hedging climate change

Hedging climate changeRisk report

How insurers can manage the risk of increasing natural catastrophes

Hedging climate change Risk report

2

Imprint

Risk report: “Hedging climate change” | Publication: September 2007 | Author: Allianz Dresdner Economic Research, Dr. Helmut Kesting | Published by: Allianz SE

Layout: Volk:art 51 GmbH, Munich | Contact: Michael Anthony, Allianz SE, Group Communications/Corporate Affairs, Königinstrasse 28, D-80802 Munich

email: [email protected]

Content

Natural catastrophes in thegrip of climate change

Catastrophe risk in im-portant markets is heavilyunder-insured

Natural catastrophes on the march

Are catastrophe risks capable of being efficientlyinsured?

Future insurance potentialin natural catastrophes

Path to the future 1. Extending the insurability of cat risk

2. Rethinking the role of the state

3. Private and public risk partnerships:

the example of flood insurance

Bibliography page 34

page 5

1 2

43 5

page 3 page 10

page 19page 16 page 24


3

At the beginning of February 2007, the Intergovernmental Panel on Climate Change

(IPCC) published the first part of its Fourth Assessment Report on world climate

change.1

On the origins of climate changes, the report finds that:• The main cause is the greenhouse effect which, in the first instance, can be attrib-

uted to an increased level of carbon dioxide in the air. This rose by 30 percent

between 1900 and 2005. Almost half of the increase has come in the past 25 years.

Seventy-eight percent of the higher CO2 concentration has been caused by the use

of fossil fuels. Another 22 percent is because of changes in land use (such as in-

creases in arable land).

• Further causes include rises in the amounts of other significant greenhouse gases

such as methane and nitrous oxide. Their combined increase amounts to half the

increase in CO2 levels.

• By way of comparison, changes in solar radiation have exerted a minimal influ-

ence.

A broad consensus exists that the main cause of climate change is human activity

(especially CO2 emissions). We have ourselves to blame for the rise in global tem-

peratures.

At the same time, however, this means that we are in a position to influence climate

positively. But this means quicker, more effective and globally coordinated efforts.

At the moment, the concentration of CO2 in the atmosphere is about 400 ppm (parts

per million). The current rate of increase is at least 2 ppm a year. However, this rate

is rising because energy and transport needs are increasing worldwide. Even if CO2

emissions were immediately held to present levels, greenhouse-gas concentrations

would still rise to 550 ppm by 2050. The rise in temperatures of the earth’s surface

would not stop but would, over the next decades, increase by at least half a degree.

The transition to the post-fossil fuel age involves the largest program of renewal

and restructuring that the world economy has ever seen.2 Especially affected is elec-

tricity generation (power stations), which is responsible for 40 percent of CO2 emis-

sions, transport (20 percent), industry (18 percent), plus housebuilding, service

Natural catastrophes in the grip of climatechange

1 The IPCC was set up in 1998 by the World Mete-

orological Organization (WMO) and the United

Nations Environment Programme (UNEP).

As an institution independent of governments,

the organization has the task of assessing on a

regular basis the state of knowledge of climate

change and the effects of these changes on

human society. More information can be found

on the institution’s web site (www.ipcc.ch)

2 See essay by Peter Hennicke in Handelsblatt

(20.03.07, 9)


4

industries and agriculture (together about 13 percent). Clearly, this restructuring

also involves changes in consumer habits. Climate protection is not just a matter of

money. At the same time new markets and opportunities for growth are emerging.

The markets for energy efficiency and renewable energy are alone showing a global

growth rate of between 10 and 20 percent.

Allianz SE has for years been warning – as have other insurers – of the results of

unhindered climate change.3 In cooperation with the WWF (World Wide Fund for

Nature) over a number of years, Allianz SE has supported many projects and initia-

tives to strengthen climate protection. It belongs to the Global Roundtable on Climate

Change (GROCC)4 and espouses a broad alliance of all societal forces to work towards

a sustainable energy systems capable of achieving economic growth. Other compa-

nies within the Allianz Group are also committed to climate protection. Dresdner

Bank is the market leader in European Union (EU) Emissions Trading Scheme CO2

certificates. But this is not merely a matter of supporting noble ideals such as the

preservation of life’s fundamentals for future generations. For both Allianz and Dres-

dner Bank, earth warming has long become a matter of business. Regardless of

whether it is insurance, emissions trading, asset management or project financing,

many areas of activity need to take into consideration the effects of climate change.5

Further activity involves the effects of climate change on insurance markets. At

the core of the issue is to what extent, or under which conditions, catastrophes

caused by climate change will be insurable. This is crucial because questions of pros-

perity are linked with insurance: entities such as institutions can protect themselves

from damage that threatens their existence. At the same time, positive influences on

entire economies result when people can act with the backing of insurance – they

are more prepared to enter high-risk innovative projects than they would be without

insurance.

The next part of the report documents the development of damage caused by nat-

ural catastrophes. In view of the existing massive under insuring, it investigates why

markets cannot function effectively when dealing with catastrophe damage. It con-

cludes by discussing private and state initiatives.

The study reaches the following main conclusions:• The number of catastrophes caused by natural forces, as well as the extent of the

damage, indicates a significantly rising trend (Chapter 1) .

• The insurance potential is enormous. Annual expected average total damage be-

tween 2010 and 2020 is estimated at 80-120 billion US dollars (in today’s prices).

But because of the high degree of variability, the real damage could significantly

depart from this figure (Chapter 2).

• These risks are greatly under-insured. As a whole, the markets for catastrophe

risks do not functions optimally. They are linked with the specific characteristics of

such large risks which, among other things, makes their diversification difficult

(Chapter 3 and 4).

• Innovative developments by insurers such as catastrophe bonds, (cat bonds)

which enable risks to be transferred on capital markets, as well as private and

public risk partnerships, can help improve the insurability of catastrophe risks

(Chapter 5).

3 To examine the activities of the European

Insurance and Reinsurance Federation on

climate change, see www.cea.assur.org

4 See www.earth.columbia.edu/grocc

5 See www.allianz.com/Klima


5

Both the frequency and extent of natural catastrophes have, over recent years,

increased notably. Before taking a closer look at this, some concepts need to be

clarified.

Where many separate cases of damage have a common cause, they are said to be

in a close time and geographical context. If the total amount of damage reaches an

extraordinarily high level measured against “normal” conditions, the reference is to

a catastrophe.6 But catastrophes occur in different categories.

A natural catastrophe is described as one being caused by natural forces. These

include flooding, storms, earthquakes, tidal waves, drought/bushfire/heat, cold/frost,

hail or avalanches. The extent of the damage depends not only on the strength of the

natural force. A role is played by preventive measures as well as technical or organi-

zational factors that can help limit the consequences. The overall damage from a

natural catastrophe always has a social dimension.

Man-made catastrophes are events that are closely associated with human activity.

Mostly of the time these involves a large object in a confined space, such as a build-

ing complex.7 Among such catastrophes are, for example, large fires, explosions, air

crashes, or mine disasters.

Terrorist attacks are a special category of catastrophe risk. They are not due to

chance but a result of premeditated human action. In contrast to natural or man-

made catastrophes, the probability of occurrence cannot be assessed using the

usual insurance processes.

The number of natural catastrophes since 1970 is shown in Chart 18. The trend is

clearly upwards. The annual number of natural catastrophes has, over the period,

continually risen. The representation of the trend as straight (linear function) ac-

cords with the overall picture of the data. The vast majority of natural catastrophes

are weather-related (see Chart 3). This suggests that there is a connection between

the increases in natural catastrophes and the likewise slow but constantly rising

increases in global temperatures as a consequence of climate change.

1Natural catastrophes on the march

6 What precisely “extraordinarily high” is is obvi-

ously an issue of convention. For the reporting

year of 2006, Swiss Re used, among others, the

following definition: for insurance losses in ship-

ping, 16.1 million U.S. dollars; in civil aviation,

32.2 million U.S. dollars; and in other areas, 40

million U.S. dollars. Smaller claims were there-

fore not included in the sigma catastrophe data

bank. As well as that, adjustments for inflation

were made to ensure consistency of data

7 Wars, including civil wars and events that resem-

ble wars, are excluded

8 The sources are the catastrophe data banks of

the two largest reinsurance companies, Swiss Re

and Muenchener Rueck. See the equivalent

indices from sigma (www.swissre.com) and Top-

ics Geo (www.munichre.com)


6

Chart 1

Number of natural catastrophes

In contrast, the annual level of claims resulting from natural catastrophes covered

by insurers shows from the outset a far higher variation because the total claims in

any given year are heavily dependent on whether, in that year, there is one or more

large catastrophes. Chart 2 confirms this. The year 1992 was the year of Hurricane

Andrew, up until then the second largest natural catastrophe (see Table 1). In terms

of claims, the record year was 2005, when Hurricanes Katrina, Wilma and Rita all

struck. They stand respectively in places 1, 6 and 7 on the table of the 40 most-expen-

sive insurance claims ever. The year 2006, however, shows a relatively low level of

claims amounting to just 12 billion US dollars. This is because, with ten storms de-

clared, and five of them reaching hurricane strength, the level was merely average

because no built-up areas were hit and there was thus little damage9.

Chart 2 basically shows the rising trend of damage. The increases are dispropor-

tionate. This is clear if the period covered is divided in two. Up until 1988 the total

damage exceeded the 10 billion US dollar level only once. But from 1989, the total

annual damage is only four times below that level and has exceeded the 15 billion-

dollar mark a total of ten times, sometimes considerably. That the differences of

claims increases in absolute terms over two successive years suggests the trend is

subject to a non-linear function.

Source: sigma 2/07

Source: sigma 2/07

180

160

140

120

100

80

60

40

20

01970

19731976

19791982

19851988

19911994

19972000

20032006

19701973

19761979

19821985

19881991

19941997

20002003

2006

100

80

60

40

20

0

Chart 2

Insured natural catastrophes ($ billion US)

9 See Topics Geo – Naturkatastrophen 2006


7

Table 1

The 40 most expensive insurance claims 1970-2006

Insured Losses10

In $ mn US, inflation

indexed as of 2006

Date

(Beginning)

Event Country

66,311 25.08.2005 Hurricane Katrina; floods, burst levees, USA, Gulf of Mexico, Bahamas, oil platform damage North Atlantic

22,987 23.08.1992 Hurricane Andrew; floods USA, Bahamas

21,379 11.09.2001 Terror attack on World Trade Center, USAPentagon, other buildings

19,040 17.01.1994 Northridge Earthquake, LA USA(6.6 on the Richter Scale)

13,651 02.09.2004 Hurricane Ivan; Oil-platform damage USA, Caribbean, Barbados, elsewhere

12,953 19.10.2005 Hurricane Wilma; rain, flooding USA, Mexico, Jamaica, elsewhere

10,382 20.09.2005 Hurricane Rita; flooding, USA, Gulf of Mexico, CubaOil-platform damage

8,590 11.08.2004 Hurricane Charley USA, Cuba, Jamaica, elsewhere

8,357 27.09.1991 Typhoon Mireille/Nr. 19 Japan

7,434 15.09.1989 Hurricane Hugo USA, Puerto Rico, elsewhere

7,204 25.01.1990 Winter storm Daria France, UK, Belgium, elsewhere

7,019 25.12.1999 Winter storm Lothar Switzerland, UK, France, elsewhere

5,500 15.10.1987 Storms, Flooding in Europe France, UK, Netherlands, elsewhere

5,485 26.08.2004 Hurricane Frances USA, Bahamas

4,923 25.02.1990 Winter storm Vivian Europe

4,889 22.09. 1999 Typhoon Bart/Nr. 18 Japan

4,366 20.09.1998 Hurricane Georges; flooding USA, Caribbean

4,100 05.06.2001 Tropical storm Alison; flooding USA

4,022 13.09.2004 Hurricane Jeanne; flooding, landslides USA, Caribbean (incl. Haiti), elsewhere

3,826 06.09.2004 Typhoon Songda/Nr. 18 Japan, South Korea

3,512 02.05.2003 Thunder storms, tornados, hailstorms USA

3,415 10.09.1999 Hurricane Floyd; flooding USA, Bahamas, Colombia

3,409 06.07.1988 Explosion on platform Piper Alpha UK

3,315 01.10.1995 Hurricane Opal; flooding USA, Mexico, Gulf of Mexico

Table 1 indicates the non-linear increase of expected claims levels. Thirty-four

(85 percent) of the largest 40 catastrophes happened between 1988 and 2006, while

15 (about 38 percent) happened after 2000. It is also clear that the majority of cases

of large claims were, by a long way, related to natural catastrophes. The terror attack

of September 11, 2001, is – at least so far – a rare exception.


8

Insured Losses10

In $ mn US, inflation

indexed as of 2006

Date

(Beginning)

Event Country

3,270 17.01.1995 Great Hanshin Earthquake in Kobe; Japan(7.2 on the Richter Scale)

2,905 27.12.1999 Winter storm Martin Spain, France, Switzerland

2,736 10.03.1993 Snowstorms, tornados, flooding USA, Canada,Mexico, Cuba

2,587 06.08.2002 Serious flooding UK, Spain, Germany, elsewhere

2,516 20.10.1991 Urban forest fires, drought, in California USA

2,505 06.04.2001 Hailstorms, tornados, flooding USA

2,364 18.09.2003 Hurricane Isabel USA, Canada

2,331 05.09.1996 Hurricane Fran USA

2,305 03.12.1999 Winter storm Anatol Denmark, Sweden, elsewhere

2,299 11.09.1992 Hurricane Iniki USA, North Pacific

2,217 29.08.1979 Hurricane Frederic USA

2,155 23.10.1989 Petrochemical works explosion USA

2,134 26.12.2004 Earthquake (9 on the Richter Scale), Indonesia, Thailand, elsewhereTidal wave in Indian Ocean

2,091 19.08.2005 Rain, landslides, flooding Switzerland, Germany, elsewhere

2,044 18.09.1974 Tropical Cyclone Fifi Honduras

2,009 04.07.1997 Flooding after heavy rain Poland, Czech Rep., Germany, elsewhere

Data analyzed by Muenchener Rueck arrives at similar conclusions11. About 16,000

natural catastrophes from between 1980 and 2005 were examined. The catastrophes

were divided into six loss categories: 1. Small Losses; 2. Medium Losses; 3. Medium-to-

Serious Losses (totalling more than 60 million US dollars); 4. Serious Losses (more

than 200 million dollars); 5. Devastating Losses (more than 500 million dollars); and

6. Huge Natural Catastrophes (extreme losses as defined by the United Nations).

When the incidence of catastrophes was worked out, the dominating occurrence

with more than 85 percent was weather-related natural catastrophes. This empha-

sizes again the central significance of climate change for catastrophe insurance12.

The incidence of catastrophes however in percentage terms differ little from one of

the three (consolidated) loss categories to another. Earthquake related losses are the

least evenly divided.

Source: sigma 2/07

10 Property damage and production breakdown

claims, but not life assurance or third-party

liability

11 See www.munichre.com and the NatCatService

page

12 However the greatest conceivable insurance

losses are connected with earthquakes. A serious

earthquake in California and a major earthquake

in Tokyo with total losses running into trillions of

US dollars would probably cause a world-wide

depression. Astronomical events such as the ex-

treme case of a collision with a black hole are not,

for obvious reasons, taken into account


9

A completely different picture emerges if the significance of individual categories

in relation to total losses is considered. Here, major losses dominate, although they

comprise just three percent of all cases. Categories 5 + 6 are responsible for the bulk

of all losses and deaths with respectively 80 percent and 86 percent.

According to Muenchener Rueck, the geographical distribution of losses is:• With 4,500 disasters, most of the damage is in Asia, the most heavily populated

continent with the most cities and conurbations. Although 70 percent of the disas-

ters involved small losses, this region was also hit by the highest number of large-

scale and devastating disasters (225).

• Most of the fatalities were also in Asia, with more than 800,000. Almost 90 percent

of these were victims of Category 5+6 occurrences.

• By comparison, both Europe and North America (USA and Canada) were hit by

an almost equal number of natural catastrophes. Most of those in Europe resulted

in small losses while in North America there was a high proportion of major dam-

age. Consistent with this is the fact that the total damage in North America was

three times as high as in Europe. But in absolute terms, more people died in

Europe, mainly because of the heat wave of 2003 which claimed the lives of more

than 35,000.

Overall, it is clear that over the past decades the insured damage caused by natural

catastrophes in relation both to the number of linear occurrences and the non-linear

extent of damage have increased. The annual total damage is far and away a conse-

quence of large natural catastrophes, which are mainly the result of extremes of

weather (various storms, including hurricanes and typhoons, as well as flooding,

drought and heat waves).

Source:

Muenchener Rueck

45%

40%

35%

30%

25%

20%

15%

10%

5%

0%Category 1+2 Category 3+4 Category 5+6

Temperature extremes,mass motions such aslandslides

Chart 3

Catastrophes according to category (1980-2005)

Earthquakes, tidal waves, volcanic eruptions

Storms

Flooding


10

As impressive as the losses are, they cannot be said to reflect the extent of the real

damage. There are various reasons. The report on insured losses put together

by Swiss Re does not cover all actual insured losses. The central issues are property

damage and production interruptions. Because of slow data accumulation, liability

insurance and life assurance were also not included.

Non-insured losses are also part of the picture. Apart from private losses, this also

includes damage to public infrastructure, which is almost never insured.

An important role is played by the way the term “damage” is defined13. Insurers

mostly choose a relatively tight interpretation in the interests of topicality, precision,

and commercially related reasons that in the first instance aim at defining immedi-

ate losses. Broader economic analyses tend to take into greater consideration indirect

losses such as consequential damage. In this way, apart from the immediate costs of

a loss of production because of damaged manufacturing capacity, projected reduc-

tions in growth are also considered.

Definitions can, in fact, be so broad that even “non-monetary” damage such as loss

of quality of life can be included. However, there are usually insuperable problems in

estimating and assessing such losses. The difficulties of arriving at a “quantitatively

measurable assessment” were especially clear in the case of the tidal wave that in

2004 was unleashed by a submarine earthquake in the Indian Ocean. In Table 1, it is

listed in fourth-to-last-place because, in the first instance, losses in the tourist indus-

try were taken into account. The insured losses were estimated at only about two bil-

lion US dollars. That is in no way commensurate with a human tragedy that resulted

in the deaths of more than 280,000 people and the destruction of the means of exis-

tence for innumerable families.

This is why the various estimates of total damage are only to some extent compa-

rable and need to be interpreted with reference to the methods used in compilation.

This means they should be used as a guide only rather than an exact estimate. In

spite of these problems, the differences between insured and real losses is relevant

2Future insurance potentialin natural catastrophes

13 It has already bee pointed out that the term

“natural catastrophe” is defined differently by

Swiss Re and Muenchener Rueck. Typically,

Muenchener Rueck’s numbers are smaller

because of a tighter definition


11

because the latter can be many times bigger. One spectacular example is that of Hur-

ricane Katrina, in August 2005, which is the biggest individual case of loss (see Table

1). The insured losses amounted to 49 billion US dollars whereas the total losses

amounted to 144 billion dollars14.

This disproportion between total losses and insured losses is greatest in develop-

ing countries, where insurance markets are rudimentary. In 1996 flooding in China

caused losses of about 24 billion US dollars, of which less than 500 million dollars

were covered by insurance – a ratio of 48-to-one, high which ever way it is looked at.

Two years later, more flooding in China caused an economic loss of 30 billion dollars.

Insurance covered just one billion dollars – a ratio of 30 to one.

But cases of extremely high imbalances between total losses and insured losses

are also known in industrialized countries. In the Kobe earthquake of 1995, the ratio

was 37 to one – 110 billion dollars to just three billion dollars. Normally, this ratio is

between 1.5 and 5, depending on the tightness of damage definition. Improved insur-

ance options, obligatory safeguards (such as in the case of mortgage credit) and bet-

ter provisions against contingencies ensure that at least relatively closely defined

total losses do not exceed insured losses by more than a low single figure15.

Scientists and specialized companies develop complex catastrophe models to help

estimate future losses in natural catastrophes. These models use specific regional

influences to predict the consequences of climate change in terms of the respective

loss categories. (See Chapter 5.1) Ideally, the accumulation of a wealth of individual

factors would then result in a global pattern.

Because of the available information, such a bottoms-up approach is however,

not really practicable. The catastrophe models are drawn up specifically with the rel-

evant risks in mind with the result that an incalculable number of equivalent model

results need to be processed. This more-or-less trips up on the fact that free access

to most model results is not available. Instead, the alternative here is to estimate pro-

jected global losses over the next decade (2010-2019) with the aid of a simple statis-

tical process. The trend of insured losses is first extrapolated; this result is then cal-

culated into projected total losses.

This process is, however, based on the assumption that over the following years,

the fundamental cause-effect structure remains essentially the same. This makes

sense. At a global level, three main factors are responsible for climate-related natu-

ral catastrophes. The first is climate change itself. The temperature rises caused by

it have been noticeable in the past few years. According to the World Meteorological

Organization, 2006 will probably go down as the sixth warmest year on record. The

past six years are now among the seven “record years.”16

Climate change marches on. That is not surprising, because the climate reacts

to changed greenhouse-gas emissions very sluggishly and with considerable time

delays. In comparison with pre-industrial levels (1750-1850) the global average tem-

perature has risen half a degree Celsius. And it is certain that the current greenhouse-

gas concentration in the atmosphere will lead to an increase of at least another half

degree in the coming decades.

The extent of global losses caused by natural catastrophes is influenced by two

other main factors: one is worldwide economic growth, and the other is urbanization,

a process that goes hand in hand with flight from the land. In 1950, 2.5 billion people,

or 30 percent of the world’s population, lived in cities. According to United Nations’

estimates, by 2025, a total of 8.3 billion people, or 60 percent of the world’s popula-

tion, will be in cities. The number of mega cities with more than 10 million people

will increase from 12 in 1990 to 26 in 2015. Both these factors, growth and urbaniza-

tion, are bringing about an increase in the concentration of assets in endangered

regions17. It is plausible to assume that in the next few years there will be no change

in the basic trends – as with climate change.

This leads to an estimate of average losses for the period 2010-2019. On the basis

of deducing an average from insured-losses data (see Chart 2) a potential trend func-

tion emerges. The determining coefficient of 0.954 shows that the value of the trend

14 Sigma 2/07, 13. The proportion is smaller if the

state’s own NFIP National Flood Insurance Pro-

gram is considered, because the insured losses

then rise to 66.3 billion dollars. On the other

hand, the total losses from Katrina are estimated

at up to 170 billion dollars (Kunreuther/Michel-

Kerjn, 2007, 22), which increases the difference

15 See Kunreuther/Michel-Kerjn, 2007

16 For a description of the extreme weather condi-

tions in 2006, and their position in a larger con-

text, see Topics Geo (2006), 42

17 In addition, coastal regions such as Florida have

a magnet effect. The population of Florida is ris-

ing from 6.8 million in 1950 to a projected 19.3

million by 2010. See Kunreuther/Michel-Kerjan,

2007


12

curve broadly concurs with the data for the period 1970-2006 (see Chart 4). The right-

hand column indicates that this trend will continue. The average annual insured

damage over the period 2010-2019 amounts therefore to a projected 41 billion US

dollars (in constant prices of 2006).

This extrapolated trend suggests that the average losses in the period 2000-2006

is roughly equivalent to the average losses for the entire period 2000-2010. In order to

be able to use the historical values to calculate projected actual losses, the assump-

tion must be made that the relation between insured losses and total losses will not

significantly change.

The assumption over historical values is not as problematic as it might seem at

first glance. To take an example, an increased awareness of risk leads to higher in-

surance penetration. It is highly probable that this would mean a lower ratio of

insured losses to total losses. The overall effect would be small. If such a change in

ratio were not lasting, it would not matter, because of the average reading. However,

the quality of the prognosis does depend in the first instance on how stable the frame-

work conditions remain. Otherwise, there would be no point in calculating a future

trend.

The following findings emerge: • With a conservative forecast (tight loss parameters) with a factor two it is to be

expected that projected average annual total damage in the period 2010 to 2019

will be in the region of a good 80 billion US dollars18.

• With a more progressive forecast (broader loss parameters) using a factor of

three, the projected annual figure rises to a good 120 billion US dollars.

Two things need to be noted about interpreting these figures. First, both estimates

are actually conservative inasmuch as a relatively tight loss parameter (low ratio of

total to insured losses) is used. From an insurance/technical point of view – bearing

in mind that the level of premiums to be calculated needs to be sufficient to cover

risks – the expected losses need to be expressed as unambiguous and quantifiably

as possible.

Second is the fact that this deals with averages. Annual total losses can vary con-

siderably up or down from this average. An initial indication of the dimensions of

these annual departures from the trend is shown in Chart 2. Over the past 16 years

(from 1990 to 2006) insured losses exceeded their trend values seven times. In three

of these years, the variations were considerable: in 2004 the insured losses were

more than 150 percent of the trend value. In 1992, they were twice as large, and in

18 In today’s prices. Nominal amounts for, as an

example 2010, are calculated with an inflation-

ary adjustment

Source: own calculations

$45 bn

$40 bn

$35 bn

$30 bn

$25 bn

$20 bn

$15 bn

$10 bn

$ 5 bn

01970-79 1980-89 1990-99 2000-06 2010-19

Chart 4

Average insured losses per period ($ billion US)


13

2005 more than three times as high. In line with the logic of the method used, it

means that annual total losses of up to 400 billion US dollars (progressively calculat-

ed) are not merely possible but probable, bearing in mind that the calculations do

not even include any extremely rare occurrence.

Muenchener Rueck data confirms this conclusion. It finds that between 1975 and

2006, total losses exceeded the respective trend value eight times, and by large

amounts: three times well over 150 percent above, three times around 200 percent

more, once well over 200 percent greater, and once almost four times as much19.

Another indication that extraordinarily high annual losses need to be reckoned

with can be seen in Table 2. It uses Swiss Re loss reference figures that in giving an

idea of the dimensions of damage potential includes rarer and larger natural

threats. Long recurrent periods have been intentionally chosen because the length

of the period under observation the amount of the maximum damage increases.

Such mega damage occurs rarely, but it does occur.

19 Again, Muenchener Rueck and Swiss Re calculate

using different data limits. But because the

amounts are percentages, the differences do not

matter. See Topics Geo 2006, 47

Table 2

Major natural disasters in selected regions

Country Occurrence Review period Total damage % of GDP Uninsured

years (approx.) in $ bn US in %

Japan Earthquakes 200 500 11.5 0-95

USA Earthquakes in California 200 300 2.3 80-90

USA Hurricanes 200 300 2.3 40-60

Japan Typhoons 200 50 1.1 60-80

Italy Earthquakes 500 50 2.7 70-80

Turkey Earthquakes 500 50 12.6 70-80

Mexico Earthquakes 500 50 5.9 80-90

Portugal Earthquakes 1,000 50 25.9 80-90

Britain Storms 200 30 1.3 10-30

Canada Earthquakes 500 20 1.6 30-50

Australia Earthquakes in Sydney 1,000 20 2.7 30-50

France Storms 200 15 0.7 10-30

Germany Storms 200 15 0.5 40-60

Netherlands Storms 200 7 1.0 10-30

Belgium Storms 200 5 1.3 30-50

Source: sigma 2/07


14

Table 2 shows these damage references in selected countries. It deals with esti-

mates for isolated events. Often, these events are not independent of each other.

As an example, winter storms in Europe can hit the entire continent and not just

individual countries. Swiss Re estimates the 200-year reference damage for Europe

at 50 billion US dollars.

The future loss potential from natural catastrophes is also notable. Sums of low

three-digit billions for annual total losses must be regarded as a rule rather than as

an exception. At any given time, a rogue occurrence could push the figures much

higher. So the insurance markets are facing future challenges that demand valid

parallels from the past.

This data also can be placed into another context. The effects of climate change

are not exhausted by large natural catastrophes. In October 2006, Sir Nicholas Stern,

former chief economist at the World Bank, presented his report, The Stern Review on

the Economics of Climate Change, for the British government. It was the first econom-

ic investigation to contain a comprehensive quantitative estimate of the long-term

consequences of climate change and the possible measures that might be taken20.

According to the Stern Report, climate change threatens to become the greatest

market failure ever seen . It “threatens the fundamentals of human life in the entire

world – access to water, food production, health, and the use of land and the envi-

ronment”. The most important consequences for humans and nature are outlined

in Box 1.

20 www.hm-treasury.gov.uk/independent_

reviews/stern_review_economics_climate_

change/sternreview_index.cfm

• Melting glaciers bring first an increased risk of flooding, andthen sharply reduced reserves of water. This would threaten onesixth of the world’s population – mainly on the Indian subconti-nent, parts of China, and in the South American Andes.

• Declining crop yields, especially in Africa, could mean thathundreds of millions of people will be unable to produce or buysufficient food. In the medium to high latitudes, crop yields mightincrease with moderate rises in temperature of between twoand three degrees Celsius, but then go into decline as tempera-tures keep climbing. Increases of four degrees and more wouldprobably seriously affect global food production.

• At higher latitudes, the incidence of death linked to the coldwould increase. Climate change would mean higher death ratesworldwide because of inadequate nutrition and heat. Diseasessuch as malaria and dengue fever would spread if no effectivepreventative measures were taken.

• Rising sea levels would, at temperatures increases of three or fourdegrees Celsius, mean flooding every year for dozens or evenhundreds of millions more people. Coastlines in South-East Asia(Bangladesh and Vietnam), of small Caribbean and Pacific islandsas well as of big coastal cities such as Tokyo, New York, Cairo, andLondon, would be seriously threatened and pressure to protectthem would increase. It is estimated that, by the middle of thiscentury, 200 million people will be permanently displaced becauseof rising sea levels, worse flooding, and more intensive drought.

• Eco systems will be especially vulnerable to climate change, and a global warming increase of just two degrees Celsius wouldbe enough to threaten about 15 to 40 percent of species withextinction In addition, acidification of the oceans, a direct resultof increased carbon dioxide concentrations, would have seriousrepercussions for marine eco systems, with possibly serious con-sequences for fish reserves.

• Higher temperatures increase the possibility of unleashingabrupt large-scale changes.

• Global warming could cause sudden changes in regional

weather patterns, such as monsoon rains in south Asia or theEl Niño phenomenon. These changes would have serious conse-quences for the availability of drinking water as well as increas-ing the likelihood of flooding in tropical regions and threateningthe lives of millions of people.

• A number of studies indicate that the Amazon rain forests

could be susceptible to climate change, and models point toconsiderable dehydration in the region. One model, for example,comes to the conclusion that global warming of between twoand three degrees Celsius would be enough to damage Amazonrain forests, perhaps irreversibly.

• The melting or crumbling of ice layers would threaten landon which five percent of the human race lives.

Box 1

Global warming has many serious consequences, often involving water:

Source: Stern Report, Summary, Pages VI/VII


15

Some central findings of the report:• The annual losses by the middle of this century would amount to five percent of

global GDP.

• If non-monetary factors such as health, the extinction of many species, and other

non-market effects, as well as the so-called “feedback mechanisms” (which may

mean that as the stock of greenhouse gases increases there is a disproportionate

rise in warming with each new increment in emissions) are taken into account,

the annual damage could rise to as much as 20 percent of world GDP.

• On the other hand, the cost of stabilizing global temperature increases at beneath

the dangerous threshold of two-three degrees Celsius above the temperatures of

the pre-industrial age would amount to “only” one percent of global GDP by 2050.

• If stabilization of temperatures were not successful, costs would increase consid-

erably.

The report’s conclusions are clear: immediate action is easily the best option.

Delayed action would from every perspective be much more expensive. To do noth-

ing is not a realistic alternative, because the consequences would be incalculable21.

The costs of climate change are geographically unevenly distributed. The threat

to developing countries is especially serious: climates there are generally already

warmer than in other regions and precipitation levels are highly changeable. Devel-

oping countries are heavily reliant on agriculture, which is the sector most vulnera-

ble to climate. As well, the low level of economic performance makes it difficult to

put through measures to adjust and adapt.

21 Such a complex prognosis over such a long

period of time is naturally in many ways chal-

lengeable. Some of the objections are certainly

justifiable. Some evaluations are objectively not

clearly resolvable (What, for example, is the

monetary value of an extinct specie?) That as it

may be, the merits of the Stern Report lie in the

fact that, for the first time, the overall conse-

quences of climate change have been scientifi-

cally analyzed


16

Risk-spreading, or general diversification of risk, is among the greatest social

successes of modern society. In view of the damage potential of natural catas-

trophes, efficient insurance markets are of especial importance so that both private

people and companies can be protected from ruinous damage. A second benefit is to

prevent the consequences of such damage from spreading across entire economies.

However, empirical surveys suggest that these markets do not function quite

as ideally. In cases where actual damage exceeds insured damage by a substantial

amount, insurance penetration is small. In other words, risks related to natural

catastrophes (cat risks) are highly under insured. There are considerable deficits in

coverage, as data compiled by Swiss Re shows (Table 2).

Various studies reveal a series of empirical abnormalities, which also indicate that the efficiency of catastrophe-risk markets is not high22:• A large proportion of catastrophe risks are not insured. Large industrial firms

tend to insure themselves. Policies that offer cat-risk cover are often inadequately

reinsured. In total, risk sharing functions much better with small/medium risks

than with major risks. It can be said that the diversification of cat risk is not opti-

mal.

• Cat risk premiums for both insurance and reinsurance are very expensive in some

markets. They can amount to as high as seven times as high as expected losses23.

Pricing is sometimes inefficient.

• Premium changes depend to a large extent on the appearance of natural catastro-

phes. After Hurricane Andrew, in August 1992, premiums rose sharply. After the

Northridge (Los Angeles) earthquake in 1994 and when no further major events

occurred, they eased somewhat. (see Chart 5) Demand for cat-risk insurance is

also occurrence dependent.

This occurrence dependence indicates that the market players do not act rationally

and/or that mistakes are happening in the market. It is difficult to establish why the

3Catastrophe risk in important markets isheavily under-insured

22 See Froot (2001), Cummins (2006) and

Jaffee/Russell (1997)

23 See Froot (2001): 539. The level of expected

losses for each respective period are regarded as

a benchmark for a “fair” premium. However

there can be objective reasons for surcharges on

premiums. See text for more on this


17

absence of a catastrophe during a relatively short period should result in either a

reassessment of projected losses (and thus premiums) or of demand. On the other

hand, the appearance of a catastrophe can cause a reassessment of risk provided

that new information (over, for example, the level of damage) emerges. But only

then. There is no automatic actuarial context between the occurrence of a catastro-

phe and changes in premiums.

This all goes to show that the global market for cat insurance is decided by oc-

currence-driven cycles. Chart 5 appears to confirm this. The Rate-on-Line-Index is a

measurement of the tone of the market. It is defined as the relation between insur-

ance premiums on the basis of reinsurance policies to the maximum agreed cover-

age in those policies.

A higher index hints at a “harder” market phase. When big losses occur as a result

of a major occurrence, reinsurance capital becomes rarer and reinsurers are pre-

pared only to offer a narrow range of coverage. At the same time, awareness of risk,

and thus the demand for insurance cover normally rises to a high level. Because of

their strong positioning on the market, reinsurers can achieve high premiums for

relatively low levels of cover (higher index).

If no further catastrophes strike, then, over time, market relations change. Rein-

surers have more capital because of profits. Then, lured by attractive conditions,

new capital comes to the sector. Awareness of risk declines, and thus the demand

for insurance cover. Premiums begin to sink (on average to as low as 50 percent of

their peak levels), and the market enters a “soft” phase with low premiums and rela-

tively high amounts of cover in money terms (lower index).

The ideal type of situation pretty well reflects reality, as is shown in Chart 5. The

index rose sharply, principally because of Hurricane Andrew, but then, after 1993,

went into a long decline. However, 11 September 2001 and a generally tougher mar-

ket conditions at the turn of the century again ushered in a “hard” phase.

Similarly, the years 2004/2005 show that this development does not take place

automatically and that record catastrophe years do not always lead to ever higher

indexes. New insurance capital (Bermuda-registered reinsurers) and general factors

such as advances in cat models (ascertaining premiums that are adequate to cover

risk) have a dampening effect on the Rate-on-Line Index.

Chart 5

World Rate-On-Line Index: catastrophe reinsurance

Source: Carpenter (2005)

400

350

300

250

200

150

100

50

01990

19911992

19931994

19951996

19971998

20032004

20051999

20002001

2002

Business for primary insurers is even more cyclical. Chart 6 shows the underwrit-

ing cycle of United States property insurers. The operational yields (in percent) pro-

vide data about the profitability of pure insurance business (underwriting policy)

that is determined by the relation of premium income to paid compensation claims.

On the other hand, general yields also take into account results from the financial

assets.

Here, the influence of major occurrences becomes especially clear. Following

on the heels of the big insurance crisis at the beginning of the eighties, a number

of losses as a consequence of catastrophes (Andrew in 1992, the Northridge Earth-

quake in 1994, and the World Trade Center attack in 2001) ushered in “hard” market

phases24.

The extent of under insuring as well as an inadequate level of functioning risk

diversification raise the entire issue of the insurability of catastrophe risks. Which

is dealt with in the next section.


18

24 For more on the indemnity insurance crisis

see Lai and others (March 1997). For an outlines

of major insured losses in the USA, see Litan

(2006 b): 16

Source: A.M. Best

Company, according

to Cummins (2006)

Chart 6

Yields of US property insurers (%)15

10

5

0

-5

-10

-15

-20

General yields Operational yields

19791981

19831985

19871989

19911993

19951997

19992001

2003


19

Insurers take on risks and then diversify by grouping similar risks in a collective.

This spreading of risks in a group works best when it deals with many individual

risks of limited extent and not connected with one another. One example of this is

motor vehicle insurance. In this case, the total losses vary relatively little from year

to year. This enables simple premium strategies. For example, premiums can be

adjusted to the average losses of the past three years25.

In such situations, the insurer can assume that, in any given period, premium

income will essentially be sufficient to cover insured losses. Of course additional

capital is required as a buffer just in case total losses in any given year are unexpect-

edly high. But this demand for capital is relatively small and visible at a glance be-

cause losses and premiums develop overwhelmingly constantly and in accord with

one another.

These examples allow various criteria that are relevant to the insurability of risks to be elabo-rated (in Box 2, insurability is dealt with using mathematical and statistical yardsticks):• A large number of people need to be exposed to a certain risk. They then form a

group within which risk spreading can take place. The insured must be averse to

risk. This implies a readiness to use financial means to transfer the risk to a third

party.

• A private offer to insure these risks comes about when the insuring company is in

a position to levy an appropriate premium. The prerequisite is that the probability

of occurrence and the extent of damage can be reliably estimated. This applies

particularly in “high-frequency – low-severity” risks (as in the above example).

• Occurrence and extent of damage must not be subject to influence by the insured

party. If that were the case, the moral-hazard situation might come into play. This

might take the form, for example, where the insured party is more prepared to

take risks than he or she would be without insurance. The result of this would be

doubts about the calculability of risks26.

4Are catastrophe risks capable of being efficientlyinsured?

25 Of course determining levels of premiums is

subject to other factors such as the competitive

situation. But on the issue of risk insurability this

is a secondary matter

26 An ex-ante moral hazard emerges in the case of

catastrophe risk, for example, if, after an insur-

ance policy has been signed, prevention meas-

ures are not taken. An ex-post moral hazard situ-

ation arises when the insured party desists, after

the outbreak of a catastrophe, to act to limit the

extent of that catastrophe (Pfister 2003: 12)


20

In mathematical terms, insurability rests on two laws of statistics:the law of large numbers and the central limit theorem. Lossesresulting from exposure to a risk can be interpreted as a randomvariable. Damage occurring within a certain period forms a randomsample of this random variable. In accordance with the law of largenumbers, the middle value of this random sample tends “almostcertainly” to move against the expectancy value of the randomvariable, provided that the extent of the random sample equateswith a desired size. In an insurance context, this means that theexpected losses (= the fair premium) can be reliably approxi-mated with the help of a middle value of realizations from the pre-vious period provided we are dealing with a high-frequency riskunder which many cases of realizations (large random sample) areavailable. Alternatively, these many cases of realizations made byinsurers (collective) inside a year can be equated with identicallydistributed random variables (same risk). On the other hand, medi-um levels of losses give a reliable approximation of the expecteddamage (fair premium), provided the group and thus the numberof realizations is high.

The insurer, however, needs to retain capital in case of unexpectedlosses. These capital requirements can be calculated with the aidof this central limit theorem. Then the (standardized) spread ofunexpected losses (= total of damage minus premium income)tends with increasing random surveys (large collective) against thenormal distribution. It transpires that in a large group, identical andindependently spread risks with relatively small variations of capital

requirements per policy strive towards zero. This lends weight tothe conclusion that the insurer remains solvent if he determinesthe premium approximately with the aid of the projected damage.Insurance markets with many identically spread , independent riskswith moderate variations are consequently locally insurable (=only through the primary insurer).

With increasing capital requirements, reinsurance becomesimportant. This is the case with large variations and less identicallyspread but statistically independent risks. If the risks are correlatedamong each other, then the capital requirements per policy riserapidly in line with the number of risks. Instead of risk spreadinginside the group, the accumulation of risks becomes a threat.Under certain circumstances, reinsurance can even here step in.Locally dependent risks can be globally independent. Take theexamples of losses caused by tornados in the Mid West of the Unit-ed States and Australia. The reinsurer can also diversify in that heretains a global portfolio containing many risks that are independ-ent of each other. Locally uninsurable risks that in this way, with thehelp of a reinsurer are globally diversifiable, are termed globally

insurable risks. With an increasing dimension of these risks, tradi-tional reinsurers however do push up against limits, so that newmethods of risk diversification become crucial. (see Chapter 5.1)

Box 2

Insurability and risk classification27

27 For a more formal account, see Cummins 2006:

342

28 Occurrences causing damage of at least one bil-

lion US dollars and/or claiming at least fifty lives

were included. See American Re (2002)

If all the above criteria are fulfilled, then conditions are ideal and the process of

risk spreading functions optimally in the group. But for natural catastrophes, the cri-

teria are either not or only partly fulfilled because with catastrophe risks (cat risks),

it is a matter of “low-frequency – high severity” risks. From an actuarial viewpoint,

this has weighty consequences.

1. Problem: the predictability of occurrence likelihood and loss levels. In the case of

frequently occurring damage, the insurer has possession of sufficient data to be able

to calculate, with the aid of statistical processes, a “fair premium.” However, natural

catastrophes are a relatively rare occurrence. Which is why the amount of data

available to calculate the probability of losses in the future is low. This means the

projections are loaded with considerable uncertainty.

Another difficulty is that in the case of extreme occurrences, the spectrum of pos-

sible damage is extreme. This is because the damage caused by a hurricane does

not entirely depend on storm strength but also on the route it takes. Between 1950

and 2000, there were a total of 51 cases of major damage caused by natural forces28.

The next table outlines the extraordinarily large spectrum of damage.


21

Natural catastrophes might occur rarely, but the damage they cause varies enor-

mously. Future incidence often varies considerably from past incidence. This is shown

by the example of Katrina in 2005, where the total economic damage is estimated as

high as 170 billion US dollars. This exceeds by a factor of four the next most devas-

tating hurricane between 1950 and 2000.

If the level of future projected damage does not allow itself to be adequately deter-

mined, this has consequences for premium calculation. Security loadings are levied

on the presumed projected level of damage. Because the insurer himself is risk

averse and, in view of the scale of the possible damage, becomes worried about his

own solvency, these loadings can either be pretty high and/or the insurer reduces

his offer or even declines to offer cover at all.

2. Problem: high capital requirements. With natural catastrophes, occurrence pat-

terns of the damage is highly variable from year to year, and thus inconstant. In

many years, damage is slight. In rare cases, mega damage is the threat. As a result,

premiums and damage trends are to a high degree not synchronized. The following

table illustrates the point using Californian earthquake insurance.

Table 3

Historic damage in $ billion US

(6 cases) (27 cases) (18 cases)

Earthquakes Hurricanes Flooding

Source: American Re (2002)

Maximum 51.3 37.0 25.1

Median 2.7 3.0 1.6

Minimum 0.1 0.9 0.1

Table 4

Annual losses as a percent of premium income (1972-1994)

Source: Jaffee/Russell (1997)

72

0.0

73

0.6

74

3.4

75

0.0

76

0.0

77

0.7

78

1.5

79

2.2

80

9.2

81

0.9

82

0.0

83

2.9

84

5.0

85

1.3

86

9.3

87

22.8

88

11.5

89

129.8

90

47.0

91

17.2

92

12.8

93

3.2

94

2,272.7

Where coverage involves small risks and many claims (such as motor vehicle

insurance), payments is made in the group within a certain period, usually a year.

In the case of rare and high losses risk diversification takes on a time dimension

(intertemporal diversification). The catastrophe insurer must at all times have

access to considerable amounts of liquid capital, apart from premium income, in

case catastrophe losses exceed premium income by a hefty amount.


22

As an illustration, if we assume a one percent occurrence probability per year,

then annual premiums equate to one hundredth of the calculated loss29. This means

that capital required in a case of catastrophe can be as much as one hundred times

larger than the premium income in the year the catastrophe occurs. The risk that

the catastrophe occurs before the insurer has collected a sufficiently large propor-

tion of premium cash (including interest) is termed “timing risk.30”

There are various ways of going ahead with intertemporal diversification. The

most common ways are self- and reinsurance31. With self insurance, the primary

insurer backs the catastrophe risk with own capital. In view of the sums that are

here involved, this version has limits. The funds needs to be in the forms of liquid

assets, and thus subject to low-interest rates. If an effort to cover the cost of own

capital through the equivalent loading on the insurance premium, the yield on capi-

tal sinks. Expensive and rare capital thus limit the underwriting capacity of the

insurer. A high reserve of cash then makes the firm an ideal target for a takeover32.

3. Problem: sufficient reinsurance capacity? With catastrophe losses, the primary

insurer cannot manage without risk transfer. The risk-transfer capacity then becomes

the limiting factor of cat-risk underwriting capacity. From 2000 to 2005 the annual

average level of insured losses from natural catastrophes worldwide amounted to

about 30 billion US dollars (Chart 5). The narrowly defined annual total damage

was estimated at between 60 and 90 billion US dollars (factors two or three). By con-

trast, reinsurers had available a worldwide average of about 300 billion US dollars

in capital33.

At first glance it might seem that there was more than enough capital to insure

natural catastrophes. The truth is however that such a global comparison is more or

less irrelevant. First, it deals with average losses. As was shown in 2005, actual total

damage can be much higher. In addition, the estimate in Chapter 2 shows that in the

future much higher levels of damage must be reckoned with. Second, reinsurers use

only part of their capital for natural catastrophe risks. Some reinsurers avoid this

market altogether.

Third, in the case of “low-frequency – high severity” risks, reinsurers face funda-

mentally the same problems as primary insurers. For example, they are confronted

with similar uncertainties with regard to projected damage. In such a situation,

much depends on the building up of trust in insurance markets. Stable, persisting

business relations between primary insurers and reinsurers depend on a common

consensus that losses long-term need to be spread between them. If such a relation-

ship exists, then neither side need fear any short-term duplicity. A failure of the mar-

ket through uncertainty in the event of catastrophe damage would be highly unlike-

ly, if not eliminated.

In this relationship between primary insurers and reinsurers, it seems as if the

issue of a fair division of burden pales into insignificance. Business relations are be-

coming less stable and clearly increasingly decided through short-term yield consid-

erations. This is a trend that undermines the capability of the sector to cover extreme

risks. The reinsuring capacity in catastrophe risk depends not only on capital avail-

ability but also on the nature of the business relationship between primary insurer

and reinsurer34.

The above three problem areas describe the specific difficulties involved with cat

risk. By comparison, the moral risk of catastrophe insurance plays no major role. In-

surers have learned to cope with such mistaken incentives. Fund retention, maximum

coverage levels and other contractual regulations such as adhering to regulations

and safety standards can here, as in other insurance fields, be deployed to mitigate

against such erroneous courses of action.

Another often mentioned reason for market failure is “adverse selection”, which

is the choice of bad risks. In the case of asymmetrically distributed information, the

insurer is unable to distinguish between “good” and “bad” risks. In such a situation,

there is the danger that people exposed to an over-average risk can insure themselves

29 It is assumed in the interest of simplicity that a

(fair) annual premium works out as the equiva-

lent of the projected value of an annual loss

30 See Litan (2006a, 2006b)

31 In the next section, further possibilities (capital

markets, state means) will be discussed

32 If an insurer possesses a degree of liquidity to

finance a catastrophe with an assumed ten-year

recurrence periodicity, then an investor could

take over the insurer in the first year, not renew

the insurance policies in the second year, and

use the capital elsewhere

33 See Cummins 2006: 347

34 See sigma 4/05


23

too cheaply. This comes at the expense of people exposed to below-average risk. As a

consequence the insurer is threatened with a concentration of bad risks. In connec-

tion with natural catastrophes, any information advantage can, however, assumed

to be on the side of the insurer. Adverse selection can be said to hardly play a role.

Overall, this is the picture: The market error we’ve been dealing with here is

directly related to the specific characteristics of natural catastrophes. The high level

of under insurance becomes comprehensible. On one side, many primary insurers,

because of the existent uncertainty in relation to probability of occurrence and level

of damage, are right from the outset opposed to entering the market. On the other,

premiums are high because of loadings (uncertainty, timing problem, and capital

costs) . And, just as in every other market, high prices dampen demand. The event-

driven cycle now makes sense. The intertemporal diversification of low-frequency –

high severity risks is created only with certain difficulties. Serious catastrophes there-

fore usually imply large unexpected losses. This forces the insurer to limit the offer

and demand higher premiums, or to abandon the market altogether (harder market).


24

Climate change has long been a reality. Over the next decades, natural catastrophes

will have an increasingly destructive effect, even if many details are still disputed35.

There can be no doubt that catastrophe risk will increase for millions of people. Which

leads to the question of how the efficiency of markets can be improved to better cope

with these risks.

5.1 Extending the insurability of Cat Risk

Private insurers are working at extending insurability borders and thus opening

up new avenues for insuring weather-related catastrophes. Two developments in

particular should be mentioned: the construction of catastrophe models and the use

of capital markets for risk diversification.

As we have seen, weather-related natural catastrophes embody an extreme chal-

lenge for the insurer. The potential damage is very high, and the nature of the event

itself unpredictable. Once, insurers relied mainly on figures derived from experience

to determine projected levels of losses. That changed with Hurricane Andrew. This

caused levels of damage (see Table 1) that, at the time, were not considered possible.

Since then, work on so-called catastrophe models has been intensified. Apart from

the large insurance companies, special firms such as Risk Management Solutions

(Newark, Calif.), Applied Insurance Research (Boston, Mass.), and Eqecat (Oakland,

Calif.) are also involved in this work. The aim is to improve the precision of project-

ing the probability of and the damage caused by certain catastrophes to improve

the insurability of these risks.

A catastrophe mode is based on four fundamentals, as shown in Chart 736.

5Path to the future

35 See for example the summary of discussion over

the causes of hurricanes in Kunreuther/Michel-

Kerja (2007): 13

36 See Kunreuther/Michel-Kerja (2007)


25

To arrive at a precisely defined risk, parameters of all dimensions that exert a

substantial influence on damage are specified as exactly as possible and the rela-

tionship between them assessed in line with model requirements. As an example,

an insurer wants to know what levels of losses he faces if a hurricane hits a certain

region. Then factors that determine the character of the hurricane such as wind

strength and storm path are entered in the risk factors module. After that, the port-

folio is identified. This contains information such as insured property in geographi-

cally localized regions, together with characteristics such as type of construction,

number of floors, and age. Using this as a basis, the physical effects of the hurricane

are calculated. Then, the monetary loss is arrived at. A distinction is made between

direct losses and indirect losses such as those through production interruptions,

for example.

Such a model can be used for simulation. For example, for a strength-5 hurricane,

a large number (1,000 or if necessary, many more) of different potential routes

through a certain region can be calculated using a computer. The result is a pattern

of theoretical distribution of damage that gives the insurer an idea of the spectrum

of possible damage and its probability of occurrence. In an ideal case, the model is

sufficiently efficient (in the sense of being close to reality) for the insurer to use the

calculated fictional factors (probability of occurrence, damage) to calculate premi-

ums. This leads to a large range of catastrophe-risk coverage offers with attractive

conditions (reduced security loadings).

Catastrophe models serve in the first instance to reduce the inherent uncertainties

over cat risks. But diversification also comes into the picture. As has been shown, it is

not enough for reinsurance to provide for adequate intertemporal diversification of

major risks caused by natural forces.

It is appropriate to proceed with diversification over capital markets, because the

potential damage in relation to market capitalization is relatively small. Alone the

value of average fluctuation of all the net assets traded daily in the USA amounts to

133 billion US dollars37. That figure is significantly larger than the greatest case of in-

sured loss and is round about the level of total damage caused by Hurricane Katrina.

Larger insurers (and investment bankers) have therefore developed a number of

capital market instruments that enable intertemporal risk diversification. The most

important instruments today are formed by cat bonds38. Chart 8 illustrates typical

trading patterns for this class of asset.

37 See Swiss Re (2002)

38 Since December 1992, futures contracts (CAT

futures) have been traded on the Chicago Board

of Trade’s (CboT) catastrophe insurance indexes.

These also enable a transfer of CAT risks. But

these CAT futures or CAT options (introduced

on CboT in 1995) have so far have not been able

to establish themselves. Trading has been low,

for reasons principally to do with credit risks in-

volved. Their utilization possibilities are investi-

gated by Albrecht et al (1994) . For a brief over-

view of other instruments of risk transfer, see for

example, Krenn/Oschischnik (2003): 76

Chart 7

Structure of catastrophe models

Risk factors

Vulnerability(= Physical effects)

Damage(= Monetary loss

Portfolio


26

If an insurer wants to transfer the catastrophe risks of his portfolio to the capital

markets, he sets up a so-called Special Purpose Vehicle to issue the bond. The income

is invested as safely as possible in securities of the highest credit rating. At the same

time, the investment vehicle insures the insurer against a precisely defined catastro-

phe, for which an annual premium is paid.

In most cases, cat bonds are subject to variable interest rates. The coupon payment

emerges from the relevant money market interest (LIBOR) level plus an interest

loading . The investment vehicle finances these interest payments with earnings

from safe bonds (risk-free interest) and insurers’ premiums.

Embedded in the security is an option on the event. If an event does occur, then,

in the simplest case, the bond plus interest simply expires39. The vehicle immediate-

ly sells the safe bonds and pays the insurer the agreed coverage. If the event does not

occur, the investor gets back the nominal value of the catastrophe bonds at maturi-

ty. This, in turn, is financed through the sale of safe funds.

After payments have been completed, the investment vehicle is dissolved. Aside

from the legal and taxation advantages of this system, there is another important

advantage. Where the insurer himself is the issuer, investors would be affected by

any possible insolvency of the insurer, and would therefore need to be paid an equiv-

alent amount because of this additional credit risk. But this is here not the case be-

cause the vehicle and the funds it holds are not part of the insurer’s body of assets.

This is also an advantage for the investor because he is now offered the chance of

pure play in catastrophe risks40.

The most important cat bond parameter is the trigger event that causes the option. Thereare various types. Among them41: • Indemnity-based trigger. The trigger here is a certain loss by the insurer’s business.

In this case, there is no basic risk for the insurer. That means that the insurer is

completely safeguarded through the cat bonds because the loss equates precisely

with the agreed level of coverage. From the investors’ point of view this approach

has the disadvantage that the insurer under certain circumstances can influence

the trigger event to his own advantage. Equivalently large is the investors’ need for

information in relation to the insurer’s business policies.

39 A great number of variations is possible. The

coverage can be limited merely to future inter-

est payments and/or part of the nominal value.

Basically what counts is: the smaller the cover-

age, the better the investment vehicle rating

40 Strictly speaking this is not quite right because

he is still exposed to other risks, although these

are of a subordinate role: a money market risk

(LIBOR), a risk of liquidation (either no or a tight

secondary market) and possibly to a currency

risk

41 See Pensa (2004), 14

Chart 8

Cat Bond payment system

Insurer Investmentvehicle

Security withhighest credit

rating

Investors

Insurance premiums

Cover

Nominalvalue

Risk-freeinterest

One-time capitalpayment

Coupon payments


27

• A market-damage based index. The trigger here is a decline in an index that

reflects the exposure of a group of insurers. There is no chance of exercising any

influence. The basic danger can be considerable if the risks or their weight in the

portfolio vary greatly from those of the group.

• Model losses as trigger. This is where a catastrophe model offered by one of the

firms specializing in such models are used. In using the parameters of a real

occurring catastrophe, the calculation is made to assess whether the “model

damage” exceeds that of the contractually laid-down loss trigger or not. In such a

case, the basic risk is small (provided, as pointed out above, the portfolio of the

insurer is adequately taken into account). For investors, this trigger is usually the

least transparent.

• Physical trigger. The occurrence of a catastrophe is tied to certain objective

parameter values. For example, a hurricane must reach speeds of at least 200

km/h to qualify as a catastrophe. The approach is interesting for both investors

and rating agencies because it is extremely transparent. On the other hand, the

basic risk can be high.

• Parametric indexes. The majority of today’s cat bonds are based on this trigger

type, which is a further development of the physical trigger (second generation).

Objective measurable factors relating to the exposure of insurance in certain

regions are summarized in an index . When a catastrophe occurs, the level of cov-

erage is determined by a stipulated formula (Box 3). In an ideal case, the insurer’s

loss is adequately approximated (small basis risk) while the investors need for

transparency is taken into account.

As can be seen, the various loss triggers are viewed differently by insurers and in-

vestors. As a rule, there is a discord between, on the one hand, the basic risk of the

insurer and, on the other, the objectivity and transparence of the method.

On top of that, the characteristic of a catastrophe bond is naturally determined by

the size of the spread. This depends largely on the price of the option. Further risks

such as, for example, a minimal liquidity of a bond or the investment vehicle’s rat-

ing likewise influence the size of the interest loading.

Regrettably, no generally acceptable valuation formula exists for these catastro-

phe options. Options prices are independent of the dynamic of the so-called under-

lyings. In the case of share options, these are the movement of a relevant share.

Because the dynamics of catastrophe damage is considerably different from assets

traded on the finance markets, proved options price formulas (for example, that of

Black-Scholes) cannot be used. Instead, methods that are tailored to the relevant cat

bonds, such as simulation, are used.

This is however not without problems if the level of information of the involved parties

is either different or not high. Problems can emerge if:

• the insured damage is submitted only after a long delay

• the assets of the catastrophe model are not clearly clarified

• or the know how is concentrated one-sidedly with the insurer

Where is the attraction of cat bonds for the investor? A central role is played by port-

folio aspects. Cat bonds are not regarded in isolation but in relation to the portfolio

of a potential investor. As a rule, they have very advantageous correlation properties.

And even if it cannot be entirely ruled out that the occurrence of a natural catastrophe

will cause a price collapse on capital markets, this is fairly unlikely42. On the other

side, a crash of financial markets cannot cause a natural catastrophe. Cat bonds

show no (or an extremely small) correlation with financial issues already being

trade by the market. In the jargon of portfolio theory, the beta factor is zero (or very

small)43. Investors can successfully diversify their portfolio by acquiring cat bonds.

A further argument in favor of buying these securities is their attractive yield.

Between 2003 and 2006 the spreads of various cat securities amounted to between

42 The S&P 500 lost more than 12 percent in five

trading days after the reopening of the stock

exchange after the attack of September 11

43 In other words, they can drive their efficient

frontier upwards and achieve a high portfolio

yield with the risk remaining the same. Empirical

examinations confirm this. See Pensa (2004): 19

Source: Swiss Re Capital Markets


28

200 and 900 basic points44. This means that catastrophe securities yields are among

the highest. However, in the case of long-term yields, much depends on whether a

large catastrophe occurs. The uncertainty of catastrophe predictions is reflected in

prognoses relating to catastrophe securities.

Box 3 contains a case study in which the first cat bond issued by Allianz SE is shown.

On April 10, 2007, Allianz Global Corporate & Specialty announcedthe successful offering of an innovative catastrophe security in anamount of 150 million US dollars. The cat bond provides protectionagainst high severity losses incurred from earthquakes in Canada orthe United States, not including California, and river floods in GreatBritain. The multi-peril issuance is part of a one billion dollar programby Blue Wings Ltd, a special purpose vehicle launched by AllianzGlobal Corporate & Specialty. The emission was fronted by Swiss Rewhich also acted as arranger and lead manager. The bonds offer areturn of 3.15 percent over LIBOR and have a Standard & Poor’sBB+ rating, and the insurance risk, modeled by Risk ManagementSolutions (MRS) is 0.54 percent per annum. This means that in-vestors must reckon with an annual loss of 0.54 percent of theirinvested capital. But in reality there is a high probability that insidethe maturity period of six years no triggering event (catastrophe)occurs, for which capital from the bond would have to be used. Asa result, the chances of a worst-case scenario in which the invest-ment is totally lost is small. The cover is fully collateralized withminimal credit exposure.

Earthquakes in Canada and the United States – except California –and severe river flooding in Britain were chosen because they represent high risks that are insufficiently covered by existing rein-surance. The trigger for earthquakes is based on modeled lossesusing data from the United States Geological Survey. The modeledinsurance risk from earthquakes was 0.43 percent. The trigger forsevere river flooding is based on a parametric index of the secondgeneration. The modeled insurance risk by river flooding in thisregion was 0.11 percent (a total of 0.54 percent).The parametricindex trigger for river flooding is the most innovative part of theproject. It enabled for the first time an insurer to issue a cat bond

in which the expectations of both the sponsor (insurer) and theinvestor were fulfilled. A sponsor doesn’t want to concede theinvestor a too-detailed insight into the state of assets, risk manage-ment, underwriting or claims settlements, as is necessary in thecase of indemnity based trigger. This is why a parametric triggerwas selected. The challenge here was to construct an index thatwas robust and transparent enough to satisfy both investors andrating agencies and, at the same time, to keep within limits thebasic risk for the sponsor, Allianz.

The architects of the bond drew up what might be called a “vir-tual infrastructure”. This, administered by the Halcrow Group, aBritish firm of consulting engineers, enables the parties involved tomeasure with a high degree of precision the flood levels in morethan 50 places throughout Britain (the system is illustrated in thechart). The measurements are fed into the index formula, whichdetermines if and to what extent the investors lose their investedcapital.

Likewise new was the readiness of investors to invest in a catbond, although because of the new infrastructure it was not possi-ble to ascertain retroactively the performance of the trigger (highwater index) for any previous periods. Instead, the investors reliedon an estimated distributive curve within the index that was estab-lished by Risk Management Solutions (RMS) from its still unsur-passed flood model for Britain.

The bond found a large resonance in the market. Demand washeavy. It is the aim of Allianz Global Corporate & Specialty as wellas the Allianz Group to strengthen their competence in this fieldand make available such products to customers both medium andlong term.

Box 3

Marc Hannebert, head of reinsurance strategy at Allianz Global Corporate & Specialty AG

The new Allianz Cat Bond

Vertical distance – reference point flood depthFor each reference location specified in a measurement notice, the vertical dis-

tance (v) is the vertical distance (measured by the measurement agent within

the relevant measurement window) between each relevant reference point

and the corresponding watermark.

For each reference point satisfying the measurement conditions, the reference

point flood depth (d) is calculated with the formula: d = r – v

Where: r is the difference between the elevation of the reference point and the

ground elevation of the corresponding reference location (as measured during

the establishment of the reference point and listed in a schedule), v is the vertical

distance of the respective reference point

rv

d (calculated)

Pre-event Post-event

Reference point

Ground elevationof referencelocation

Flood watermark

Reference point

}

44 See sigma 2/7: 9


29

The next chart shows how the market of Cat Bonds developed until March 2005.

Annual issuing totals of a maximum of 4.7 billion US dollars and in total a volume

of about 15 billion US dollars of issued securities are fairly modest levels45. However,

the trend of emitted cat bonds is showing in an upward direction. Yet, as of today, the

market for cat bonds could not meet all expectations. One reason might be the prob-

lem of valuation. Expected advances in the modeling of catastrophe risks will bring

with them adjustments. Also, some insurers regard the bonds as somewhat expen-

sive. It has been shown that, so far, cat bond spreads lie clearly above reinsurance

premiums.

Another possible explanation is the high transaction costs, because most of the

time a large number of external experts such as investment bankers, lawyers, spe-

cialist firms, and actuaries are needed. More explanations indicate a low level of

liquidity, a shortage of experience with these securities, and a lack of interest by

investors. In the event, insurance companies in 1999 turned up on a large scale as

buyers of cat bonds (primary insurers with a market share of 30 percent, and rein-

surers with one of 25 percent). So insurers dominated both side of the market, sup-

ply and demand. But by 2004 this had fundamentally changed. Only seven percent

of bond underwriters were now from the insurance industry. The interest among

hedge funds, specialist cat bonds, and other institutional investors had also in-

creased significantly.

A major part of the problem seemed to be traceable back to typical teething

troubles. The potential of the financial markets in relation to the intertemporal

diversification of cat risks is in any case enormous. Now, it is a matter of improving

on this potential. Insurers can also contribute to this by , for example, make cat

bonds contractually more attractive46. The ability of insurance markets to function

is not least dependent on the efficiency of capital markets.

45 The first large wave of issues was in 1997. The

current annual volume of issues is a good three

billion US dollars

46 Froot (1998) names the following elements that

positively influence the success chances of a

cat-bond issue: a higher excess, a larger volume

because of the high fixed costs, transparent

triggers, and marginal incomes that are not too

high. Implicit in the last is a low probability of

loss and an equivalently low bond rating that

does not justify the issuing price

Source: Marsh & McLen-

non Securities (2006):

The Catastrophe Bond

Market at Yearend 2005

Chart 9

Cat Bond issues (1997-2006)

Cat Bonds in $ bn US

Volume of Cat Bonds in $ bn US

Number of emissions

Number of emissions

5

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

25

20

15

10

5

0

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006


30

5.2 Rethinking the role of the state

Inadequately functioning insurance markets in relation to natural catastrophes

serves to put politics under heavy public pressure to become engaged in the field.

When a catastrophe does strike and cause damage, as a rule the state steps in and

makes available a wide range of resources. This ex-post help, while understandable,

is not without problems.

For a start, it contains considerable fiscal/political risks. In the case of Katrina,

assistance from the US federal government in the form of expenditure and tax relief

amounted to more than 100 billion US dollars. A major part went on compensation

for storm damage that was not covered by insurance, although that would have been

possible47.

Second, state assistance is unfair, inasmuch as it comes from general taxation and

is thus financed by the entire population. It does not make sense for people who vol-

untarily live in an endangered area to be supported by the rest of the population.

Third, this form of aid is inefficient, because it creates a false set of incentives. It

leads to a variation of the “samaritan dilemma”: state support after a disaster under-

mines the motivation of people who are potentially affected to carry out risk-reducing

measures in advance of a possible catastrophe. This low level of insurance penetra-

tion in expectation of assistance in turn simply increases the pressure on politics to

extend assistance. This threatens to become an ever stronger self-propelling cycle.

A higher level of penetration for cat risks is therefore lies in the state’s own inter-

ests. This is hardly a new phenomenon. Many countries run programs to guarantee

the availability of cat insurance in cases where the private sector offers either inade-

quate coverage or no coverage at all48. Although these programs show significantly

differing financing structures and means of operating, most do pursue similar

aims49.

• Extension of the insurance protection on offer, or even creating an equivalent

market if no private coverage is available.

• Limitation of household risks to natural catastrophes.

Just as in the private sector, a state insurer can also function basically as either a pri-

mary insurer or a reinsurer. A well-known example of a public primary insurer is the

Federal Flood Insurance Program (NFIP) in the USA50. Insurance protection against

flooding was for the first time offered by private firms in the late 18th century. But

heavy losses caused by floods in the Mississippi in 1927 prompted most insurers to

pull out of this market. Over the next forty years, insurance coverage against flood-

ing was limited to a few private firms. The risks, because of the concentration in spe-

cific regions, and because of the sheer amount of potential damage, made this mar-

ket appear to be uninsurable.

In 1968, Congress approved the National Flood Insurance Program (NFIP) . At the

moment, the federal government is the primary insurer of coverage against damage

to private houses and small business caused by floods. Private insurers market the

NFIP and administer claims settlements. Participating communities in endangered

regions are under obligation to ensure that building plans and safety standards

include factors to mitigate against flood damage. It is also envisaged that the NFIP

will finance itself so that, in the long term, no taxation funds will be needed.

In August 2006, there were almost 4.6 million insurance policy holders in 20,000

communities in the USA. In the period from 1968 until August 2006, the NFIP paid

out 14.6 billion US dollars in compensation for insured damage. At first glance, it

seems that the NFIP has been a success. It has meant that the range of insurance on

offer has been significantly extended.

47 Protection against flood damage was offered

by the National Flood Insurance Program. The

insurance penetration among house owners in

Louisiana communities affected by Katrina var-

ied between 57.7 percent (St. Bernard Parish)

and 7.3 percent (Tangipahoa Parish). In Orleans

Parish, 40 percent were insured. See Kunreuther

(2005): 3/4

48 A special role played here in recent times is ter-

rorism. Since the September 11 attacks, these

risks also count among major cat risks. State

involvement appears both necessary and sensi-

ble. First, these risks are not exclusively privately

insurable. Second, there is here a general overall

interest by society that justifies the use of tax

resources. Coverage of terrorist risks often takes

place in the form of partnerships between private

enterprise and public authorities. See attachment

in sigma 4/2005

49 For an overview, see OECD (2005)

50 See Cummins (2006): 357


31

But the truth is that reforms are urgently needed. Premiums are not appropriate

to the risks and coverage is as low as 35-40 percent of expected damage. After the

year of record damage in 2004/5 (Table 1) the NFIP is practically insolvent. About 25-

30 percent of the compensated damage claims fall into the category of “repetitive-

loss properties”, properties for which compensation claims have been settled sever-

al times over. This leads to the conclusion that the incentive to take damage-preven-

tion measures that is meant to be in place simply does not exist. Finally, insurance

penetration is inadequate. Even in the most endangered regions, only 50 percent of

house owners, at best, are insured.

The faults of the NFIP are not isolated. State primary insurance programs are

often trapped in various forms of inefficiency. Subsidized policies can “crowd out”

private insurers or inhibit their market penetration. Public programs are exposed to

massive lobby interests. Subsidized premiums create “moral hazard” problems.

High administration costs (public servants placed in privileged positions), lack of

know how, and the lax handling of tax money can cause further problems.

The deficits associated with state-run primary insurance programs can generally

avoided if the following principles are observed51.

1. Private insurers should as far as possible be involved in the state program. State

insurance programs should back the private market or supplement it, but not

replace it.

2. If state programs are needed, their design should, as far as possible, imitate the

proven structure of private markets.

But this results in a paradoxical situation: if an efficient state primary insurance

program against catastrophe risks should imitate as far as possible private market

structures, why have a public program at all? Clearly it would make more sense if

the state in the first place took the trouble to create the basic conditions to enable a

free-market coverage of catastrophe risks. What is needed therefore is not so much

state primary insurance but, instead, appropriate cooperation between private in-

surers and public authorities. The example of Britain shows that flood risks are in

fact insurable on the free market.

51 See Jaffee/Russell (2006)


32

5.3 Private and public risk partnerships: the example of flood insurance

Although it cannot be expected that every risk is insurable at any given time, a

risk partnership between the private insurance business and public authorities

can decisively improve the insurability of these risks. Here, two approaches are rele-

vant to the issue of large risks.

The first approach involving cooperation between private insurers and public

authorities is based on the obvious fact that public policies can exert considerable

influence over the effects of natural catastrophes. A prime example is the case of

flood risks in Britain52.

More than two million houses, or 10 percent of the total, are presently exposed

to the risk of flooding. Of those, 400,000 are in highly endangered regions where the

annual occurrence risk of flood damage is 1.3 percent. Long-term, if suitable counter

measures are not taken, the situation could significantly worsen because of the effects

of climate change. This means that the number of buildings vulnerable to risk threat-

ens to rise to 3.5 million.

After widespread floods in the autumn of 2000 caused by record levels of rainfall,

doubts arose about whether this sort of damage would even be insurable on private

insurance market53. All parties agreed that public infrastructure needed to be funda-

mentally modernized to minimize flood damage. After constructive dialogue be-

tween the government and the Association of British Insurers (ABI)54 it was agreed in

January 2003 that private insurers would continue to offer insurance protection

against flood damage while the government would make available more funds to

improve mitigation efforts in endangered areas and improve its flood risk informa-

tion practices55.

In November 2005, the partnership was extended. Insurance cover would contin-

ue to be offered to private house owners and small businesses for whom the proba-

bility of flood damage was no higher than 1.3 percent. Premiums were to be calculat-

ed on the basis of the extent of the risk. On top of that, insurance cover remained

available for another 100,000 buildings that within the next five years would be, with

state assistance, brought into the 1.3 percent category. Existing policyholders in

highly endangered regions for whom no risk-mitigation program exists, were to be

dealt with on a case-by-case basis.

Private insurers agreed to take on these obligations on the condition that the governmentmade progress in five key areas:• reducing risks over the next three years for 100,000 buildings in endangered areas.

• Establishing adequate investment programs against flood damage, taking into

account climate change.

• Improving land-use planning to restrict construction of new buildings in risky

regions.

• Making available more accurate state information to the public about flood risks

and state programs to mitigate these risks.

• Taking steps to minimize risks posed by drain flooding and flash floods.

The British example shows that, even under difficult conditions, the insurability of

cat risks can be warranted. But this is only one, individual, example. The range of

possible cooperation is large and full of possibilities. Areas of cooperation include

safety standards, construction regulations, area planning, public infrastructure

(such as dams and other protective installations), deciding on appropriate tax con-

ditions, and catastrophe protection measures. Public and private risk partnerships

can help make those effects of climate change that cannot be avoided at least man-

ageable from a social point of view56.

52 See the web site of the Association of British

Insurers (ABI): www.abi.org.uk/flooding

53 The insured damage amounted to more than

one billion pounds sterling

54 More than 400 insurance companies, or 97

percent of the British insurance industry, is

organized under the ABI umbrella

55 The funds were initially increased to 394 million

pounds sterling (2002/3) and then, in stages,

to 564 million pounds by 2005/6

56 Of course policyholders can also contribute by

not taking unnecessary risks and by making

available information that is relevant for insur-

ance purposes


33

An important condition for the insurability of major risks is the possibility of

diversification. But in many markets, as we have seen, the opportunities for risk

transfer using reinsurance are limited and the relevant capital-market instruments

under-developed. This is where the second approach comes into play. The insurabil-

ity of risks to natural catastrophes can be improved if state reinsurance exists57.

Such programs can be arranged in many ways. Of course, the previously men-

tioned construction principles should be used. Specifically these involve observing

the following principles.

First: State reinsurance should only be available for cases of major damage that ex-

ceed a certain level. Where this level is set depends on the relevant market conditions.

Out of this emerges a system of multi-layered liability, as is typical in insurance

markets. The insured damage is spread between policy holder (through an excess

requirement) primary insurers, private reinsurers and capital markets, and, finally,

public authorities.

Second: the private insurance business should always remain involved in cases of

major damage. This, inasmuch as state coverage, by remaining flexible and adjusted

to the relevant market, is limited. Alternatively, a proportional division of coverage –

above that “certain level” – between insurers and state can be agreed on. In this way,

there is an overview of household risks. At the same time, private primary insurers

and reinsurers remain interested in using their own wherewithal to improve the

insurability of risks to natural forces.

Third: Such a program needs to be financed by premiums that are set at levels

appropriate to risk. This is to avoid false incentives being inferred from public rein-

surance. If premiums are too low, the danger is that private insurers will be crowded

out of the market. In addition, the opportunity increases for moral-hazard behavior

by both primary insurers and policy holders. If premiums are too high, there will be

no demand for state reinsurance and they will the program will thus not fulfill its

aim.

If, because of public pressure and political pressure, setting an appropriate premium

level becomes either difficult or impossible, a price can be set at more-or-less mar-

ket conditions with the help of regular auctions. This means that the state auctions

off a limited number of reinsurance contracts at annual auctions where a reserve

price is set at a level that reflects the risk58. Buyers would, in the main, be primary

insurers but, in certain circumstances, other investors as well. Such speculative pur-

chases would be welcome because they would increase liquidity and make it easier

to set prices more efficiently.

Such a system of state reinsurance has several attractive facets. For a start, it gives

the impression of extending the market without actually entering the private insur-

ance market. It also reduces the timing risk and provides for a commercial environ-

ment which allows the primary insurer to insure cat risks at more attractive condi-

tions. At the same time, it allows policies to remain in force that were completed

with private insurers under conditions containing incentives for policyholders to act

appropriately to the risks. Policyholder would need to continue to accept excess pro-

visions if they are to obtain coverage from a primary insurer. The state benefits from

such a program as its fiscal risks are reduced. But all of society also gains. Having a

more efficient way of dealing with the risks of natural catastrophes is to the advan-

tage of everyone.

57 Such programs already exist in one form or

another. Prominent examples include the Japan-

ese Earthquake Reinsurance Company and the

French Programm Caisse Centrale de Reassur-

ance (CCR). See Cummins (2006)

58 The Clinton administration intended introducing

such a system. See Litan (2006b)


34

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hedging climate change

Education