Investment Portfolio Optimization of Insurance Companies with Copulas and Extreme Value Approach

Subject Areas : Journal of Investment Knowledge

arash goodarzi ¹ , reza Tehrani ² , Ali souri ³

1 - Ph.D Student in finance-Insurance,, Department financial management and insurance, faculty of management, university of Tehran , Tehran, Iran and Lecturer in Finance, Department of financial management, faculty of economic and
2 - Professor, Management and Insurance Group, Faculty of Management, Tehran University, Tehran, Iran
3 - Associate Professor at Faculty of Economics, University of Tehran, Tehran, Iran

Received: 2021-08-30 Accepted : 2021-09-29 Published : 2024-03-20

Keywords: Extreme Value Theory, Copula Functions, Underwriting Activity, Conditional Value at Risk, Investment Activity,

Abstract :

This study determines the optimal investment portfolio of insurance companies by considering underwriting activities. investment decisions in insurance companies are affected by underwriting activities. In this paper, the investment optimization problem of insurers is modeled using the copula-based conditional risk value, taking into account the results of insurance activities. Also, since the emphasis is on tails of distribution, the probability distribution of variables in tails is estimated using Pareto distribution and in other parts of the distribution using the Empirical probability distribution. Data collected on monthly basis covers two periods in-sample, between 2006 to 2015 and out-of-sample, between 2016 to 2019.The findings show that the optimum portfolio includes eighty percent of risky assets (stock and real estate) and only twenty percent of risk-free assets (bank deposits) and it is outside the legal constraints set by Central Insurance Therefore, legal constraints prevent insurance companies from the optimal selection of investment portfolio. Also, the comparison of out-of-sample performance with in-sample performance of portfolios shows that portfolios based on copula functions have better and more robust performance than traditional models.

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