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All Journal International Journal of Quantitative Research and Modeling
Betty Subartini
Department of Mathematics, Faculty of Mathematics and Sciences, Universitas Padjadjaran, Sumedang, Jawa Barat, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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Journal : International Journal of Quantitative Research and Modeling

 1 
Analysis of Pet Owners' Willingness to Pay for Pet Insurance Premiums in DKI Jakarta Using Logistic Regression Model Andhita Zahira Adib; Riaman Riaman; Betty Subartini
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.v5i2.578

Abstract

Pets provide many benefits to their owners, both physically and mentally. Pet lovers are increasingly aware of the importance of proper health and care for their beloved animals. This has led pet enthusiasts to consider pet insurance. In participating in insurance, there are factors that influence the willingness of pet owners to pay premiums. The objective of this research is to determine the premium for pet insurance and analyze the factors influencing the Willingness To Pay (WTP) of pet owners. This study utilizes choice modeling format by conducting surveys to identify the factors influencing the purchase of pet insurance. Subsequently, binary logistic regression model analysis using the Maximum Likelihood Estimation (MLE) method and the Newton-Raphson Iteration approach is employed to analyze the factors influencing the magnitude of WTP. The research results show that the average willingness to pay for pet insurance premiums is IDR128,574.76 per year. Factors influencing the decision of pet owners include the number of family dependents and awareness of the importance of participating in pet insurance. The likelihood of cat owners being willing to pay pet insurance premiums is 0.8691 or 86.91%.
Optimal Portfolio Using Single Index Model (SIM) For Health Sector Stocks Silvia Wijaya; Betty Subartini; Riaman Riaman
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.v5i1.591

Abstract

Investment is one of the fund management activities with the aim of obtaining future profits. In addition to profits, investors also need to consider the risks that will be faced by diversifying. Diversification is done by forming an optimal portfolio. This research aims to determine the proportion of stocks in the optimal portfolio and calculate the expected return and risk value of the optimal portfolio. The object used to form the optimal portfolio is health sector stock group for the period January 2020 - December 2022. The method used to form the optimal portfolio is Single Index Model (SIM). The results showed that there were 6 combinations of health sector stock in the optimal portfolio, such as IRRA, PRDA, SAME, SILO, MERK, and HEAL stocks of 8.94%, 9.24%, 9.34%, 11.92%, 27.15%, and 33.41% respectively with expected return of 2.68% and a risk value of 1.85%.
Analysis The Effect Of Volatility On Potential Losses Mutual Fund Investments Using The ES-GARCH Method Abram Chandra Aji Pamungkas; Betty Subartini; Dwi Susanti
International Journal of Quantitative Research and Modeling Vol. 5 No. 3 (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.v%vi%i.594

Abstract

Investing in mutual funds has become a popular choice for investor who looking to participate in the capital markets with more diversified risk. However, the success of mutual fund investments depends on investors understanding the potential losses and opportunities that may arise during the investment period. Analyzing the risk of mutual fund investments is fundamental in helping investors comprehend potential losses. Therefore, research is conducted to understand potential losses by estimating asset price volatility and determining the maximum possible losses. The Expected Shortfall (ES) method proves useful in measuring downside risk and extreme loss potential in investments, but it is less effective in addressing nonlinear trends and the complexity of volatility patterns. Hence, a combination of the Expected Shortfall (ES) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methods is employed to measure the risk of mutual fund investments. The research findings indicate that volatility has a positive impact on Value at Risk (VaR), and the potential maximum losses (ES) increase with higher volatility, indicating a greater risk.
IDX30 Stock Portfolio Optimization Using Genetic Algorithm Based on Capital Asset Pricing Model Nayra Pavita Rahmadhisa; Dwi Susanti; Betty Subartini
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025): 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.v6i2.981

Abstract

The stock market plays a vital role in supporting economic growth by serving as a primary channel for companies to raise capital and for investors to gain profits through long-term investments. In practice, one of the biggest challenges for investors is identifying which stocks are worth purchasing and how to allocate their funds optimally. One commonly used approach to evaluate stock feasibility is the Capital Asset Pricing Model (CAPM), which helps identify undervalued and overvalued stocks based on the relationship between systematic risk and expected return. Additionally, it is necessary to determine the optimal investment weight allocation. Therefore, this study combines the CAPM method for stock selection and Genetic Algorithm, a metaheuristic approach capable of finding optimal solutions in complex problems, to determine the optimal portfolio weight composition. The object of this study includes stocks listed in the IDX30 index during the period from February 2021 to November 2023. The results show that five stocks—ADRO, BBCA, BBNI, KLBF, and TLKM—are classified as undervalued according to the CAPM method and are recommended for inclusion in the optimal portfolio. Portfolio optimization using the Genetic Algorithm results in the following stock weight composition: ADRO 26.55%, BBCA 36.20%, BBNI 9.09%, KLBF 12.20%, and TLKM 15.96%, with a Sharpe Ratio of 4.043906. The expected return and risk of the optimal portfolio are 0.00067373 and 0.00012407, respectively.
Co-Authors Abram Chandra Aji Pamungkas Andhita Zahira Adib Dwi Susanti Nayra Pavita Rahmadhisa Riaman Riaman Silvia Wijaya