Catastrophe model

A catastrophe model, or catastrophe risk model, is a complex piece of computer software, typically built by specialist firms (like Verisk, Moody’s, Karen Clark & Company etc.), and used to analyse catastrophe risk exposure.

A catastrophe model is a sophisticated computer-based tool that integrates science, engineering, and statistical data to simulate and approximate the financial impacts of potential disasters – such as earthquakes, hurricanes, floods, and wildfires.

By combining simulations of potential hazards with data on vulnerable assets (like buildings), these models estimate the financial impact of catastrophic events. They are essential tools for the insurance industry and risk managers to:

• Price policies accurately.
• Manage risk exposure.
• Ensure financial solvency in the wake of major disasters.

Catastrophe risk models are widely used in reinsurance and insurance-linked securities (ILS). Catastrophe bond offerings of securities typically come with a third-party catastrophe modellers risk assessment.

How the Process Works

Catastrophe modeling follows a sequential five-step process, moving from the simulation of a natural event to the calculation of financial impact.

1. Event Generation The model creates a massive set of plausible, hypothetical events. This “stochastic event set” covers the full spectrum of possibilities, ranging from frequent, low-impact weather events to rare, severe disasters.
2. Local Intensity Calculation Once an event is simulated, the model calculates the specific intensity of the hazard at different geographic locations.
   • Examples: Calculating wind speed at a specific zip code or ground-shaking intensity at a specific fault line.
3. Exposure and Vulnerability Assessment The model overlays the hazard intensity data onto a database of vulnerable assets (the “exposure”). It analyzes specific details to determine how susceptible an asset is to damage, including:
   • Building materials (e.g., wood vs. concrete).
   • Age and condition of the structure.
   • Structural reinforcements (e.g., hurricane shutters).
4. Loss Estimation By combining the hazard intensity (Step 2) with the asset vulnerability (Step 3), the model estimates the physical damage and translates that into potential financial losses for each specific simulated event.
5. Portfolio Analysis Finally, by aggregating simulations representing tens of thousands of years, the model provides a probabilistic view of risk. This allows an insurer or reinsurer to understand the potential losses across their entire portfolio of policies, rather than just a single location.

There are two main model approaches:

Deterministic models focus on a single, specific “what-if” scenario to assess the impact of a defined event. For example, a risk manager might ask, “What would be the financial loss if a Category 5 hurricane struck downtown Miami directly?” This approach creates a specific footprint of the event to test a portfolio’s resilience against a known historical disaster or a theoretical worst-case scenario. It is useful for stress testing and disaster response planning but does not tell you how likely that event is to happen.

In contrast, probabilistic models run thousands of simulations to generate a statistical view of risk over time. Instead of looking at just one event, this method considers the frequency and severity of all possible events—from minor storms to 1-in-100-year earthquakes. This allows insurers to estimate the probability of reaching certain financial loss thresholds in any given year.

While deterministic models answer “How bad could it get?”, probabilistic models answer “How likely are we to lose this amount of money?”