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Effect of Insurance Portfolio Dependence on Aggregate Risk Estimates

Abstract

Insurance portfolios often contain multiple risks whose outcomes may be related rather than completely independent. Dependence between insurance risks can influence the frequency and severity of aggregate claims and consequently affect the overall risk profile of an insurer. Accurate recognition of dependence is therefore important for estimating aggregate insurance risk, determining capital requirements, and supporting effective actuarial risk management. This study examines the effect of insurance portfolio dependence on aggregate risk estimates. The study will assess how relationships among individual insurance risks influence the estimation of total portfolio losses. It will focus on how different levels and structures of dependence affect aggregate claims distributions and the resulting measures of insurance risk. The study will consider factors such as claim frequency, claim severity, correlation between risks, dependence structures, portfolio size, loss distributions, exposure concentration, and aggregate claims volatility. Alternative dependence assumptions will be examined to determine how changes in the relationship between individual risks affect expected aggregate losses and other portfolio risk measures. A quantitative research approach will be adopted for the study. Insurance claims and exposure data will be analysed using probability distributions, correlation analysis, dependence modelling, compound loss models, and simulation techniques. Alternative dependence structures will be applied to estimate aggregate losses and compare risk measures under independent and dependent portfolio assumptions. Sensitivity analysis will also be used to evaluate the effect of changes in dependence levels on aggregate risk estimates. The study is expected to reveal that greater dependence among insurance risks may increase aggregate risk estimates, particularly when adverse claims events occur simultaneously across different portfolio segments. Assuming independence where significant dependence exists may therefore result in understated aggregate losses and risk measures. The findings may also indicate that the effect of dependence varies according to the type of insurance risks, portfolio composition, and underlying loss distributions. The study will be useful to actuaries, insurance companies, risk managers, regulators, and financial institutions involved in portfolio risk assessment. It may provide useful information for improving aggregate claims modelling, determining appropriate capital requirements, evaluating portfolio concentration, and strengthening insurance risk management. The findings may also support more realistic assessment of portfolio-wide exposure to correlated losses. The study concludes that insurance portfolio dependence is an important consideration in aggregate risk estimation because relationships among individual risks can significantly influence total loss outcomes. It is therefore recommended that insurers incorporate appropriate dependence structures into aggregate risk models, use reliable claims data to assess relationships between risks, and conduct sensitivity and simulation analyses to improve the reliability of aggregate risk estimates.

Keywords: Insurance portfolio dependence, aggregate risk estimates, aggregate claims, dependence modelling, risk correlation, claim frequency, claim severity, loss distributions, portfolio risk, insurance risk, aggregate loss modelling, risk concentration, actuarial modelling, simulation techniques, capital requirements.