International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

A Quantile Regression-Based Scenario Writing Framework for Operational Risk Modeling in the Insurance Industry

Articles in Press

Document Type : Review paper

Authors
Somaieh Babaei 1
Seyyed Mohammad Reza Miri Lavasani 2
Fraydoon Rahnamay Roodposhti 3
Afsaneh Zamani Moghadam 3
1 Department of Finance,SR.C.,Islamic Azad University,Tehran,Iran.
2 Department of Finance, SR.C., Islamic Azad University, Tehran, Iran
3 Department of Finance, SR.C., Islamic Azad University, Tehran, Iran.
10.22034/ijfma.2026.79561.2416
Abstract
Operational risk management represents a critical dimension of financial stability in the insurance sector. Because operational loss distributions exhibit pronounced skewness, heavy tails, and extreme non-linearities, conventional conditional-mean estimation models—such as Ordinary Least Squares (OLS)—fail to capture systemic vulnerabilities at tail quantiles. This paper develops an integrated methodological framework combining scenario planning with panel Quantile Regression (QR) to model operational risk across conditional loss distributions. Utilizing an empirical panel dataset of 37 Iranian insurance companies from 2019 to 2023 (185 firm-year observations), this study formulates a mathematical quantile optimization problem governed by asymmetric loss functions. The empirical estimates demonstrate significant heterogeneity across the conditional distribution (τ=0.10,0.25,0.50,0.75,0.90). The dampening effect of risk management quality (RMQ) and information technology (Tech) increases monotonically from the lowest to the highest quantiles, proving that risk mitigation mechanisms are most potent during distress states. The proposed model provides a dynamic stress-testing apparatus for regulatory capital calculation and strategic planning.
Keywords
Operational Risk
Quantile Regression
Scenario Writing
Distributional Heterogeneity
Solvency II
Tail Risk Modeling.
  1. Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50.
  2. Li, J., Yi, S., Feng, J., & Shi, Y. (2019). Modelling the mitigation impact of insurance in Operational Risk management. Procedia Computer Science, 4, 1668–1674.
  3. Papadopoulos, S. P., & Siettos, C. I. (2021). Operational risk capital estimation using extreme value theory and robust regression. Journal of Operational Risk, 16(3), 45–68.
  4. Zhang, T., & Cao, J. (2023). Impact of regulatory policy adjustments on insurance company costs and cost efficiency. Finance Research Letters, 58, 104611.
International Journal of Finance & Managerial Accounting

Articles in Press, Accepted Manuscript
Available Online from 10 November 2026