DETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY

An insurance company need to manage the insurance risk to avoid the error in measuring the risk. The substantial risk faced by insurance policyholders is referred to as a claims severity. The significant number of claims with extreme large claims may occur. In this thesis, claims severity data is an...

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Main Author: Suciana Arnel, Silfia
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/50193
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:50193
spelling id-itb.:501932020-09-23T08:23:34ZDETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY Suciana Arnel, Silfia Indonesia Theses Threshold, Extreme Value Theory, Peak Over Threshold, Value at Risk INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/50193 An insurance company need to manage the insurance risk to avoid the error in measuring the risk. The substantial risk faced by insurance policyholders is referred to as a claims severity. The significant number of claims with extreme large claims may occur. In this thesis, claims severity data is analyzed using an Extreme Value Theory (EVT) model with the Peak Over Threshold (POT) approach. In specifying an extreme value using the POT method will assigned some candidates of a limit of threshold. Threshold obtained from fixed variable is specified with mean excess function plot. While the Threshold obtained from random variables is determined using the minimum upper limit of the claim severity for each year.The threshold candidate will be tested using the Kolmogorov Smirnov test to get a threshold value that matches with the Generalized Pareto distribution. The matching value will be used in performing calculating premium and a reserved claim. In applying this method, will use broad data claims on property insurance that occurred in the year 2010-2016. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description An insurance company need to manage the insurance risk to avoid the error in measuring the risk. The substantial risk faced by insurance policyholders is referred to as a claims severity. The significant number of claims with extreme large claims may occur. In this thesis, claims severity data is analyzed using an Extreme Value Theory (EVT) model with the Peak Over Threshold (POT) approach. In specifying an extreme value using the POT method will assigned some candidates of a limit of threshold. Threshold obtained from fixed variable is specified with mean excess function plot. While the Threshold obtained from random variables is determined using the minimum upper limit of the claim severity for each year.The threshold candidate will be tested using the Kolmogorov Smirnov test to get a threshold value that matches with the Generalized Pareto distribution. The matching value will be used in performing calculating premium and a reserved claim. In applying this method, will use broad data claims on property insurance that occurred in the year 2010-2016.
format Theses
author Suciana Arnel, Silfia
spellingShingle Suciana Arnel, Silfia
DETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY
author_facet Suciana Arnel, Silfia
author_sort Suciana Arnel, Silfia
title DETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY
title_short DETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY
title_full DETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY
title_fullStr DETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY
title_full_unstemmed DETERMINING RESERVE OF THRESHOLD IN A RANDOM VARIABEL FOR EXTREM CLAIM SEVERITY
title_sort determining reserve of threshold in a random variabel for extrem claim severity
url https://digilib.itb.ac.id/gdl/view/50193
_version_ 1822000587786420224