Calculation of Ruin Probability by Insurance Lines and Proposal of an Optimal Portfolio Optimization Method for Insurance Companies
Keywords:
Ruin probability , insurance risk , portfolio optimization , Markowitz theory , value at risk, insurance solvency , financial stabilityAbstract
This study aimed to calculate the ruin probability across different insurance lines and propose an optimal portfolio optimization method for insurance companies to enhance financial stability and minimize risk. A quantitative data analysis approach was employed using historical data from 2014 to 2023 across various insurance lines, including life, liability, freight, fire, and engineering. Statistical distribution models such as exponential and normal distributions were applied to estimate loss distributions. Portfolio optimization was performed using Markowitz’s portfolio theory, considering constraints such as Solvency II regulations and market conditions, with data analyzed using MATLAB and Excel. The results indicated that life insurance exhibited the highest ruin probability at 0.9921, while personal insurance had the lowest at 0.1447. Engineering and freight insurance also showed high ruin probabilities due to large financial exposures. Portfolio optimization significantly reduced the value at risk (VaR-99%), from 37.5% to 26.6% for liability insurance and from 27.8% to 20.3% for freight insurance, demonstrating the effectiveness of strategic capital allocation. Investments in stocks provided higher returns compared to bank deposits and real estate, with the optimal portfolio comprising 34% stocks, 39% deposits, and 27% real estate. Accurate calculation of ruin probabilities and optimal portfolio management are crucial for mitigating financial risks in insurance companies. Diversification and dynamic portfolio adjustments, supported by advanced statistical models and regulatory compliance, enhance financial stability and ensure operational efficiency in volatile markets.
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