The IIASA Modeling Tool Natural disaster risk management The CatSim Model Stefan Hochrainer Department of Statistics and Decision Support Systems (University of Vienna) Risk, Modeling and Society Group (IIASA)

Introduction: Ex-ante measures: Measures undertaken before the disaster happens: -Mitigation -Insurance -Reserve Fund -Contingent Credit Ex- post measures: Measures undertaken after the disaster happens –Diversion –Assistance –Domestic Credit –MFI loans, Int. borrowing

What does the CatSim Model? It assesses the costs and risks of financial vulnerability and analysis selected ex-ante financial instruments measures for reducing vulnerability. What is unique/new about the CatSim Model ? First integrated modeling approach to assess financial risk management strategies for natural disaster. Includes ex-ante and ex-post measures from an inter- correlated perspective. User can change interesting parameters and assess the consequences directly. Probability based approach and dynamic modeling of economic effects. The CatSim Model

CatSim Model Disaster risk management for developing countries as a two stage decision problem under uncertainty. First stage: Ex-ante Second stage: Ex-post Integrative view: The scope of possible actions at stage two influences the decision at stage one.

CatSim Model Model uses Monte Carlo Simulation Technique (probability based approach) –Important sampling algorithm to generate the scenarios from a given damage distribution function. –Scenarios are stratified samples instead of uniformly events. Model evaluates the scenarios dynamically in the time horizon. User Interface: Strategies for government financing of disaster risk can be developed and its costs and consequences on important indicators studied.

Model consists of two parts: Module I: Assessment of Financial Vulnerability for the next year for various impacts of a disaster. (Limited information approach) Module II: Assessment of Financial Vulnerability for a given time horizon using ex-ante and ex-post measures (Probability based approach) CatSim Model: Modules

CatSim Model:User Interface

CatSim Model:User Interface: Module I

User-Interface: Module I: Hazard

User-Interface: Module I: Vulnerability

User-Interface: Module I: Elements at risk

CatSim Model:Case Studie Honduras

Conclusions Honduras is highly indepted and highly exposed to natural disaster. Honduras is very dependent for borrowing on loans. Mitigation for the lower year events. Insurance for the higher events.

End of Presentation Thank you Questions?

Decision variables: Expenses for mitigation. XL-Insurance. Contribution to reserve fund. Fee for contingent credit. Response variables: Discounted expected return for the next x (e.g.11) years. Shortfall probability for the next x years Expected reduction of the credit buffer in the next x years. Decision and Response variables

Economic parameters: Return on capital Discount rate for future returns Depreciation rate Factor for mitigation Premium loadings for insurance Interest rate for reserve fund, contingent credit, domestic credit, MFI loan, international bond Fee for contingent credit Maximal Diversion Maximal Domestic Credit Initial capital Initial reserve fund Fixed budget (planned for t=1,..,x years) Credit buffer Catastrophe parameters: Probability of first loss 20-years event loss 50-years event loss 100-years event loss 500-years event loss 1000-years event loss Simulation parameters: Time horizon Number of Scenarios Expenditure length Input Parameters

Mitigation: Bold printed line shows the loss as a function of the “hypothetical” loss without mitigation. Up to a limit given by the invested mitigation no loss occurs. If the “hypothetical” loss is larger than this limit, the full loss occurs. Hence there are two negative effects at once.

Pricing of Insurance Contracts: Insure against certain "layers" of risk, e.g. insuring against events in excess of the 100 year up to the 500 year hurricane event. The XL layer is determined by two points: The attachment point (A) and the exit point (E). The payment depends on the size of the damage (D). So the insurance only pays claims if the damage is larger tan (A) In other words, the insurance pays: 0 if D < A D-A if A < = D <= E E-A if D > E A E claim damage

If the damage distribution function is denoted by F(z), the par-price thencan be calculated by integration: ParPrice =  A(z) d F(z) Pricing of Insurance Contracts: However, the insurance company asks for a risk premium to be added to the ParPrice, this risk premium must be greater (or equal) 1 and monoton increasing. To calculate the new price, a function h(p), 0  p  1, is considered, which has the property stated above, namely: h(p)  1 h(p) is increasing

AdjustedPrice =  A(z) h(F(z)) d F(z) =  A(F-1(p)) h(p) dp =  A(z) h(F(z)) f(z) dz The function g(z) = h(F(z)) f(z) can be seen as a kind of weight function Pricing of Insurance Contracts: