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International Journal of Quantitative Research and Modeling
Published by Research Collaboration Community
ISSN : 27225046     EISSN : 2721477X     DOI : https://doi.org/10.46336/ijqrm
Core Subject : Science, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics
International Journal of Quantitative Research and Modeling (IJQRM) is published 4 times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJQRM to present papers which cover the theory, practice, history or methodology of Quatitative Research (QR) and Mathematical Moodeling (MM). However, since Quatitative Research (QR) and Mathematical Moodeling (MM) are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Quatitative Research (QR) and Mathematical Modeling (MM) to real problems are especially welcome. In real applications of Quatitative Research (QR) and Mathematical Moodeling (MM): forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community Quatitative Research (QR) and Mathematical Moodeling (MM), education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support Mathematics Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Heuristics Industrial Engineering Information Management Information Technology Inventory Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Marketing Engineering Markov Chains Mathematics Actuarial Sciences Big Data Analysis Operations Research Military and Homeland Security Networks Operations Management Planning and Scheduling Policy Modeling and Public Sector Production Management Queuing Theory Revenue & Risk Management Services Management Simulation Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation Risk Management Modeling of Economics And so on
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 373 Documents
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Net Single Premium Estimation for Credit Life Insurance under Floating Interest Rates Using the Cox-Ingersoll Ross (CIR) Stochastic Model and Amortization Method Azzah Nailah Salsabila; Muhammad Hanif Faridy
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): 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.v7i1.1238

Abstract

Credit life insurance is designed to protect lenders against the risk of loan default arising from the death of borrowers during the loan period. In practice, premium determination for credit life insurance often assumes constant interest rates and does not fully account for demographic risk factors, which may lead to inaccurate pricing. This study aims to estimate the net single premium of credit life insurance by incorporating both borrower-specific mortality characteristics and floating interest rate dynamics under a stochastic framework. The loan interest rate is assumed to follow a floating structure linked to the BI 7-Day Reverse Repo Rate, which is modeled using the Cox–Ingersoll–Ross stochastic interest rate model to capture mean-reverting behavior and ensure non-negative interest rates. Loan repayment is structured through a monthly amortization scheme, resulting in a decreasing insurance benefit equal to the outstanding loan balance at the time of death. Mortality risk is evaluated using the Indonesian Mortality Table IV, with monthly death probabilities derived under the Uniform Distribution of Death assumption to accommodate fractional-age valuation. The actuarial present value of insurance benefits is computed by discounting the outstanding loan balance for each month and weighting it by the corresponding probability of death. The expected value of this random present value yields the net single premium. Numerical illustrations demonstrate that premiums increase with borrower age and are higher for male borrowers than for female borrowers of the same age, reflecting underlying mortality differences. Furthermore, the use of floating interest rates leads to annual adjustments in loan installments, which directly influence the evolution of insured benefits and premium values. Overall, the results indicate that integrating stochastic interest rate modeling with demographic mortality structure produces a more accurate and risk-reflective estimation of credit life insurance premiums, particularly in environments where floating interest rates are applied.
Estimation of Stock Return Volatility Using Bayesian MCMC-Based Stochastic Volatility Model Muhammad Bahrul Ilmi; Hanan Hamuda
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): 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.v7i1.1239

Abstract

Parameter estimation of a distribution can be performed through two main approaches: the classical method and the Bayesian method. The Bayesian method integrates the sample distribution with the prior distribution, where random sampling is conducted via simulation techniques such as Markov Chain Monte Carlo (MCMC) with the Gibbs Sampling algorithm. This algorithm works by constructing a Markov Chain through recursive sampling from the full conditional posterior distribution for each parameter until convergence is reached. This study applies the Bayesian method with MCMC using the Gibbs Sampling algorithm to estimate the parameters of the Stochastic Volatility model, which allows asset price volatility to vary over time. The obtained Stochastic Volatility model is then used to predict the stock returns of PT. Aneka Tambang Tbk. (ANTM.JK), where the prediction results show good conformity with actual data. The resulting prediction values can be utilized by investors as a reference in making optimal investment portfolio decisions.
Development of Kaplan Fixed Pitch Microhydro Turbine as a Renewable Energy Source at the TNI AD Border Post Koko Hadi Santoso; Pradika Noviandani; Suryaman
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): 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.v7i1.1253

Abstract

Micro hydropower plants offer a promising renewable energy solution for remote areas lacking access to electricity, particularly military border posts. This study aims to design and develop a fixed pitch Kaplan turbine for low head micro hydro applications. The turbine operates under a head of 1.8 m and a discharge of 0.006 m³/s. The research methodology includes theoretical calculations, numerical simulations using Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), and experimental prototype testing. Key design parameters include runner diameter, blade configuration, shaft design, and transmission system. Simulation results show stable flow distribution and safe structural stress levels. Experimental results indicate a power output of 74.9 W with an efficiency of 70.7%. These findings demonstrate that the proposed turbine is suitable for low-head applications and can serve as an alternative energy source for remote military installations.

Page 38 of 38 | Total Record : 373

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