What is CCRIF’s hazard and loss modelling framework?
CCRIF’s second-generation hazard and loss modelling framework has been developed to assist CCRIF in developing new policy formulations and in developing regional technical capacity in catastrophe risk modelling. It will enable a new approach to policy formulation - one of modelled loss instead of index parametric, the latter being the current basis for policies. This means that the new policy will be able to reduce the basis risk in the parametric loss estimates by modelling each loss as it happens, rather than reducing the loss estimation methodology to a series of equations. Furthermore, the new model will use the best definition available of the entire wind, storm surge and wave field for hurricane policies and earthquake shaking field for earthquake policies to drive its loss model. Instead of being estimated only at distinct measuring points, the new model estimates the level of hazard and consequent loss for every 1km grid square of a country’s territory. The losses are then added up across the country to find the total country-wide loss.
Given the operational needs of CCRIF, its hazard and loss modelling framework must meet the Facility’s objectives accurately and in a manner that provides the Facility with both reasonable calculation times and maximum flexibility in designing and costing contract options. The CCRIF Facility Loss Model (FLM) is a stand-alone tool designed to enable the Facility to:
- estimate loss probabilities for individual territories and a portfolio of territories with specific contract terms;
- price contracts for specific territories; and
- estimate site-specific hazard levels and losses for specific events — either historical or active events during the contract period.
Main strengths of the FLM are that it:
- is built upon a strong, validated hazard modelling base;
- uses the same techniques and code for both historical hazard assessment and loss modelling as well as real-time storm modelling and payout calculation;
- is implemented using open modelling techniques from the published scientific literature;
- is highly scalable and can be applied at a wide range of modelling resolutions; and
- is implemented on a geographic base, enabling straightforward generation of results in mapping formats.