Journal of Actuarial, Finance, and Risk Management
Journal of Actuarial, Finance and Risk Management is a peer-reviewed journal dedicated to interchange for the results of high quality research published by President University. The editor welcomes scholars and statisticians and data science experts who develop their applications in various fields. In addition, final year or postgraduate students are encouraged to submit their original research to this journal.
Premium Reserve Calculation on Last Survivor Whole Life Insurance using Prospective Method
Human life is full of uncertainties that create financial risks, especially the risk of death which increases with age. Therefore, life insurance is needed as a financial protection tool that transfers the risk from individuals to insurance companies. To maintain financial stability, insurance companies are required by the Financial Services Authority Regulation Number 71/POJK.05/2016 to calculate premium reserves accurately. One of the main life insurance products is whole life insurance, which provides lifetime protection and pays benefits to beneficiaries upon the death of the insured at any time. There are two major methods of calculating premium reserves, namely the Prospective and retrospective methods. This study applies the Prospective method, which calculates the reserve as the difference between the present value of future benefits and the present value of future premiums. The purpose of this study is to calculate the premium reserve for whole life insurance involving more than one insured individual. The mortality basis used is the 2019 Indonesian Mortality Table from the Indonesian Life Insurance Association, while the interest rate applied is the BI-Rate of 5.75% as of January 2025. The results show that the premium reserve reached IDR 80,102,511.87 at the 74th policy year and decreased to IDR 79,926,374.14 in the 75th year. This decline occurred because the present value of future benefits decreased as the insured aged. Based on the results, the Prospective method offers a practical approach for estimating reserves that align with the company’s long-term obligations.
Hak Cipta (c) 2026 Nayla Hapsari, Fauziah Nur Fahirah Sudding
Application of ARIMA Model in Forecasting the Stock Price of PT Telkom Indonesia (Pesero) Tbk (TLKM)
PT Telkom Indonesia (Persero) Tbk (TLKM) is a major telecommunication company that is a vital component of Indonesia's digital economy. In addition to offering more products and technologies, TLKM also offers its shares to the public, so how the stock performs is a major indicator of the company's market worth and investor confidence. Forecasting stock prices is therefore crucial, as this allows the company and investors to forecast potential dangers and make wise decisions. In this study, the Autoregressive Integrated Moving Average (ARIMA) method is applied to forecast TLKM's weekly stock prices based on historical data from April 2024 to April 2025, totaling 50 observations obtained via Investing.com. The best fit model, according to the analysis, is ARIMA (4,1,1) with MSE 11506,68, RMSE 107,2692, MAE 63,9526 and MAPE 1,84%. This result helps investors and companies better predict stock price changes, allowing them to make the best investment decisions.
Hak Cipta (c) 2025 Ni Made Mila Mahadewi
Forecasting the Weekly Stock Price of PT. Bank Negara Indonesia Tbk (BBNI) Using ARIMA Model
This study forecast weekly stock price of PT. Bank Negara Indonesia Tbk (BBNI) by applying the ARIMA model using Box-Jenkins method. The data used from March 5, 2023 – May 5, 2024 with 61 data observations, 57 data for training and 4 data for testing. The Box-Jenkins method determined the ARIMA (2,2,0) to be selected after second differencing and passed the residual test (Shapiro and Ljung test). This model resulting the best model with the lowest values of MAPE 1.72%, MSE 8901.2517, RMSE 94.346445, and MAE 83.94. The results show that ARIMA is effective for short-term stock price predictions. This study provides valuable insights for investors while recognizing the limitations in considering external market factors. The future research can also explore the combination of ARIMA with other forecasting techniques.
Hak Cipta (c) 2025 Tania Oktavionabila Kurniawan
A Review of Kruskal-Wallis Test Applications in Scientific Research
The Kruskal-Wallis test is a widely applied non-parametric statistical method for comparing multiple independent groups. Despite its frequent usage in various disciplines, limited bibliometric studies have been conducted to examine research trends, influential authors, and collaborations related to this test. This study aims to analyze the bibliometric landscape of Kruskal-Wallis test research using Scopus as the primary database and Scopus-AI as a secondary source for validation. The dataset consists of journal articles published in English between 2021 and 2025, retrieved using the search query TITLE-ABS-KEY (kruskal AND wallis AND test) with additional filters for document type and language. The bibliometric analysis was conducted using VOSviewer, focusing on co-authorship networks, keyword co-occurrence, citation analysis, bibliographic coupling, and co-citation patterns. The results reveal key research clusters, emerging trends, and highly influential publications. The keyword analysis highlights the interdisciplinary applications of the Kruskal-Wallis test, particularly in biomedical research, machine learning, and social sciences. Citation network analysis identifies high-impact authors and journals, while co-authorship mapping illustrates significant global research collaborations. By integrating Scopus-AI as a second opinion, this study strengthens the validity of findings and uncovers additional insights that may not be evident from conventional bibliometric searches. The results provide a comprehensive overview of research developments related to the Kruskal-Wallis test, offering valuable guidance for future studies and interdisciplinary applications.
Premium Reserves Calculation on Whole Life Insurance Using The Fackler Method
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 benefit for 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 premium reserves reaches maximum amount in year 71 with the amount of IDR 85,911,685.02 for male and IDR 84,599,140.63 for female. After that premium reserves value decrease and reach IDR 0 at maximum age (111).
Hak Cipta (c) 2025 Akbar Jabbarudin, Fauziah Nur Fahirah Sudding
Forecasting UNTR Weekly Stock Price using ARFIMA
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.
Application of ARIMA Modeling to forecast the WeeklyStock Price per Share of (Persero) PT Telekomunikasi Indonesia Tbk
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.
Hak Cipta (c) 2025 Jocelyn Beatrice Mihardja
Application of Projected Unit Credit Method (PUC) and Entry Age Normal (EAN) in Pension Fund Calculation
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.
Hak Cipta (c) 2025 Agus Sofian Eka Hidayat
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 benefit for 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.
Pricing Asian Options on BBCA Stocks: A Binomial and Black-Scholes Approach
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
Hak Cipta (c) 2025 Srava Chrisdes Antoro, Maria Yus Trinity Irsan
Analyzing The Effect of Financial Ratio on Financial Distress Using The Logistic Regression Method in Manufacturing Companies
Financial distress is a company that has difficulty paying its obligations, so it cannot carry out business as usual and may experience bankruptcy. This study aims to analyse the factors that influence the possibility of financial distress by considering financial ratios as indicators to predict the occurrence of financial distress in manufacturing companies in Indonesia. Four independent variables which are financial ratios include Current Ratio, Debt Ratio, Return on Assets (ROA), and Working Capital Turnover. In comparison, the dependent variable is financial distress. This study uses the Altman Z-score model and the data analysis method used is logistic regression analysis. Where logistic regression is one of the statistical analysis methods used to represent the relationship between independent variables and dependent variables containing nominal and ordinal data. The population used in this study includes manufacturing companies listed on the Indonesia Stock Exchange (IDX) in the 2015-2019 period. The sample was determined by the purposing sampling technique. The results showed that not all financial ratios can have a significant effect on the occurrence of Financial Distress. In the results that have been analysed, it is found that Current Ratio does not have a significant positive effect on Financial Distress, Debt Ratio does not have a significant effect on Financial Distress, Return on Assets (ROA) has a significant positive effect on Financial Distress, and the last financial ratio Working Capital Turnover has a significant negative effect on Financial Distress.
Hak Cipta (c) 2025 Windi Marnizal Putri, Maria Yus Trinity Irsan
The Effect of Changes in the Indonesian Mortality Table on Pension Fund Calculations Using the Projected Unit Credit Method
Companies are required to prepare a pension fund for their employees as a form of appreciation and compensation for the services that have been given to the companies. In Indonesia, the calculation of pension funds is regulated in the Financial Services Authority Regulation (POJK) number 3 concerning Pension Fund Investments. Projected Unit Credit uses the distribution of pension benefits that pension participants will get if they work until they reach normal retirement age with a total of years of service. In this research, the researchers are using two mortality tables on the value of pension funds using the projected unit credit method. The data in this research uses salary data from PT. XYZ with the assumption of retirement age at the age of 58, and an interest rate of 6.25% based on Bank Indonesia’s rate. The results of this research show that changes in normal cost and actuarial liability for both males and females are visible at the age of entry into employment until the middle of the working period, while the effect of changes in the mortality table at the final age approaching retirement is not very significant.
The Estimation of relationship between actuarial rate of return, Maturity and Coupon: The case of Tunisia
The aim of this paper is to study the estimation of the relationship between the actuarial rate of return, maturity, and coupon issued by the Tunisian banking and financial institutions, knowing that it is a significant component of financing. Therefore, this relationship issue is an essential link between investments. An important first step is to study yield bonds. For various forms of financial research.To date, the research has primarily focused on the yield bonds for institutions, except for studies conducted by institutions, financial and banking. There are regression models that are tested by different methods. The first model focuses only on the relationship between the actuarial rate of return, maturity, and coupon. The other models are prominently featured in the published literature regarding the yield of bonds. From these models, we discovered that the interest rate has an impact on the yield bonds. These results indicate that the maturity and coupon have a significant impact and exhibit a favorable correlation with the actuarial rate of return. Our estimation of our models proves to provide a high level of explained variation in the yields observed in the Tunisian bonds market
Comparison of Chain Ladder Method and Cape Cod Method in Reinsurance Incurred But Not Reported (IBNR) Reserve Estimation
In the era of globalization, there are a lot of risks that surround individuals and companies, including insurance companies. The necessity of risk management becomes important, and risk transfer through reinsurance is crucial in managing the company’s risk profile. As stated in the Otoritas Jasa Keuangan (OJK) Regulations Number 71 of 2016 Article 19 Paragraph (2), reinsurance companies are obliged to establish technical reserves, in which one of the components is Incurred But Not Reported (IBNR) reserve. Considering there is inconsistency from past studies and the importance of accurately calculating IBNR reserves. Therefore, this study aims to compare the results of IBNR reserves using the Chain Ladder method and the Cape Cod method. This study utilizes the Chain Ladder and Cape Cod models that are being applied in Microsoft Excel to calculate the IBNR reserves. The secondary data used in this study is the Paid & Reported Loss Triangle 2013-2022 from Munich Reinsurance’s Financial Results. The result of this study shows that the value of IBNR claim reserves generated using the Chain Ladder gives better accuracy, with a 12.8% relative error compared to the Cape Cod method’s 26.9%. Based on the assumptions made, the Chain Ladder model is suitable for portfolios that are just starting or have sufficient amounts of data. Meanwhile, the Cape Cod model is ideal for portfolios that have been operating for a long period.
Hak Cipta (c) 2025 Zievan Ananta Pahlevi
Forecasting Weekly Stock Price of PT. Aneka Tambang Tbk (ANTM) Using ARIMA Box-Jenkins Method
Stock price movements in the dynamic economic world require investors and companies to be able to predict future price changes. One method that can be used to predict is Autoregressive Integrated Moving Average (ARIMA). The application of the ARIMA method to forecasting the share price of PT Aneka Tambang Tbk (ANTM) for 4 weeks produces equation Â Â obtained from ARIMA (3,1,0) as the best model.
Premium Calculations with Long Term Care Insurance using Markov Chain Method for Human Immunodeficiency Virus Inpatients
Â Indonesia has a high prevalence of HIV, which is inversely proportional to its insurance product propolis. Life insurance offers economic protection by providing payment upon the death of the policyholder. Long Term Care (LTC) insurance, a type of health insurance, guarantees care and health insurance for the elderly. This study calculates LTC insurance premiums with annuities as rider benefits using a multi-status model that includes health status, HIV, and death. Using Markov Chain modeling and data from Malang city in 2022, we determine the premium based on transition probabilities. This case study involves a 20-year-old man with 5 years of LTC coverage, a benefit value of IDR100,000,000, and an interest rate of 5%. The premium varies by age and gender: at age 20, male pay Rp 2,719,241 and female Rp 2,701,893, at age 21, male pay Rp. 5,762,095 and female pay Rp.5,746,178. At age 22, male pay at Rp. 6,203,680 and females pay Rp.6,189,454 while at age 23, male pay Rp. 6,640,674 and females pay 6,628,146. And male at 24, pay Rp. 7,066,949 while females pay Rp. 7,012,138 and at age 25, male pay Rp 7,068,616 and females Rp 7,029,781. The results of this calculation show that the more age increases, the greater the net premium and also the male net premium is greater than the female net premium
Joint-Life Endowment Insurance Premium Calculation Based on Makeham Mortality Law
This research determined life insurance premiums joint-life endowment insurance, that is the insurance that the benefit is paid if one of the insureds dies or all the insureds survive until the end of the contract. This premium calculation uses one of the famous mortality laws, that is Makeham. Makeham mortality law is an updated law of Gompertz's mortality law where the reason that makes Makeham different from Gompertz is the death calculation factor, because Gompertz only takes the cause of death from the age factor. Even though the death factor can also come from accidents, therefore, Makeham combined the two causes of death so that it is corresponded to the actual factors. The Makeham law will function to create the Makeham assumption mortality table with trials and errors method to construct Indonesia Mortality Table 2019 (TMI IV). This construction requires parameters that must be founded to determine the survival probability ( ) of the Makeham. After finding the , calculate the annual premium by dividing the net single premium by the life annuity. The joint-life endowment insurance premium calculation in this study uses 10-year protection with a 5.75% for interest rate and Rp1,000,000,000 for the benefit, and based on the research, the annual premium for this married couple is Rp78,016,785, where the premium calculated based on Makeham will get a higher premium than calculated based on TMI 2019, which gets an annual premium Rp77,410,954.
Hak Cipta (c) 2024 Inas Aulia Risdiari, Fauziah Nur Fahirah Sudding
DETERMINATION OF MAINTENANCE PRIORITIES ON DISTRICT AND CITY ROADS IN LAMPUNG PROVINCE IN 2019 USING THE SIMPLE ADDITIVE WEIGHTING (SAW) METHOD
Road infrastructure is a primary transportation asset extensively used by society to support economic, educational, and other activities. Consequently, roads function as a crucial supporter of the country's socio-economic activities. UU No. 38 of 2004 regarding Roads stipulates that roads are essential transportation facilities for the economy, social aspects, environment, politics, and defense and security. This research employs the Simple Additive Weighting (SAW) method to collect secondary data. The secondary data is obtained from the Department of Communication, Informatics, and Statistics of Lampung Province and processed to generate an assessment of road segments based on preference values. The research results indicate that, based on the criteria weighting, heavily damaged road conditions have the highest importance weight at 58.6%, followed by the criterion of average daily traffic. These findings suggest that road experts share a similar perspective on how road criteria are prioritized for specific regions. The ranking results using the SAW method with a Likert scale indicate a value of 97.34.
Hak Cipta (c) 2024 Rifky Fauzi, Mei Wulandari Manurung
Modeling bitcoin price by using Euler-Maruyama method
In this study, we use Euler-Maruyama method to simulate Bitcoin prices dynamics. We investigate a year-long movement of Bitcoin prices. Daily closing prices were collected over a period starting from May 27th, 2023 and ending on May 27th, 2024. This data provides a comprehensive picture of how Bitcoin behaved on a daily basis throughout that specific year. The Euler-Maruyama method is used as numerical method for solving stochastic differential equations (SDEs). The method involves discretizing the SDE into an iterative process Â to obtain a simulated price trajectory. The drift term was estimated by the average daily return of Bitcoin prices over the study period. The volatility term was estimated by the standard deviation of daily Bitcoin returns. A Monte Carlo simulation was performed to generate a range of possible price trajectories using the Euler-Maruyama method. The result shows that the Euler-Maruyama method was able to capture the short-term trend of Bitcoin prices effectively. However, the method can also be used to capture the long-term trend of Bitcoin prices.
Hak Cipta (c) 2024 Eliana Wati, Rifky Fauzi, Raymon Dacesta Barus, Nur Aini Balqis Nugroho
Life Table Prediction Using the Lee-Carter Model
Mortality is the state where all signs of life permanently, which can occur at any time after a person is born. Mortality data can be presented in the form of table called a mortality table, which is a table that provides an overview of the life of a population group starting from births at the same time and life of a population group that starts from birth at the same time and slowly decreases due to death. In actuarial studies, this table has a role as one of the important factors to determine the amount of premium costs and premium reserves, especially life insurance. Therefore, it is necessary for continuity in conducting research related to mortality tables. This research aims to forecast the death rate and the probability of death of the population in the future using the Lee-Carter model, which is a mortality forecasting model that combines a demographic model with a time series model. This mortality model shows that the logarithm of mortality rate is the sum of the parameters of the average general mortality rate by age and the multiplication of the trend parameter of mortality rate changes by age with the mortality index parameter. Forecasting process begins with estimating the parameters of the average mortality rate and the trend of mortality rate changes influenced by the mortality index parameter using the singular value decomposition (SVD) method. After that, the mortality index mortality index is forecasted using the ARIMA model and the results of this forecast are then reinserted into the Lee-Carter model to obtain death rate prediction. Based on the results of the mortality rate prediction, it can then be the prediction of the probability of death for the mortality table. The result of this research is a mortality table that contains predictions of the probability of death of the Indonesian population for both male and female gender using the Lee-Carter model from the year 2022 2026. Based on these results, it is concluded that the value of the probability of death for each year increases with increasing age.
Comparison Between Machine Learning Regression Modelling to Predict Individual Premium Price
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.Â
Hak Cipta (c) 2023 Srava Chrisdes Antoro, Elisabeth Gloria Manurung, essykapna Randalline, Maria Yus Trinity Irsan
Forecasting the Weekly Stock Price of PT. OCBC NISP Tbk. using Auto Regressive Integrated Moving Average
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.
Hak Cipta (c) 2023 Elisabeth Gloria Manurung, Edwin Setiawan Nugraha
Annual Premium Calculation On Single Life Insurance using Gompertz Mortality Assumptions
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.
Value at Risk Calculation of Digital Bank Stocks Portfolio in Indonesia
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.
Hak Cipta (c) 2023 Steffany Indra Gunawan
Application of the Historical Burn Analysis Method in Determining Rainfall Index for Crop Insurance Premium Using Black-Scholes
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.
Hak Cipta (c) 2023 Agus Sofian Eka Hidayat
Estimation of Premium Reserves for Last Survivor Endowment Insurance Using the New Jersey Method
There are a few cases of life insurance firms going bankrupt due to mistakes when estimating premium reserves, causing companies unable to pay compensation to policyholders. This is caused when the number of claims submitted by the insured that must be paid exceeds the number of claims previously estimated. Situations like this can be anticipated if the insurance firm has a properly prepared and calculated reserve value. There are various types of life insurance, one of which is endowment life insurance. The purpose of this study is to calculate the amount of reserves adjusted for last survivor endowment life insurance using the New Jersey method. To compute reserves, first, researchers determine the benefits, then the annuity, and finally the annual premium. In the first year, the premium reserve value under the New Jersey method is zero. The New Jersey method begins premium reserves in the second year, for t years, with Â where Â reflecting the duration of the insurer's contract. Based on the data analysis performed, the value of the New Jersey reserves for the two insureds is determined by the length of the insurance contract and the age of the insured at the time of insurance. The value of reserves is seen based on the initial age of a person when starting insurance, the older the initial age of the insurance participant, the greater the amount of reserves obtained by the company. This research is expected can be a reference and help insurance field to estimate premium reserves.
Hak Cipta (c) 2023 Bella Dwi Ananda, Fauziah Nur Fahirah Sudding
Analysis of Premium Reserve Using Zillmer Method and Canadian Method for Endowment Joint Life Insurance
Several life insurance companies are unable to compensate policyholder prompting financial losses, the situation can be foreseen if the insurance company has a properly established and calculated reserve value. Endowment life insurance is one types of life insurance. Life insurance provides protection for one person (single life) or two or more people (multiple life). According to the insured death status, there are two terminologies used in multiple life insurance: joint life and last survivor. The Zillmer Method and Canadian Method used in this study for 3 age cases for a couple of husband and wife whereas a husband is older than wife, a husband has the same age as wife, and a husband is younger than wife to determine the amount of reserves adjusted for endowment joint life insurance. Researchers first determine the benefits, then calculate the annuity, and finally calculate the annual premium in order to compute reserves. The Zillmer Method premium reserve value is minus in the beginning year to cover cost for the company, meanwhile Canadian Method is not. According to the result of this study, the case that the age of wife is same as the husband have lesser reserve than any cases which represent this is beneficial for the company to cover several costs for the policy in the beginning of period. Based on data analysis, the period of the insurance contract and the age of the insured define the reserve value. The older the insurance participant, the lesser the value of reserve.
Hak Cipta (c) 2023 Yuhza Al Ghifari, Fauziah Nur Fahirah Sudding
Forecasting PT Bank Central Asia Tbk Stock Price Using ARIMA Model
Stocks are one of the most popular financial market instruments. Issuing shares is one of the company's options when deciding to fund the company. On the other hand, stocks are an investment instrument that many investors choose because stocks are able to provide an attractive level of profit. Autoregressive Integrated Moving Average (ARIMA) model is a method used to predict the stock price of PT Bank Central Asia Tbk. This analysis shows that ARIMA (3,2,0) is the best model for forecasting the stock price of PT. Bank Central Asia Tbk because it has the smallest MAPE among the other model which is 14.03%. This forecasting is very useful for the investor as a guideline in the future for making effective and efficient decisions about stocks on PT Bank Central Asia Tbk.
ARIMA Model and Holt Winters Seasonal Smoothing Accuracy for Stock Price Prediction
One way to generate capital is by investments in the capital market, with the expectation that this money will increase in tandem with an issuer's stock price. In the past few decades, technology for communication and information has developed incredibly quickly, followed by the introduction of 5G, which boosts data transfer rates. This study intends to compare the accuracy of SARIMA and HOLTS WINTER SEASONAL SMOOTHING in predicting the price of TLKM. The SARIMA (1, 1, 1) x (0, 1, 1) 52 model outperforms Holt's Winter Seasonal Smoothing with a 3% MAPE in terms of forecasting TLKM stock price data
Forecasting the Monthly Stock Price per Share of Taiwan Semiconductor Manufacturing Company Limited (TSM) using ARIMA Box-Jenkins Method
Taiwan Semiconductor Manufacturing Company Limited is a Taiwanese multinational semiconductor contract manufacturing and design company. People can buy Stocks from Taiwan Semiconductor Manufacturing Company Limited. Stocks are one of the attractive investment instruments for companies and individuals. There are some theories and analyses to predict stock prices to help investors make wiser decisions when buying and selling stock portfolios. In this study, researchers will use ARIMA(p,d,q) technical analysis to predict the stock price of Taiwan Semiconductor Manufacturing Company Limited for the next 5 months from January 1, 2005 to May 1, 2005. For this forecasting, researchers used Taiwan Semiconductor Manufacturing Company Limited historical stock price data from January 1, 1998 to December 31, 2004 that was obtained from the Yahoo Finance website. Based on the test results of 8 ARIMA models, the best model that researchers got is model 2 ARIMA (2,1,1) with the equation Yt = 0.0759Yt-1 + 0.2706Yt-2 + et - 0.198et-1. This model is considered to be the best because it has the smallest MSE Value, which is 0.1076018; the smallest RMSE value, which is 0.0301156; the smallest MAE value, which is 0,2495926; and the smallest MAPE value, which is 3.0116%. This study shows that the stock price is predicted to rise for the next 5 months from January 1, 2005 to May 1, 2005.
Hak Cipta (c) 2023 Gabriella Maria Singgih