In each period of time we have a change in the capital of the ensurance company and the outcome of a two-state Markov chain stabilishs if the company pays a benece it heat to one of its policyholders or it receives a Hightimes c > 0 paid by someone buying a new policy. At the end we will determine once again by the recursive equation for expectation the time ruin for this company

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In the work reported here we present theoretical and numerical results about a Risk Model with Interest Rate and Proportional Reinsurance based on the article Inequalities for the ruin probability in a controlled discrete-time risk process by Ros ario Romera and Maikol Diasparra (see [5]). Recursive and integral equations as well as upper bounds for the Ruin Probability are given considering three di erent approaches, namely, classical Lundberg inequality, Inductive approach and Martingale approach. Density estimation techniques (non-parametrics) are used to derive upper bounds for the Ruin Probability and the algorithms used in the simulation are presented

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In this work, the author looks forward to develop a new method capable of incorporate the concepts of the Reliability Theory and Ruin Probability in Deep Foundations, in order to do a better quantification of the uncertainties, which is intrinsic in all geotechnical projects, meanly because we don't know all the properties of the materials that we work with. Using the methodologies of Decourt Quaresma and David Cabral, resistance surfaces have been developed utilizing the data achieved from the Standard Penetration Tests performed in the field of study, in conjecture with the loads defined in the executive project of the piles. The construction of resistance surfaces shows to be a very useful tool for decision making, no matter in which phase it is current on, projecting or execution. The surfaces were developed by Kriging (using the software Surfer® 12), making it easier to visualize the geotechnical profile of the field of study. Comparing the results, the conclusion was that a high safety factor doesn't mean higher security. It is fundamental to consider the loads and resistance of the piles in the whole field, carefully choosing the project methodology responsible to define the diameter and length of the piles

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In this work, the author looks forward to develop a new method capable of incorporate the concepts of the Reliability Theory and Ruin Probability in Deep Foundations, in order to do a better quantification of the uncertainties, which is intrinsic in all geotechnical projects, meanly because we don't know all the properties of the materials that we work with. Using the methodologies of Decourt Quaresma and David Cabral, resistance surfaces have been developed utilizing the data achieved from the Standard Penetration Tests performed in the field of study, in conjecture with the loads defined in the executive project of the piles. The construction of resistance surfaces shows to be a very useful tool for decision making, no matter in which phase it is current on, projecting or execution. The surfaces were developed by Kriging (using the software Surfer® 12), making it easier to visualize the geotechnical profile of the field of study. Comparing the results, the conclusion was that a high safety factor doesn't mean higher security. It is fundamental to consider the loads and resistance of the piles in the whole field, carefully choosing the project methodology responsible to define the diameter and length of the piles

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2000 Mathematics Subject Classification: 60B10, 60G17, 60G51, 62P05.

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2000 Mathematics Subject Classification: 60K10, 62P05

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2010 Mathematics Subject Classification: 60E05, 62P05.

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2000 Mathematics Subject Classification: 60K10, 62P05.

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We consider a spectrally-negative Markov additive process as a model of a risk process in a random environment. Following recent interest in alternative ruin concepts, we assume that ruin occurs when an independent Poissonian observer sees the process as negative, where the observation rate may depend on the state of the environment. Using an approximation argument and spectral theory, we establish an explicit formula for the resulting survival probabilities in this general setting. We also discuss an efficient evaluation of the involved quantities and provide a numerical illustration.

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In this article we propose a bootstrap test for the probability of ruin in the compound Poisson risk process. We adopt the P-value approach, which leads to a more complete assessment of the underlying risk than the probability of ruin alone. We provide second-order accurate P-values for this testing problem and consider both parametric and nonparametric estimators of the individual claim amount distribution. Simulation studies show that the suggested bootstrap P-values are very accurate and outperform their analogues based on the asymptotic normal approximation.

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This article provides an importance sampling algorithm for computing the probability of ruin with recuperation of a spectrally negative Lévy risk process with light-tailed downwards jumps. Ruin with recuperation corresponds to the following double passage event: for some t∈(0,∞)t∈(0,∞), the risk process starting at level x∈[0,∞)x∈[0,∞) falls below the null level during the period [0,t][0,t] and returns above the null level at the end of the period tt. The proposed Monte Carlo estimator is logarithmic efficient, as t,x→∞t,x→∞, when y=t/xy=t/x is constant and below a certain bound.

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A large deviations type approximation to the probability of ruin within a finite time for the compound Poisson risk process perturbed by diffusion is derived. This approximation is based on the saddlepoint method and generalizes the approximation for the non-perturbed risk process by Barndorff-Nielsen and Schmidli (Scand Actuar J 1995(2):169–186, 1995). An importance sampling approximation to this probability of ruin is also provided. Numerical illustrations assess the accuracy of the saddlepoint approximation using importance sampling as a benchmark. The relative deviations between saddlepoint approximation and importance sampling are very small, even for extremely small probabilities of ruin. The saddlepoint approximation is however substantially faster to compute.