New Directions in Catastrophe Risk Models

Transcription

1 New Directions in Catastrophe Risk Models World Bank Brown-Bag Lunch Presentation October 9, 2007 Richard J. Murnane Baseline Management Company, Inc.

2 Overview! Background on reinsurance and cat models! Open-source risk models! Climate change and risk models

3 Top 40 Property Cat Losses Losses total $294 billion (2006 dollars) Swiss Re Sigma, 2/2007

4 2006 Non-life Premium Volume The US and Europe account for ~80% of premium Swiss Re, Sigma 4/2007

5 Top 30 For Victims ( )

6 Top 30 For Victims ( )

7 How Does A Company Determine:! Their risk of experiencing a catastrophic loss? They use (multiple) risk models

8 Catastrophe Risk Model Hazard Probability Location Magnitude Duration Exposure Location Construction Age Building Code Damage physical damage repair costs Insured Loss terms of coverage

9 How Does A Company Determine:! Their risk of experiencing a catastrophic loss? They use multiple risk models! How much should they charge/pay? Market forces

10 Cat Market Price Cycle Price cycle relative to Man-made catastrophes Natural catastrophes Insured Losses, billions 2002 US$ Swiss Re Camares, 2002, Sigma 2/2003

11 Existing Risk Models! AIR, EQE, RMS, etc. Proprietary, non-disclosure agreements with licensee! In-house models Proprietary, won t be seen outside a company! FEMA s HAZUS Public, but source code not available! Florida s Wind Loss Model Public, but source code not available

12 How Good Are The Models?! Significant effort devoted to development of each model; but, do you know: Which model is better? Which models, if any, have skill?! Insurers essentially use multi-model ensemble forecasting to estimate uncertainty! No public verification, but each company has their own opinion on which model is best for a given situation

13 What Do The Models Lack?! Standardization Not easy to incorporate new or alternate data! Flexibility Not easy to change hazard or damage algorithms! Transparency Proprietary aspects limit understanding and uses! Verification No public assessment of model skill

14 Benefits Of An Open-Source Risk Model! Accelerate development of risk models Access to latest scientific/engineering advances! Enhance liquidity of alternative risk products Direct securitization, e.g., Cat Bonds! Rationalize insurance regulation Florida Hurricane Commission California Earthquake Authority! Promote financing of natural catastrophes in developing countries World Bank programs?

15 Ubiquitous Open-Source Software! Linux is the most used Unix-type software! Apache runs over 50% of the world's web servers.! BIND provides the DNS (domain name service) for the entire Internet.! Sendmail the most important and widely used transport software on the Internet.! OpenSSL is the standard for secure communication over the internet.

16 Relevant Open-Source Efforts! The Globus Alliance - R&D effort on developing grid computing tools! OpenSHA - seismic hazards! OpenSees - earthquake engineering! AGORA - earthquake (and other?) hazards! GEMS - Global Seismic Model System! Others???

17 Why Might The World Bank Be Interested In Open-Source Risk Models?! Development of local exposure data sets! Development of risk models in areas of little or no interest to modeling companies! Customized for use in cost-benefit studies! Support efforts to meld risk and climate change models

18 Weather Hazards In Risk Models! Hurricanes! Tornadoes and Hail! European Wind Storms! Wildfire! Floods! Winter Storms (Snow, Ice, Freezing)

19 Climate Change In Risk Models! Hurricanes Hurricane models currently account for short-term views that depart from climatology Few initial efforts to provide hurricane hazard catalogs for climate scenarios! No other hazards considered! Sea level rise neglected Impact of storm surge? I believe this is greatest long-term danger

20 "There are those who believe that, thanks to satellites, computers, and modern communications, a Galveston or Labor Day disaster could never happen again...it is a sad fact that the United States may not have seen its last Galveston. - Kerry Emanuel, Divine Wind, Spring 2005 Hurricane Katrina, 1500 dead in Louisiana and Mississippi, August 2005

21 US Coast And Sea Level Rise 1 meter 2 meter 4 meter 8 meter

22 Bangladesh, 1970: 300,000 Dead

23 Sea Level Rise In Bangladesh

24 Possible Future Sea Level Rise Melting all Greenland s ice would increase sea level by 7 meters Melting the Western Antarctic Ice Sheet would increase sea level by 6 meters.

25 Possible Sea Level Rise?

26 Ocean Processes Due To Hurricanes! Storm surge Dome of water due to pressure drop (barometric tides + wind setup)! Storm waves sec waves generated by wind shear stress!wave setup Wave breaking in coastal area storm surge storm waves wave setup normal water level ILLUSTRATION NOT TO SCALE Cheung, 2003

27 Modeled High Water Marks For Katrina! Modeled Surge Modeled Surge Elevation (ft) y = x R 2 = Observed High Water Mark (ft) (Surge and wave points)! Modeled surge plus waves Modeled Peak Water Elevation (ft) y = x R 2 = Observed High Water Mark (Surge and wave points) Model results courtesy of ARA

28 Open-Source Surge Models! Primary Models ECOM Plus 2/3D tide-surge coupling model (POM with extension for flooding by UHM) WAM ocean spectral wave model (DKZ) WaveWatch 3 ocean spectral wave model (NOAA NWS) SWAN coastal spectral wave model (TU Delft) COULWAVE surf-zone processes and runup model (Cornell)! Auxiliary Components Global and regional topography and bathymetry database TPXO.6 global tide model (OSU) USN global wind database (1997 to now plus 7-day forecast) NWS global wave database (1997 to now plus 7-day forecast) NWS global hurricane database (1851 to 2004) Hwind parametric hurricane wind model (UHM) Cheung, 2004

29 Cheung, 2004 Hurricane Bob 1991 Track, Tide Gauge, and Buoys

30 Hurricane Bob Surge Modeling Ocean, Coastal, and Nearshore Regions Elev. (m) Elev. (m) Elev. (m) Cheung, 2004

31 Cheung, 2004 Hurricane Waves Nearshore Region at Matunuck, RI

32 Why Couple Cat Risk And Climate Models?! Improve policy relevance of climate change scenarios Dollar losses fit decision processes better than description of extreme events! Current risk model estimates based on past event frequency Extreme event frequency will change

33 Discussion