Dynamic Financial Analysis (DFA) of General Insurers under Climate Change

TL;DR

This paper introduces a climate-dependent Dynamic Financial Analysis (DFA) framework for general insurers, integrating climate risk into long-horizon asset–liability projections under SSP scenarios. It couples a climate module (CMIP6/ERA5-based) with hazard models (floods, bushfires, cyclones, storms, hail, ECL) and links to assets (inflation, rates, equities) and liabilities (CAT/non-CAT losses, reinsurance), producing a stochastic surplus and risk metrics. Empirical calibration to Australian data demonstrates that the interaction between economic growth and physical risk shapes risk–return profiles, with SSP 7.0 often most detrimental due to weak growth and high CAT, while SSP 8.5 can yield strong returns but greater tail risk and potential deficits. The framework supports macroprudential insight for insurers and regulators, highlighting capital planning, reinsurance dynamics, and scenario-based capital resilience; the authors provide code and data access to enable replication and extension.

Abstract

Climate change is expected to significantly affect the physical, financial, and economic environments over the long term, posing risks to the financial health of general insurers. While general insurers typically use Dynamic Financial Analysis (DFA) for a comprehensive view of financial impacts, traditional DFA as presented in the literature does not consider the impact of climate change. To address this gap, we introduce a climate-dependent DFA approach that integrates climate risk into DFA, providing a holistic assessment of the long-term impact of climate change on the general insurance industry. The proposed framework has three key features. First, it captures the long-term impact of climate change on the assets and liabilities of general insurers by considering both physical and economic dimensions across different climate scenarios within an interconnected structure. Second, it addresses the uncertainty of climate change impacts using stochastic simulations within climate scenario analysis that are useful for actuarial applications. Finally, the framework is tailored to the general insurance sector by addressing its unique characteristics. To demonstrate the practical application of our model, we conduct an extensive empirical study using Australian data to assess the long-term financial impact of climate change on the general insurance market under various climate scenarios. The results show that the interaction between economic growth and physical risk plays a key role in shaping general insurers' risk-return profiles. Limitations of our framework are thoroughly discussed.

Figures (35)

• Figure 2.1: Modelling framework of climate-dependent DFA. The major hazard events depicted in the hazard module are for illustrative purposes only (based on the Australian context). For application in other settings, users may replace these with the predominant hazard types relevant to their selected country.
• Figure 2.2: An illustrative diagram of climate variable simulations
• Figure 2.3: Illustrative diagram showing the simulation flow for equity excess returns
• Figure 3.1: Simulation results of normalised hazard losses. Solid lines represent the average simulation paths under different climate scenarios, while dashed lines denote the $5^{\text{th}}$ and $95^{\text{th}}$ percentiles. Results are derived from simulated climate variables and calibrated hazard models (see Sections \ref{['Section:Hazards']} and \ref{['Section:CalibrationResults']}). The simulations reveal an increasing trend in both the mean and volatility of hazard losses for most hazard types under high-emission scenarios.
• Figure 3.2: Simulated (log) compounded investment returns (a) and projected compounded real GDP growth in Australia (b). Panel (a) shows the simulated compounded returns on the total investment portfolios generated from the economic growth assumptions and the simulated hazard losses underlying each scenario. Panel (b) presents compounded real GDP growth projections derived from the SSP database RiVaKr17.

Theorems & Definitions (13)

• Remark 2.1
• Remark 2.2
• Remark 2.3
• Remark 2.4
• Remark 2.5
• Remark 2.6
• Remark 2.7
• Remark 2.8
• Remark 2.9
• Remark 3.1