http://repository.president.ac.id/xmlui/handle/123456789/12334 2026-04-07T14:52:33Z 2026-04-07T14:52:33Z Gunawan, Steffany Indra http://repository.president.ac.id/xmlui/handle/123456789/12348 2024-12-03T07:31:53Z 2023-01-01T00:00:00Z

VALUE AT RISK AND EXPECTED SHORTFALL CALCULATION OF DIGITAL BANK STOCKS PORTFOLIO IN INDONESIA Gunawan, Steffany Indra Nowadays, stocks investment has been increasingly growing in the society. In investing activities, there are risks that may be experienced by investors. However, sometimes many investors do not realize how much risk they might suffer in the future. One way that can be done to measure this risk is to calculate Value at Risk (VaR) and Expected Shortfall (ES). This thesis will discuss the calculation of VaR and ES values using two methods, including the Historical Simulation and Monte Carlo Simulation method for digital bank stock portfolio. Furthermore, the VaR value will be tested for accuracy using Kupiec Backtesting method with the loglikelihood ratio approach. From the results of VaR and ES calculations using Historical Simulation method sequentially obtained results of IDR 6,006,718 and 7,474,493 for 99% confidence level, IDR 4,135,857 and IDR 5,106,761 for 95% confidence level, and IDR 3,219,885 and IDR 4,388,922 for 90% confidence level. While the results of VaR and ES calculations using Monte Carlo Simulation sequentially obtained results of IDR 10,797,904 and 15,272,779 for a 99% confidence level, IDR 5,376,949 and IDR 9,159,777 for a 95% confidence level, and IDR 3,417,553 and IDR 6,868,538 for a 90% confidence level. Based on these results it is found that the results of VaR and ES are directly proportional to the confidence level used. In this case, the backtesting test results that Monte Carlo Simulation produces a more accurate VaR value compared to the Historical Simulation.

2023-01-01T00:00:00Z Novia, Michelle http://repository.president.ac.id/xmlui/handle/123456789/12347 2024-12-03T07:29:01Z 2023-01-01T00:00:00Z

ANNUAL PREMIUM CALCULATION ON SINGLE LIFE INSURANCE USING GOMPERTZ MORTALITY ASSUMPTIONS Novia, Michelle Premium calculation is one of the important aspects to insurance companies. Careless determination of the premium price can cause the insurance company to fail to bear the risk that the company has. There are several ways to determine premium payments. In this research the premium calculation will be computed using Gompertz mortality assumptions which will be applied to the annual premium calculation of whole life and term life single life insurance of man and woman. The benefit assumed, interest rate, Insurer age, Gompertz parameter and several actuarial notations such as life annuity-due and net single premium is needed in the premium calculation using Gompertz mortality assumptions. This research uses the data of Indonesian Mortality table (TMI IV) and the Linear Least Squares (LLS) method to find the Gompertz parameter to find the survival probabilities. Based on the calculation performed in this research, the value of the premium using Gompertz assumptions is influenced by parameters on the Gompertz assumptions, the interest rate used, and the Insured age. Using the LLS method the parameter found for woman is 0.00006592 for B and 1.083 for c and the parameter for man is 0.00009501 for B and 1.082795 for c. Moreover, the value of the premium based on Gompertz mortality assumptions using Linear Least Squares (LLS) method with the same age of 30 years old for man is higher than the value of the premium for woman, with the value of IDR 6,020,436.98 for man and IDR 4,808,984.04 for woman using whole life insurance and the value of IDR 2,342,104.10 for man and IDR 1,644,897.21 for woman using term life insurance.

2023-01-01T00:00:00Z Tjian, Vinesia http://repository.president.ac.id/xmlui/handle/123456789/12346 2024-12-03T07:23:56Z 2023-01-01T00:00:00Z

COMPARISON OF PREMIUM RESERVE CALCULATION ON TERM LIFE INSURANCE USING PREMIUM SUFFICIENCY AND NEW JERSEY METHOD Tjian, Vinesia Some life insurance companies can experience losses that can even lead to bankruptcy due to not being able to pay claims. This event can be anticipated by having a reserve fund that has been prepared and calculated properly. There are several types of insurance, one of which is term life insurance where the insurance pays benefits if the insured died before the insurance period ends. The purpose of this research is to calculate the amount of term life insurance premium reserves using the Premium Sufficiency and New Jersey methods since several other researchers have also tried to calculate using these methods, such as Aprijon (2020), Filemon (2022), and others, but none of those researchers have compared these two methods. The Premium Sufficiency method calculates the reserve using gross premium assumptions, while the New Jersey method calculates the reserve using adjusted net premium. This research is a quantitative research that will use some assumptions and also secondary data to calculate the premium reserve using both methods, and using Microsoft Excel to help on the calculation and the visualization of the results. In short term, for insurance companies that have limited funds at the beginning of the insurance contract period will be more suitable to use the New Jersey method because the reserves in the initial year are 0 so that the insurance company can use the existing funds to pay additional costs. While in long term, since the overall Premium Sufficiency reserve value from year to year is smaller than the New Jersey reserve value, insurance companies that in the future need larger funds to pay for other company needs will be more suitable to use the Premium Sufficiency method because the funds that need to be set aside as reserves are smaller, so that the remaining funds that are not set aside are greater than the New Jersey method and can be used for other needs. It can also be seen that at the end of the period the premium reserve accumulated using the Premium Sufficiency method is smaller than using the New Jersey method, where the amount of premium reserve using Premium Sufficiency and New Jersey method consecutively for male are Rp. 113.200.444,943 and Rp 124.761.949,747, while for female are Rp. 70.183.195,271 and Rp. 82.491.748,159.

2023-01-01T00:00:00Z Jirene, Michelynn Sola Gratia http://repository.president.ac.id/xmlui/handle/123456789/12345 2024-12-03T07:21:07Z 2023-01-01T00:00:00Z

APPLICATION OF NAÏVE BAYES CLASSIFIER METHOD FOR PREDICTING CLAIMS IN AUTOMOBILE INSURANCE Jirene, Michelynn Sola Gratia Insurance companies often experience difficulties in planning funds. This is caused by the risk of uncertainty in life. Not least, the insurance company went bankrupt because they could not pay their obligations. For this reason, insurance companies need to know the right strategy for setting up reserve funds. One solution that can help insurance companies make decisions and determine strategies is to make claim prediction. In this study, the author will use the Naïve Bayes Classifier method to predict claims in automobile insurance. The Naïve Bayes Classifier itself is a simple probability method where the calculations are based on Bayes’ Theorem. The data used is secondary data from Kaggle.com where this data consists of 10,000 samples with 19 features. The prediction results will be divided into two results, namely 0 and 1 where 0 means "no" and 1 means "yes". "yes" or "no" label will inform us whether the customer will claim or not. The data will go through preprocessing in python so that the format is appropriate. The model will be built without feature selection and with feature selection then being compared to determine the best model. Predicted data result will be compared to the actual data and the accuracy of the best model is 82%. Other evaluation method was applied to evaluate how well the model performed by using ROC – AUC score which has a score of 0.87 and 10-fold cross validation which has an average score of 80%. The result of the prediction will help insurance company with underwriting decision and financial approach or planning.

2023-01-01T00:00:00Z