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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 4, No 1 (2025)" : 5 Documents clear
Application of Projected Unit Credit Method (PUC) and Entry Age Normal (EAN) in Pension Fund Calculation Hidayat, Agus Sofian Eka; Maharani, Ni Kadek Gita; Hapsari, Nayla; Ajeng, Aprilia
Journal of Actuarial, Finance, and Risk Management Vol 4, No 1 (2025)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v4i1.5993

Abstract

This study explores the application of the Projected Unit Credit (PUC) and Entry Age Normal (EAN) methods in calculating normal cost and actuarial liability for pension funds. By comparing the two methods, the research aims to provide insights into their implications for pension fund management. The PUC method, which takes into account salary growth over time, typically results in increasing normal cost and smaller actuarial liability as the participant's service period lengthens. Conversely, the EAN method spreads the pension cost evenly over an employee's working years, leading to stable normal cost and higher actuarial liability, particularly in the mid-period of membership. The study utilizes data from PT. XYZ, applying both methods separately for male and female participants due to differences in life expectancy. The results offer a comparative analysis that highlights the financial implications of each method for both participants and pension fund companies, contributing to more effective pension fund management strategies.
Premium Reserves Calculation on Whole Life Insurance Using The Fackler Method Jabbarudin, Akbar; Sudding, Fauziah Nur Fahirah
Journal of Actuarial, Finance, and Risk Management Vol 4, No 1 (2025)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v4i1.6247

Abstract

Everyone has a risk of death and as they get older, the risk of death will increase. Therefore, everyone suggested to have insurance. Not only for individual, the risk is also faced with insurance provider. There are several categories of life insurance. One of the category is whole life insurance. Whole life insurance has benefitfor lifetime of the insured. The insurance provider will pay beneficiaries when the policyholder dies within any years. There are two major methods of calculating premium reserve which are prospective and retrospective method. The Fackler method adapting the concept of retrospective method. The assumptions of the Fackler method that final reserve value is determined as the reserve at the end of the next year. Considering the long-term impact, this study conducted premium reserves calculation on whole life insurance using the Fackler method. This study use “Tabel Mortalitas Penduduk Indonesia 2023” from BPJS Kesehatan as Mortality Data and 5.75% as interest rate from BI-Rate. The result of this study shows that the amount of premium reserves reaches the promised benefit at the age of 67 years old for male and 70 years old for female. Life expectancy in Indonesia is 73 years old for male and 78 years old for female. Based on the result, the Fackler method success reaches the promised benefit below life expectancy Indonesia.
Application of ARIMA Modeling to forecast the WeeklyStock Price per Share of (Persero) PT Telekomunikasi Indonesia Tbk Mihardja, Jocelyn Beatrice
Journal of Actuarial, Finance, and Risk Management Vol 4, No 1 (2025)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v4i1.6246

Abstract

This research is aimed to forecast the weekly stock price of PT Telkom using the ARIMA modeling approach. The dataset used in this study spanned from May 02, 2022 to January 30, 2023. The ARIMA(3,2,1) model was found to be the best fit for the data. The model's formula was Yt=-0.7692 Yt-1-7409 Yt-2-0.5175 Yt-3+et-0.2444 et-1, where Yt represents the stock price at time t and et represents the residual error at time t. This model was used to predict the stock price for the next 8 weeks. The accuracy of the chosen model is 85005.53, 0.0899, 353.67, and 8.99% respectively to MSE, RMSE, MAE, and MAPE. The forecasted results showed a gradual upward trend in the stock price with some fluctuations, indicating a positive outlook for PT Telkom in the coming weeks.
Pricing Asian Options on BBCA Stocks: A Binomial and Black-Scholes Approach Antoro, Srava Chrisdes; Irsan, Maria Yus Trinity
Journal of Actuarial, Finance, and Risk Management Vol 4, No 1 (2025)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v4i1.6241

Abstract

This paper aims to evaluate the pricing of Asian options using two widely recognized methods: the Binomial Option Pricing Model and the Black-Scholes Model. Asian options are a form of exotic options where the payoff depends on the average price of the underlying asset over a specified period, reducing the impact of market volatility compared to standard European or American options. The research focuses on BBCA (Bank Central Asia) stock data over a two-month period from September to November 2024. The study uses the arithmetic average for the binomial model and the geometric average for the Black-Scholes model. Essential financial parameters such as risk-free interest rate, volatility, and strike prices are determined based on real market data and standard assumptions. The binomial model offers a numerical approach through discrete time intervals, while the Black-Scholes model provides a closed-form analytical solution. Results show that the call option prices from the binomial and Black-Scholes models are 1,228.79 and 1,272.02 respectively, with a Mean Absolute Percentage Error (MAPE) of 3.52%. For the put options, the binomial and Black-Scholes prices are 1,754.46 and 1,711.21, respectively, with a MAPE of 2.46%. These low error rates suggest that both models can accurately estimate Asian option values. The study concludes that both the binomial and Black-Scholes models are effective tools for pricing Asian options on BBCA stock, offering comparable results with minimal deviation. This finding supports the use of these models in financial decision-making for exotic options in the Indonesian market
Forecasting UNTR Weekly Stock Price using ARFIMA Singgih, Gabriella Maria; Hamzah, Dadang Amir
Journal of Actuarial, Finance, and Risk Management Vol 4, No 1 (2025)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v4i1.6284

Abstract

Predicting stock prices plays a pivotal role in the decision-making processes of organizations and individual investors. This research focuses on the predicting weekly closing stock prices, specifically for UNTR, using the ARFIMA method. The ARFIMA method shows promise in handling long-memory data, but its effectiveness in predicting UNTR's stock prices requires thorough examination to ensure its applicability and reliability. The aim of this study is to predict the weekly closing prices of UNTR stocks using the ARFIMA method. The training data used spans from January 1, 2020, to December 31, 2022, with the objective of predicting the period from January 1, 2023, to February 28, 2023. The result shows that the ARFIMA (10; 0.4993; 3) model was selected due to its optimal performance, having the lowest RMSE and MAPE values, specifically an RMSE of 0.4 and a MAPE of 4.16%. This model successfully captures the long-term memory patterns in the data, generating accurate predictions for the projected period. 

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