Generators of Seismic Events and Losses: Scenario-based Insurance Optimization

A principal difficulty in insuring catastrophic risks is the insufficiency of disaggregated historical data on the catastrophe losses. The lack of data and complex spatial and dynamic interdependencies between catastrophic events imply the necessity to base the insurance of property against natural hazards on catastrophe modeling. The purpose of this research is to develop a working tool for increasing capacity for insurance networks, which insure property against earthquakes. This includes a generator of earthquake scenarios and losses, which is based on seismic maps and geophysical formulas. The outputs of the generator serve as inputs for a procedure, which finds an optimal structure of a network of insurance companies. A "guaranteed" approach to finding coverage of the insurance company is outlined

Guaranteed Optimization in Insurance of Catastrophic Risks

The proposed approach to the insurance of regionally distributed property against risk catastrophes is based on finding statistically robust coverages of the insurance companies. Such coverages guarantee that all companies survive no matter what scenario of the catastrophe from a given scenarios takes place. We describe a sequential algorithm that computed the minimum of the companies' premiums and finds optimal coverages. A step of the algorithm is interpreted as searching a minimum-premium coverage that eliminates a current aggregate risk. The latter aggregates the risks of all companies with respect to all admissible catastrophe scenarios in a "fair" manner: the higher is the individual risk, the greater is its contribution to the aggregate risk. To justify the convergence of the algorithm we suggest a new global optimization procedure for a class of nonconvex minimization problems

Earthquake Risk Management: A Scenario Generator

This paper presents a software package EDGE, an Earthquake Damage Generator/Estimator for Toscana, Italy. EDGE creates samples of multidimentional distributions of damage using models of geophysical processes, seismic-geophysical data and a catalog of vulnerability of buildings in the region. The main algorithmic elements: seismic maps, geophysical formulas, and stochastic modeling, are described in detail. The work contributes to a joint research program of Dynamic Systems, and Risk, Modeling and Society projects on data-based methodological support for decision making in the insurance industry against risks of natural catastrophes. The designed catalogs of expected damages can be used for actuarial calculations and optimization of the regional insurance portfolio

Generators of Seismic Events and Losses: Scenario-based Insurance Optimization.

A principal difficulty in insuring catastrophic risks is the insufficiency of disaggregated historical data on the catastrophe losses. The lack of data and complex spatial and dynamic interdependencies between catastrophic events imply the necessity to base the insurance of property against natural hazards on catastrophe modeling. The purpose of this research is to develop a working tool for increasing capacity for insurance networks, which insure property against earthquakes. This includes a generator of earthquake scenarios and losses, which is based on seismic maps and geophysical formulas. The outputs of the generator serve as inputs for a procedure, which finds an optimal structure of a network of insurance companies. A "guaranteed" approach to finding coverage of the insurance company is outlined.

Guaranteed Optimization in Insurance of Catastrophic Risks.

The proposed approach to the insurance of regionally distributed property against risk catastrophes is based on finding statistically robust coverages of the insurance companies. Such coverages guarantee that all companies survive no matter what scenario of the catastrophe from a given scenarios takes place. We describe a sequential algorithm that computed the minimum of the companies' premiums and finds optimal coverages. A step of the algorithm is interpreted as searching a minimum-premium coverage that eliminates a current aggregate risk. The latter aggregates the risks of all companies with respect to all admissible catastrophe scenarios in a "fair" manner: the higher is the individual risk, the greater is its contribution to the aggregate risk. To justify the convergence of the algorithm we suggest a new global optimization procedure for a class of nonconvex minimization problems.