Using Probabilistic Models to Appraise and Decide. Ley-Borrás and Fox assets at risk is taken. Once this process is undertaken for all of the events in the stochastic event catalog, the result is a table of event‐by‐event losses known as an Event Loss Table (ELT).

The ELT contains, for every event that causes damage, the annual rate of occurrence, total mean loss and related uncertainty parameters. By combining the frequency and severity of the losses in the entire stochastic event catalog, probabilistic catastrophe models are able to calculate the distribution of losses related to all of the possible events. Two common outputs of catastrophe models are Average Annual Loss (AAL) and Probable Maximum Losses (PML). The AAL is simply the sum product of all of the event annual frequencies and expected losses, and represents the expected annual economic loss from the modeled portfolio of assets exposed to the natural perils represented by the hazard module.

PMLs represent the annual chance that losses from the portfolio of assets being analyzed could exceed a given threshold. This metric can also be referred to in terms of return periods – for instance if there is an annual likelihood of 0.5% that a portfolio of risks could exceed US $100 million loss, this is the same as saying that, on average, a loss of US $100 million or greater is expected to occur once every 200 years. PMLs are derived from the production of Exceedence Probability (EP) curves by catastrophe models.

Probabilistic catastrophe model output can be of use to decision makers because it provides insight into the sizes of potential economic impacts across a range of timeframes. AALs and short return period PMLs (e.g., 5 year return period) can provide information on the likely impacts of relatively small, frequent natural events; while long return period PMLs (e.g., 200 years) can inform as to the likely economic consequences of low‐frequency, high severity catastrophes. Such metrics have been used for the quantitative evaluation of potential disaster risk reduction (DRR) strategies (e.g., Michel‐Kerjan et al., 2012), but their application in aiding the design and evaluation of SDRFI strategies has been somewhat limited.

While probabilistic catastrophe models are routinely used to represent the likely economic impacts of natural catastrophes, apart from the considerations related to the accuracy to which a computer model can represent physical work interactions, there are many potential limitations in their use. While it is commonplace to focus on what the models _can_ do, their use in developing and evaluating SDRFI strategies should also include a robust understanding of what they _cannot_ do. For instance, not all of the perils that could impact a country may be modeled, there may be secondary perils (e.g., tsunamis following earthquakes) that may not be captured in a particular model, or there may be specific elements (e.g., energy assets) not adequately represented.

**Government use of probabilistic catastrophe models to inform sovereign disaster risk financing decision making**

Probabilistic catastrophe models, as used by the international (re)insurance community, rely on outputs which represent _direct losses_ resulting from the impact of natural perils. For governments, _indirect losses_ must also be considered, given their impacts at a macro‐level.

Direct losses are derived from the actual physical damage to building structures, their contents and the subsequent time‐dependent interruptions (e.g., commercial business interruption). Indirect economic impacts typically include job losses, economic slowdown, decreases in tourism, and reduction in public

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