VALUE AT RISK ANALYSIS USING AUTOMATEDTHRESHOLD SELECTION METHOD FORPROPERTY INSURANCE

One of the efforts taken to minimize financial losses is insurance. Insurance companies as providers of insurance products need to carry out risk management so that there are no errors in risk measurement. The amount of risk or loss experienced by policyholders is referred to as the size of the c...

Full description

Saved in:
Bibliographic Details
Main Author: Daniah Pahrany, Andi
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/50042
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:One of the efforts taken to minimize financial losses is insurance. Insurance companies as providers of insurance products need to carry out risk management so that there are no errors in risk measurement. The amount of risk or loss experienced by policyholders is referred to as the size of the claim.In measuring risk, for the assumption of normal events, a measurement using the Value at Risk (VaR) is generally used. But in reality, the size of claims is not always in the same condition or circumstances, there are times when there are very large claims with a small frequency which have a very big impact on the insurance company. Therefore, in this thesis, a major analysis of claims will be carried out using extreme value theory which is specialized by applying peaks over threshold (POT) approach which will be modeled following Generalized Pareto Distribution. Automated Threshold Selection Method will be used to select threshold, based on the ditribution of the difference of parameter estimates when the threshold is changed, and apply it to published claim severity. The threshold will then be tested using the Kolmogorov Smirnov test. The threshold value obtained is used to measure the risk with the value at risk which will be used in calculating the reserve for claims. In the application of the method, the claim size data is used for asset insurance with occupation code 293 (trading and storage) and occupation 297 (private buildings) which occurred in 2010- 2016. With a significance level of 0.05, for 293 occupation data it is modeled following the GP distribution with the parameters ^ = 1:230, ^ = 2:811 107 and threshold u = 3; 745 billion, for 297 occupation data modeled following the GP distribution with the parameter ^ = 0:935, ^ = 1; 733 107 and threshold u = 913 million. The VaR risk is used to determine the risk value of the claim distribution. As an illustration, the claim reserve scheme is based on the VaR value.