It is important for an
insurance company to predict the future claims in order to evaluate premiums,
to determine the reserve necessary to meet its obligation and probabilities of
ruin, etc. as the claim data is highly positively skewed and has heavy tail, no
standard parametric model seems to provide an acceptable fit to both small and
large losses. Composite models that use one standard distribution up to a
threshold and other standard distribution thereafter are developed and it is seen
that these composite models provide better fit than the standard models when
claim data involve small and high claims.
The aim of this study is to
investigate the use of the composite models namely Exponential-Pareto,
Weibull-Pareto and Lognormal-Pareto to model the Turkish Motor Insurance claim
data. From the results obtained, it is concluded that the composite
Weibull-Pareto model provides better fit to Turkish Motor Insurance claim data
than the all other models considered.
It is important for an
insurance company to predict the future claims in order to evaluate premiums,
to determine the reserve necessary to meet its obligation and probabilities of
ruin, etc. as the claim data is highly positively skewed and has heavy tail, no
standard parametric model seems to provide an acceptable fit to both small and
large losses. Composite models that use one standard distribution up to a
threshold and other standard distribution thereafter are developed and it is seen
that these composite models provide better fit than the standard models when
claim data involve small and high claims.
The aim of this study is to
investigate the use of the composite models namely Exponential-Pareto,
Weibull-Pareto and Lognormal-Pareto to model the Turkish Motor Insurance claim
data. From the results obtained, it is concluded that the composite
Weibull-Pareto model provides better fit to Turkish Motor Insurance claim data
than the all other models considered.
Primary Language | English |
---|---|
Journal Section | Articles |
Authors | |
Publication Date | August 23, 2019 |
Published in Issue | Year 2019 |