Open AccessPremium estimation in the fire insurance through semiparametric bootstrap
Author(s) -
W. F. Adnan,
Udjianna S. Pasaribu,
Hennie Husniah
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1722/1/012073
Subject(s) - bootstrapping (finance) , resampling , reinsurance , estimator , econometrics , goodness of fit , weibull distribution , statistics , replication (statistics) , actuarial science , computer science , mathematics , economics
Along with the development of information, science and technology, there is a quite popular developing resampling method, namely bootstrapping. Bootstrap estimates asymptotically against its original value (observation). Thus, the greater bootstrap replication, the resample distribution will be normally distributed. It indicates that the bootstrap estimator gives better results. Based on the goodness-of-fit test by using Kolmogorov-Smirnov test, the severity on fire insurance data follow Weibull 2 parameter distribution. A case study is conducted on reinsurance company’s data for shopping centre’s fire. It is a big data. Since it is a reinsurance company’s data, the data completeness may be inadequate. It causes the severity claim to be processed using semiparametric bootstrap.