on the moments of the time to ruin distribution when the initial reserve is large and claim amount distribution is two stage hypo exponential distribution

نویسندگان

vivekananda mutry mushini

andhra university, visakhapatnam, india arti golthi

gitam university visakhapatnam nirupama devi k

andhra university visakhapatnam

چکیده

in any classical risk model one of the important random variable is time to ruin. as time to ruin warns the management for possible adverse situations that may arise, the distribution of time to ruin place a vital role in the day to day transactions of the any insurance company. moments of the distribution are also important as coefficient of skewness of the distribution is very important in actuarial science. the results developed are applied to a real life situation. data was collected on the amount of claims from the offices and agents of the general insurance company situated in east godavari district of andhra pradesh state in india. and for that data suitable distribution for the claim amount was fitted and found that the best suited distribution is two state exponential. this paper is on the distribution of time to ruin and its moments of the time to ruin for large values of the initial reserve when claim amount distribution is two stage hypo exponential distribution and further analyzed using the theoretical developments made in this paper.

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On The Moments Of The Time To Ruin Distribution When The Initial Reserve Is Large And Claim Amount Distribution Is Two Stage Hypo Exponential Distribution

In any classical risk model one of the important random variable is time to ruin. As time to ruin warns the management for possible adverse situations that may arise, the distribution of time to ruin place a vital role in the day to day transactions of the any insurance company. Moments of the distribution are also important as coefficient of skewness of the distribution is very important in ac...

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عنوان ژورنال:
international journal of management academy

جلد ۱، شماره ۲، صفحات ۱۲۱-۱۳۳

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