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Edwin Setiawan Nugraha
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Kota Jababeka, Cikarang, Kabupaten Bekasi, Jawa Barat
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INDONESIA
Journal of Actuarial, Finance, and Risk Managment
Published by President University
ISSN : -     EISSN : 28303938     DOI : -
Core Subject : Economy, Education,
Economics, Econometrics & Finance Mathematics
This journal aims to provide high quality articles covering any and all aspects of the most recent and significant developments in the actuarial, financial, and risk management.
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 2, No 2 (2023)" : 5 Documents clear
Application of the Historical Burn Analysis Method in Determining Rainfall Index for Crop Insurance Premium Using Black-Scholes Agus Sofian Eka Hidayat; Agnes Crycencia Sembiring
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i2.4806

Abstract

Indonesia is an agricultural country where rice sector has a high risk of production loss or crop failure. Agricultural Insurance is one of the government programs that helps farmers secure their farms. The aim of this final assignment is to obtain rainfall index to determine crop insurance premiums. The rainfall index is being carried out using Historical Burn Analysis which will produce exit values and trigger values for the index window January 2014 - December 2022 using secondary data from the Bali Climatology Station. In this study, Microsoft Excel 2016 was used as an application tool. The Black-Scholes method is used to calculate the premium in Jembrana Regency. The calculation of the rainfall index using the Historical Burn Analysis formula in determining agricultural insurance premiums using Black-Scholes can be used very well, the result show that the insurance premium value is very high where the lowest premium is IDR 6,654,075 and the highest premium is IDR 6,781,555.
Value at Risk Calculation of Digital Bank Stocks Portfolio in Indonesia Steffany Indra Gunawan; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i2.4810

Abstract

Nowadays, stock investment has been increasingly growing in society. In investing activities, there are risks that may be experienced by investors. However, sometimes many investors do not realize how much risk that they might suffer in the future. One way that can be done to measure this risk is to calculate the Value at Risk (VaR). This study aims to calculate the VaR value of digital bank stock portfolio in Indonesia. The calculation of VaR will be done using two methods, include the Historical Simulation and Monte Carlo Simulation method. From the calculation, VaR with Historical Simulation and Monte Carlo sequentially generate results of IDR 6,006,718 and IDR 10,797,904 for 99% confidence level, IDR 4,135,857 and IDR 5,376,949 for 95% confidence level, and IDR 3,219,885 and IDR 3,417,553 for 90% confidence level. Based on the results, it is found that VaR result is directly proportional to the confidence level used. Through the calculation results, it also found that VaR value with the Monte Carlo Simulation method are greater than those with the Historical Simulation method.
Forecasting the Weekly Stock Price of PT. OCBC NISP Tbk. using Auto Regressive Integrated Moving Average Elisabeth Gloria Manurung; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i2.4812

Abstract

Stocks are widely used in financial markets and can be an option for companies seeking to raise funds. Additionally, investors often opt for stocks as an investment due to their potential for providing high returns. To aid investors in making informed decisions when buying and selling stocks and mitigating risks, professionals have developed different theories and analyses to forecast stock prices. Auto Regressive Integrated Moving Average (ARIMA) (p,d,q) technical analysis will be used in this study to predict the weekly stock price of PT Bank OCBC NISP Tbk (NISP.JK) for 7 weeks from Jan 7, 2022 to February 18, 2022. In this study, historical weekly stock price data for PT. Bank OCBC NISP Tbk (NISP.JK) from 1 January 2021, to 31 December 2021 was collected from Yahoo Finance website to create a forecast. The researches got 12 different ARIMA models, then the researcher determined that the second model (ARIMA (2,2,1) was the most effective. This model was chosen because it has second lowest AIC value and lowest MSE, RMSE, and MAE.
Comparison Between Machine Learning Regression Modelling to Predict Individual Premium Price Srava Chrisdes Antoro; Elisabeth Gloria Manurung; essykapna Randalline; Maria Yus Trinity Irsan
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i2.4807

Abstract

Machine Learning (ML) applications in healthcare aim to simplify people's lives by swiftly predicting and diagnosing diseases, outpacing the capabilities of most medical experts. A direct connection is established when technology, particularly digital health insurance, is employed to minimize the gap between insurance providers and policyholders. This has significantly transformed the way insurers create health insurance policies and has led to faster service delivery for consumers. Machine learning is utilized by insurance companies to offer clients precise, prompt, and efficient health insurance coverage. In this study, a regression method was trained and assessed to which one gets the bigger accuracy to forecast premium prices. The researchers accurately predicted the premium prices individuals incur based on various factors, such as age, diabetes, blood pressure issues, height, and weight. The experimental outcomes revealed the best method to predict is the KNN method in the data set that was used in the analysis, with an impressive accuracy of 87.73%. In comparison, the Random Forest is 87%, and the Boosting is 87.19% and the authors analyzed the model's performance using key metrics to assess its effectiveness. 
Annual Premium Calculation On Single Life Insurance using Gompertz Mortality Assumptions Michelle Novia; Fauziah Nur Fahirah Sudding
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Premium calculation is one of the important aspects to insurance companies. Given the importance of premiums in insurance contracts for insurance companies, determining the price of the premium must also be appropriate. 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 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 which then be used to find the life annuity-due that will be needed to compute the premium calculation of Gompertz assumptions. 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.

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