Document Type : Original Research Paper

Authors

• Tayyebeh Rajabi ¹
• abdolrasoul mostajeran ²

¹ Ph D Student, Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran.

² Assistant Professor, Department of Financial Mathematics and Actuarial. Faculty of Financial Sciences, Kharazmi University, Tehran, Iran.

Abstract

This research presents an in-depth empirical investigation into the determinants of financial risk within Iran's insurance sector, explicitly examining the interconnected roles of firm-specific financial metrics, corporate governance structures, and macroeconomic fluctuations. As a cornerstone of the financial system, the insurance industry fulfills a critical socio-economic function by pooling and redistributing risk, thereby fostering investment, economic growth, and social welfare. However, its inherent stability is increasingly tested by internal operational complexities and an external environment characterized by volatility. This study posits that a holistic understanding of insurer resilience requires a multidimensional framework that transcends traditional, siloed analyses. Consequently, it innovatively dissects financial risk into two distinct yet interrelated dimensions: internal risk (primarily measured through the solvency ratio, reflecting operational and managerial frailties) and external risk (captured via credit risk indicators, arising from systemic economic forces). The central objective is to quantify the simultaneous impact of three key variable clusters on both risk types, thereby constructing an integrated predictive model tailored to the unique contours of the Iranian market.
The methodological approach is quantitative, descriptive-correlational, and applied in nature. The study population encompasses all 23 insurance firms listed on the Tehran Stock Exchange (TSE). A balanced panel dataset is constructed over an eight-year timeframe from 2016 to 2024, yielding substantial observations for robust analysis. The econometric strategy employs panel data regression techniques. After conducting preliminary tests—including unit root (IPS, LLC), heteroskedasticity (Breusch-Pagan-Godfrey), serial correlation (Breusch-Godfrey), and multicollinearity assessments—the preferred model specification is identified as the Fixed Effects estimator, confirmed via the Hausman test. This method effectively controls for unobserved, time-invariant heterogeneity across companies.
The independent variables are operationalized into three comprehensive groups:
Firm-Specific Financial Factors: These include the Risky Investment Ratio (sum of investments in stocks and real estate divided by total assets), the Loss Ratio (incurred claims to earned premiums, with its standard deviation capturing volatility), and the Herfindahl-Hirschman Index (HHI) measuring product-line concentration.
Macroeconomic Shock Variables: This cluster incorporates annual rates for Inflation, Bank Interest Rates, and Economic Growth (GDP growth), sourced from the Central Bank of Iran.
Corporate Governance Mechanisms: Two proxies are used: Ownership Concentration (percentage of shares held by major shareholders) and Board Instability (the ratio of changes in board membership annually).
The empirical results robustly confirm all six research hypotheses. The regression models exhibit strong explanatory power, with adjusted R-squared values of approximately 0.508 for the internal risk (solvency) model and 0.603 for the external risk (credit risk) model. Key findings reveal nuanced relationships:
Financial Factors: A higher and more volatile loss ratio significantly erodes solvency and increases credit risk. Portfolio concentration (high HHI) consistently elevates both risk types, underscoring the perils of a lack of diversification. The risky investment ratio shows a complex, context-dependent effect, potentially increasing returns but also amplifying market risk exposure.
Corporate Governance: Both high ownership concentration and frequent board turnover are significantly associated with increased financial risk. This suggests that entrenched controlling shareholders and managerial instability can lead to short-termist strategies, weaker oversight, and heightened vulnerability, validating agency theory concerns in this context.
Macroeconomic Shocks: Inflation and high bank interest rates are positively and significantly correlated with elevated internal and external risks. Economic growth exhibits a more complex relationship, sometimes negatively associated with solvency, possibly due to aggressive risk-taking during expansionary phases. This highlights the profound sensitivity of the insurance industry to the turbulent Iranian macroeconomic landscape.
The study concludes that financial risk in Iranian insurance companies is not a monolithic construct but a multifaceted phenomenon shaped by a dynamic triad of financial, governance, and macroeconomic forces. Effective risk management and regulatory supervision must, therefore, adopt an integrated, holistic perspective. The findings offer critical implications: Firstly, they provide empirical justification for regulators (like the Central Insurance of Iran) to develop comprehensive early-warning systems that monitor this triad of indicators in real-time, moving beyond purely financial ratios. Secondly, they emphasize that macroeconomic stability is not merely a background condition but a direct prerequisite for the sector's financial health. Thirdly, they advocate for the strengthening of corporate governance—through policies promoting balanced ownership and board stability—as a vital tool for mitigating risk and protecting policyholders.
This research makes several distinct contributions to the literature. It is among the first in the Iranian context to empirically disentangle and simultaneously model internal versus external financial risks. By integrating governance and macroeconomic variables with traditional financial metrics within a unified panel data framework, it addresses a significant gap in domestic studies. Furthermore, its operationalization aligns with local regulatory frameworks (e.g., Solvency Regulation No. 69), enhancing its practical utility for both supervisors and insurance company managers. The study paves the way for future research employing advanced techniques like machine learning for predictive modeling and suggests extending the framework to include other variables such as reinsurance dependence and InsurTech adoption.

Keywords

• "Financial strength"
• "Corporate governance"
• "Panel data"
• "Financial risk"
• "Iranian insurance industry"

Main Subjects

• Economics of finance / insurance