Stochastic Modeling of Markov Chain in Insurance Companies
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2025
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| _version_ | 1866902197671821312 |
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| author | Md. Shohel Rana |
| author_facet | Md. Shohel Rana |
| contents | Stochastic choice modeling captures the decision- making behavior of individuals or market segments under uncertainty. By integrating probabilistic elements, discrete choice models estimate the likelihood of selecting a particular alternative based on multiple attributes. This study applies discrete stochastic models to identify the factors influencing individuals selection of insurance companies. A Markov chain model is employed to analyze the dynamic operations of insurance companies, focusing on earning patterns and subscriber enrollment trends. As insurance firms play a critical yet often overlooked role in developing economies, understanding their long-term performance is essential for financial stability and growth. In high-risk investment environments, especially those involving public savings, accurate forecasting of company profitability and client participation becomes vital. The study demonstrates that stochastic modeling provides a robust framework for evaluating and predicting insurance company performance. Long-run probabilities are estimated for various models, and comparative analysis identifies the most suitable model for the data. The findings contribute to both the economic understanding of insurance operations and the methodological advancement of stochastic modeling in Bangladesh. Keywords- Markov Chain, Monte Carlo Simulation, MCMC Simulation, Metropolis-Hasting (M-H) Algorithm, Transition probability matrix (TPM), Limiting Probabilities (LP) and Goodness-of-fit. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18074812 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Stochastic Modeling of Markov Chain in Insurance Companies Md. Shohel Rana Stochastic choice modeling captures the decision- making behavior of individuals or market segments under uncertainty. By integrating probabilistic elements, discrete choice models estimate the likelihood of selecting a particular alternative based on multiple attributes. This study applies discrete stochastic models to identify the factors influencing individuals selection of insurance companies. A Markov chain model is employed to analyze the dynamic operations of insurance companies, focusing on earning patterns and subscriber enrollment trends. As insurance firms play a critical yet often overlooked role in developing economies, understanding their long-term performance is essential for financial stability and growth. In high-risk investment environments, especially those involving public savings, accurate forecasting of company profitability and client participation becomes vital. The study demonstrates that stochastic modeling provides a robust framework for evaluating and predicting insurance company performance. Long-run probabilities are estimated for various models, and comparative analysis identifies the most suitable model for the data. The findings contribute to both the economic understanding of insurance operations and the methodological advancement of stochastic modeling in Bangladesh. Keywords- Markov Chain, Monte Carlo Simulation, MCMC Simulation, Metropolis-Hasting (M-H) Algorithm, Transition probability matrix (TPM), Limiting Probabilities (LP) and Goodness-of-fit. |
| title | Stochastic Modeling of Markov Chain in Insurance Companies |
| url | https://doi.org/10.5281/zenodo.18074812 |