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Dwi Susanti
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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Determining Pure Premium of Motor Vehicle Insurance with Generalized Linear Models (GLM) Tyrenia Rahmawati; Dwi Susanti; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i4.492

Abstract

Motor vehicle insurance guarantees protection, coverage, and compensation for the risks of accidents, damages, and loss of motor vehicles. It is crucial for companies to determine appropriate insurance premium rates as a preventive measure to avoid difficulties in meeting claims filed by policyholders. This research aims to determine the pure premium of motor vehicle insurance using the Generalized Linear Models (GLM) method, which utilizes the concept of a general linear relationship between independent variables and the dependent/response variable, as well as identifying motor vehicle characteristics that influence the determination of pure premiums. The data used in this study is from Swedish motor vehicle insurance. The research aims to determine the pure premium in the data by modeling claim frequency using the Poisson distribution and claim severity using the Gamma distribution, depending on the significantly influential characteristics. The Maximum Likelihood Estimation method is employed for parameter estimation. After conducting the research, the estimated parameters , , and the pure premium of motor vehicle insurance are found to be 35,572,223.27 kr, with the characteristics influencing the pure premium being the distance traveled by the vehicle, the insured's geographic zone, and the no-claim bonus.
Pricing of Aquaculture Industry Microinsurance Premiums with Standard Deviation Principle Approach (Case Study: Tasikmalaya) Anang Muhajirin; Dwi Susanti; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i4.542

Abstract

Aquaculture is a rapidly growing industry and has enormous potential to increase the income and welfare of fish farmers. The majority of aquaculture businesses in Indonesia are small-scale cultivators, low productivity and limited business accessibility. As a result, there is an aquaculture industry that does not understand the use of aquaculture-specific financial risk management tools. Therefore, an insurance instrument is needed to manage losses that occur so as to achieve financial and income benefits, namely Micro Insurance. This study aims to calculate premium prices with a standard deviation principle approach. The data used is loss data if aquaculture cultivators do not pay in accordance with the initial capital in Tasikmalaya obtained through primary data based on the results of field surveys through questionnaires. The method of analyzing the number of event data uses the Poisson distribution, while the loss data uses the Exponential distribution. Next, calculate the parameter estimation using the Maximum Likelihood Estimation method. The results of parameter estimation are used to find a collective risk model. From the calculation results in this study, a premium price of IDR  was obtained.
Analysis Volatility Spillover of Stock Index in ASEAN (Case Study: Indonesia, Singapore, Malaysia) Kirana Fara Labitta; Dwi Susanti; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.603

Abstract

Every country has its own income, including ASEAN countries such as Indonesia, Singapore, and Malaysia. One source of national income can come from stocks, which can be measured by the stock index. The income of each country depends on each other and can be influenced by a phenomenon, such as the Covid-19 pandemic. The Covid-19 pandemic can also cause volatility spillover. This research aims to analyze volatility spillover in ASEAN countries (Indonesia, Singapore, and Malaysia) before and during Covid-19 by looking at the effects of asymmetric volatility. Volatility spillover testing in this study uses the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model, starting with creating a time series model and then modeling the residuals from that model, then finding the estimated parameter results of asymmetric volatility effects. The results of this study indicate that during the period before Covid-19, there is volatility spillover for Indonesia and Malaysia. Then, during the Covid-19 period, there is volatility spillover for Indonesia and Malaysia, for Indonesia and Singapore, and for Singapore and Malaysia.
Based Stock Valuation Analysis on Fuzzy Logic for Investment Selection (Case Study: PT. XL Axiata Tbk. and PT. Telkom Indonesia Tbk.) Maudy Afifah Audina; Dwi Susanti; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.673

Abstract

The stock value of a company fluctuates with capital market conditions, requiring investors to consider various factors for precise investment decisions. Stock valuation determines the fair price of a company's stock, guiding buying and selling transactions. This research uses Discounted Cash Flow (DCF), Price to Earnings (P/E), and Enterprise Value to EBITDA (EV/EBITDA) to ascertain fair stock prices, integrating results with Mamdani fuzzy logic to determine investment weights. The result of this research is that both EXCL and TLKM hold significant weight in the investment portfolio with TLKM has slightly higher stock weight than EXCL. This suggests TLKM offers more potential for profitable future investments. Investors can use these results in portfolio management for investment selection
Forecasting Indonesian Stock Index Using ARMA-GARCH Model Dwi Susanti; Kirana Fara Labitta; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.686

Abstract

The stock market is an institution that provides a facility for buying and selling stocks. Covid-19 is an issue that has affected the stock markets of many countries, including Indonesia. Due to the pandemic, the condition of stocks before and during Covid-19 is certainly different. Stocks can be measured using stock indices. To predict future stock conditions, it is necessary to forecast the stock index. This research aims to predict the Indonesian stock index in the before and during Covid-19 period, using ARMA-GARCH time series model. According to the results obtained for before Covid-19 data, the best predictive model is the ARMA(0,2)-GARCH(1,0), and for the data during Covid-19, it is ARMA(3,3)-GARCH(3,3). Since the MAE is close to zero, it indicates that the model is quite accurate. These findings can help investors make better investment decisions in the future.
Comparison of Projected Unit Credit, Entry Age Normal, and Individual Level Premium Methods in Calculation of Normal Retirement on PNS Pension Funds Aulianda Anisa Putri S. R.; Dwi Susanti; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.692

Abstract

Every individual’s desire for a prosperous old age lead to the need for a pension fund program to ensure the welfare of every employee in their old age. The calculation of pension fund in this study was carried out using the Projected Unit Credit, Entry Age Normal and Individual Level Premium methods. This study aimed to determine the value of normal cost and actuarial liability using Projected Unit Credit method, Entry Age Normal method, and Individual Level Premium. Then the best method was determined based on the comparison results of the normal cost value and the actuarial liability value obtained using the three methods. The data used in this study is secondary data from PT Taspen (Persero) KCU Bandung. The results showed that the best method among the three methods studied was the Projected Unit Credit method because it produced the highest total normal cost with the lowest actuarial liability value each year.
Co-Authors Anang Muhajirin Aulianda Anisa Putri S. R. Kirana Fara Labitta Maudy Afifah Audina Riaman Riaman Sukono Sukono Tyrenia Rahmawati