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Article . 2021
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Evaluating Ruin Probabilities: A Streamlined Approach

Evaluating ruin probabilities: a streamlined approach
Authors: Paolo De Angelis; Roberto De Marchis; Mario Marino; Antonio Luciano Martire; Immacolata Oliva;

Evaluating Ruin Probabilities: A Streamlined Approach

Abstract

Summary: This paper deals with the ruin probability evaluation in a classical risk theory model, under different hypotheses about claims distribution. Our approach is totally innovative, and is based on the application of the mean-value theorem to solve the associated Volterra integral equation. The numerical experiments show that the procedure we are proposing works well in all circumstances, compared to other pre-existing methodologies.

Country
Italy
Keywords

Ruin probabilities; Volterra equations; Mean-Value Theorem; Numerical approximation, Ruin probabilitie, Numerical approximation, Volterra integral equations, Ruin probability; Mean-Value Theorem; Exponential distribution; Weibull distribution; Pareto distribution; Gamma distribution, Volterra equation, Risk models (general), Mean-Value Theorem, Numerical methods for integral equations

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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