Dr. Eberhard Faust, Munich Re, December 2006 Technical Note No. 145: The socio-economic benefits of climate services Finance, insurance, reinsurance Formatted: Centered 1. Introduction Modern societies, including the finance, insurance and reinsurance sectors, are exposed to the impacts of weather in many different ways. Extreme weather events tropical cyclones, winter storms, storm surges, severe storms, tornadoes, heavy precipitation, flooding, hail, heatwaves or drought can lead to large losses in the world s economies, and economic and insured losses caused by weather catastrophes have been steadily increasing for some time now (Figure 1). Deleted:. Deleted: E 16 14 12 10 Storm Flood Temperature extremes (e.g. heat waves, cold waves, forest fires), Mass movement (e.g. avalanche, landslide) Number 8 6 4 2 0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 180 160 140 120 Economic losses (2005 values) Insured losses (2005 values) Trend of economic losses Trend of insured losses Mrd. US$ 100 80 60 40 20 0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Figure 1 (top). The annual incidence of great major weather catastrophes. In line with United Nations definitions, catastrophes are considered great if the affected regions ability to help themselves is clearly overstretched and supra-regional or international assistance is required. As a rule, this is the case when there are thousands of, Font color: Red Formatted: Centered Deleted: : Deleted:
fatalities, when hundreds of thousands of people are made homeless, and/or when the overall losses depending on the economic circumstances of the country concerned and/or insured losses reach exceptional orders of magnitude. Figure 1 (bottom). Annual global economic and insured losses from weather catastrophes in the great category. Source: Munich Re, 2006. 1 Insurers and reinsurers have to deal with natural hazards as part of their risk management programs. Essentially, this means that they have to calculate the individual loss occurrence exceedance probability (OEP) curve in short, the loss distribution associated with a particular hazard for a given regional distribution of insured values (portfolio). To convert this curve into a relationship that assigns return periods to loss amounts is then a straightforward process, assuming a certain distribution of annual loss frequency. The loss distribution can be used to calculate the average expected annual claims expenditure incurred by an insurer due to a natural hazard. This calculation, in turn, determines the amount of the technical risk premium (prior to apportionment of administration costs and requisite risk capital). Loss distributions also indicate the limit below which an insurer settles claims directly from its own resources and above which it requires further insurance cover. In primary insurance and reinsurance risk management, the relevant loss distributions are obtained by means of geoscientifically-based loss models which also incorporate climate data. 1 Another type of risk management tool uses the zoning approach, where zones correspond to different levels of hazard. These zones are then taken as the starting point for different insurance products. Finally, information and prevention measures undertaken by insurers are also facets of risk management that make use of climate information. The capital markets segment of the financial services sector, for example, uses climate data to assess risk for securitising natural hazards, for weather derivatives and for weather hedging products. Deleted: 5 For the World Map of Natural Hazards, see Munich Re (1998), World Map of Natural Hazards. Munich Re (2004), CD-ROM World Map of Natural Hazards. Formatted: Indent: Left: 283.2 pt, First line: 35.4 pt, Don't adjust space between Latin and Asian text Deleted: Figure 1 (bottom): Annual global economic and insured losses from weather catastrophes that fall into the great category. Source: Munich Re, 2006. 2 Deleted: me Deleted: It is then a straightforward process t Deleted: Deleted: averag Deleted: Deleted: 3 Deleted:. 2. Primary insurance and reinsurance risk management services Insurance and reinsurance risk management make use of model-based estimated loss distributions (OEP curves, see above), which are return periods for given loss amounts from specified weather hazards. These models are based on long-term regional time series relating, for example, to storms and associated parameters such as maximum gusts or maximum ten-minute average wind speed measurements at weather stations over predefined regions. Other relevant time series are concerned, for instance, with flood events and associated maximum water levels, run-offs at specified gauges. These long-term time series record extreme events and their spatial parameter fields (footprints) and are the starting point for loss models that also include a spatial distribution of sums insured and functionalities. Loss models then infer the loss per spatial unit from the sum insured and relevant hazard parameters. Basically, these loss models project regional hazard climatology, for example, windstorms, onto the relevant loss distribution. Deleted: i.e. Deleted:, etc Deleted: that Deleted: The l 1 Another database which essentially displays analogous progressive trends is provided by Swiss Re s Sigma (www.swissre.com). For more information on Munich Re s NatCat SERVICE natural catastrophe database, see www.munichre.com.
Sophisticated loss models represent hazard climatology as a probabilistic event set inferred from the observed data. Long-term hazard information and exposure information are combined to establish the risk information per portfolio (that is the loss distribution that constitutes the annual loss expectancy and the loss amounts per return period). These models are used by reinsurers, primary insurers and brokers and have been developed by both the insurance sector and modelling firms. Whereas it was formerly assumed that the distribution of extreme events was stationary, natural climate variability and the impact of human-induced climate change have made it increasingly necessary to adopt a non-stationary approach and take into account changes in the frequency and intensity of extreme events over time. Besides loss modelling, another risk management option is hazard zoning, which also relies on time series of meteorological and hydrological data (that is climate information). The process involves allocating geographical zones to hazard classes that reflect the varying levels of specific natural hazards such as river floods. The technique is used, for example, to demarcate flood zones in Germany (ZÜRS) and Austria (HORA) 3. Additional zoning systems are currently being developed for other weather hazards. These are based on statistical and hydraulic analyses of water level time series and depict flood areas on the basis of return period intervals. These zones are used as the spatial basis for deriving flood insurance rates. In addition, world-wide maps on hazard intensity zones are made available by the reinsurance industry. These are compiled by processing substantial amounts of climate information. These maps can be obtained in hard-copy or CD-ROM format 5 and can also be accessed through the Internet 4. Risk management is also focusing increasingly on providing policyholders with information relevant to protection against natural hazards. In Mexico, for example, climate data are used to provide farmers with a forecast of the expected features of the forthcoming season s weather 5. Insurers are also becoming increasingly active with regard to short-term warnings relating to various loss-producing weather phenomena 6 in an effort to improve information quality and for loss prevention. Cancellation-of-events coverage is another example of how climate information is used in traditional primary insurance and reinsurance business. For example, major outdoor events, such as the summer Olympics, the FIFA World Cup, skiing World Cup races, air shows, open-air concerts, film productions. all rely on weather conditions. Again, time series are used to determine the probability that an event will have to be cancelled due to adverse weather conditions. The determining factors may be good visibility in the case of air shows or cloudless, high-pressure conditions for film sets rather than extreme manifestations of certain weather parameters. 3 For the World Map of Natural Hazards, see Munich Re (1998), World Map of Natural Hazards. Munich Re (2004), CD-ROM World Map of Natural Hazards. 4 For Munich Re s Natural Hazard Maps available as part of the NATHAN internet-based services, see http://mrnathan.munichre.com. For Swiss Re s Natural Hazard Atlas CatNet, see www.swissre.com. 5 For example, in the wider insurance context, AgroClima s Mexican web-based consultation platform www.agroclima.com.mx). 6 For instance, the web-based meteorogical service of Austrian hail insurance company Österreichische Hagelversicherung (www.hagel.at), the WIND (Weather Information on Demand) service provided by the public insurance sector in Germany (www.isst.fraunhofer.de/deutsch/inhalt/projektarchiv/2005/wind/index.html) or WetterAlarm, (www.wetteralarm.ch), a service provided by the Swiss insurance industry. has been Deleted:, Deleted: i.e. Deleted: As well as Deleted:, that is to say Deleted: ) Deleted: 4. Deleted: in Deleted: i Deleted: 6. Formatted: Superscript Deleted: 7 Deleted: 8 Deleted: with a view Deleted: t Deleted: ing Deleted: to Deleted: Fifa Deleted:,
In all of the examples cited above, account is increasingly being taken of the fact that not just natural climate variations (such as the Atlantic Multi-decadal Oscillation or the El Niño Southern Oscillation) but also human-induced climate change are causing systematic changes in average values and, often disproportionately, extreme manifestations of the respective parameters. In addition, this variability frequently increases with time. In this context, the insurance industry is carrying out research based on climate models to assess the future impact of extreme weather events caused by human-induced climate change and the resulting loss distributions 7. 3. Insurance and reinsurance finance services Heavy natural catastrophe losses at the beginning of the 1990s and the subsequent capacity shortage in the reinsurance market gave birth to the idea of securitization transferring underwriting risk to the capital markets via insurance-linked securities. The risk is then carried by capital market investors rather than by reinsurers. As a rule, the insurance company requiring the coverage sets up a special purpose vehicle (SPV) which acts as its reinsurer in exchange for a premium payment. Then, using the premium from the resulting treaty and capital subscribed by investors attracted to natural catastrophe instruments on risk diversification grounds, the SPV invests in securities which earn a return on the capital market; however, if losses resulting from a weather-related catastrophe exceed a predetermined threshold, the ceding company uses the money or a portion of it to settle losses. Examples of trigger securitizations are indemnity bonds, which apply to regional (market) losses, and parametric bonds, which are triggered when meteorological readings exceed a predefined threshold value determined by means of an index. The risk associated with parametric bonds is assessed by means of event time series and their descriptive parameters (that is using climatological information from the relevant regions). This information is used to calculate the exceedance probabilities and return periods of the various trigger values. The underlying parameter might, for example be wind speed readings for extratropical storms taken at predefined weather stations. These are then used to generate a specific regional index for each windstorm event. Banks and insurance companies offer facilities for issuing and trading in weather derivatives. These are capital market products designed to offset the risk of a company s production volume and sales being hit by adverse weather conditions, such as when cool, wet summers impact soft drinks sales and attendance at open-air events and leisure parks. Derivatives can also be issued to offset lost wind farm production during periods of prolonged calm. The principle behind weather derivatives is that payment is triggered if readings exceed or fall below a given reference value. The reference value might be a critical amount made up of aggregated daily temperature readings over a defined period, such as the heating degree day (HDD) or cooling degree day (CDD) indices. Alternatively, the critical day may be defined in terms of other parameters such as precipitation, wind speed, sunshine hours or snow cover. The price of the derivative depends on the amount of the potential payment and the probability that the critical reference value will be exceeded. It is calculated by generating a time series of the relevant climate parameters for a specific reference station. 1 For example, the Association of British Insurers (2005), Financial Risks of Climate Change, Summary Report. (www.abi.org.uk/climatechange). Deleted: have to be cancelled due to adverse weather conditions. The determining factors may be good visibility in the case of air shows and cloudless, high-pressure conditions for film sets, rather than extreme manifestations of certain weather parameters. Formatted: Superscript Deleted: 9 Deleted:. H Deleted: or Deleted:, that is to say Deleted: Formatted: Footnote Text
Other weather hedging products are also based on parametric triggers. For instance, it is possible to hedge against drought risks in Africa by means of a product which utilizes precipitation as the parametric trigger. Another securitization avenue currently being explored is the possibility that anthropogenic climate change risk might also be transferred to the capital markets; however, assessing the risk involved in probabilistic terms is difficult since projecting increased future average temperatures based on climate models represents a typical boundary value problem as it depends on assumptions about global economic development and emission scenarios. A fully probabilistic calculation is therefore hard to achieve. Deleted: Further Deleted: anthropogenic Deleted:. H Deleted: it is difficult to The products described here are just a few of the more prominent examples of a wide range of insurance and financial sector products and services that are based on time series of meteorological parameters and, thus, on climate data in the broadest sense.