The Role of the Insurance Sector. 3rd Meeting of the High Level Expert Panel on Water and Disasters. Water Disasters

(1)

3rd Meeting of the

High Level Expert Panel on Water and Disasters

Marseille, June 9

-

10, 2008

Water Disasters

The Role of the Insurance Sector

Wolfgang Kron

Geo Risks Research

Munich Reinsurance Company

High Level Expert Panel on Water and Disaster

High Level Expert Panel on Water and Disaster Marseille, June 9Marseille, June 9--10, 200810, 2008 Wolfgang Kron:

Wolfgang Kron: Water Disasters Water Disasters ––The Role of the Insurance Sector The Role of the Insurance Sector

Themes

Recent Disasters and Loss Statistics

Reasons for Increasing Losses

Risk Spreading and Risk Partnership

Insurance Aspects

4 Annexes:

A: PML assessment and cat modeling

B: Obligatory insurance

C: Cat bonds

(2)

Cyclone Sidr Bangladesh

2007

economic: 3,375 mio US$

insured: ≈

0 deaths:

3,363

Recent Disasters and Loss Statistics

economic: 10,000 mio US$

insured: 1,000 mio US$

deaths: 220,000

Floods Sudan

2007

economic: 300 mio US$

insured: ≈ 0

deaths:

> 150

Cyclone Nargis Birma

2008

economic: ??

insured: ≈

0 deaths: > 100,000

Focus: Humanitarian Aspect

Tsunami Indian Ocean

2004

Focus: Monetarian Aspect

United Kingdom

2008

economic: 8,000 mio US$

insured: 6,000 mio US$

deaths:

5 Oman / Cyclone Gonu

2007

economic: 3,900 mio US$

insured: 650 mio US$

deaths:

7 Central Europe

2002

economic: 18,000 mio US$

insured: 3,400 mio US$

deaths:

> 100

USA / Hurricane Katrina

2005

economic: 125,000 mio US$

insured: 61,600 mio US$

deaths:

> 1322

(3)

Principal causes of flooding

Æ

important for insurance considerations

Storm surge

Cause: high water level

due to wind setup

(plus astronomical

high tide and waves,

no effects by rainfall)

Exposed: coastal plains (of

seas, bays, lakes)

Losses: low frequency

(dykes), very high

potential

Flash flood

Cause: locally heavy rainfall

Exposed: any location

Losses: high frequency,

usually relatively

small, high

destruction potential

River flood

Cause: long lasting,

wide-spread rainfall with

great depth;

inundation starts

from the river

Exposed: flood plains (always

the same areas)

Losses: low frequency

(dykes), high

potential

losses in m US$: total insured [% ins]

2000

Japan: Typhoon Saomai

1,400

1,050

75 2002

China (Yangtze)

8,200

<1

2003

China (Yangtze, Huai)

7,890

<1

2004

China (Yangtze, Yellow, Huai)

7,800

<1

2004

India, Bangladesh, Nepal

5,000

<1

2005

China (Pearl)

5,000

<1

2005

India (Mumbai)

5,000

770

15 2006

India (Gujarat, Orissa)

5,300

400

8 2007

Indonesia (Jakarta)

1,700

410

24 2007

Tajikistan

1,000

<1

2007

India

2,600

<1

2007

Oman: Tropical Cyclone Gonu

3,900

650

17 2007

China (Huai)

6,800

<1

2007

Pakistan: Tropical Cyclone Yemyin

990 <1

2007

Bangladesh: Tropical Cyclone Sidr

*

3,775

<1

2000

Italy (north), Switzerland (south)

8,500

470

6 2000

United Kingdom

1,500

1,100

73 2002

Central Europe (Elbe, Danube)

21,500

3,400

16 2003

France (Rhone)

1,600

900

56 2005

Romania, Bulgaria

2,440

15 <1

2005

Switzerland, Austria, Germany (Bavaria)

3,300

1,760

53 2007

United Kingdom

8,000

6,000

75 2001

USA: Tropical Storm Allison (Houston,TX)

6,000

3,500

58 2001

Argentina

750 <1

2005

Canada (Alberta)

860

190

22 2005

USA: Hurricane Katrina (Gulf Coast)

* 125,000

61,600

49 2007

Mexico (Tabasco)

2,500

350

14 2007

Australia (East Coast)

*

1,300

680

52 2008

Australia (Queensland)

?

2,000

1,600

>80

2000

Mozambique, Zimbabwe, South Africa

715

50

7 2007

Madagascar

*

240 <1

2007

Sudan

300 <1

The costliest

floods in the

21st century

(original values

in US$ million, not

adjusted for

inflation)

* including

wind-storm losses)

(4)

Flood

Geological events

Weather related events

Temperature extremes Windstorm dry

Earthquake, tsunami, volcanic eruption

Wet-storm

Great Natural Disasters 1950 – 2007

Percentage distribution worldwide

* 2007 values

Overall losses: US$ 1,770bn*

Number of events: 283

28% 27% 26% 5% 14%

Deaths: 1.8 Million

55% 2% 34% 7% 2% 31% 24% 24% 6% 15%

Insured losses: US$ 370bn*

10%

55% 5% 4%

26%

tsunami 13 %

Great

Natural

Disasters 1950 – 2007

0

20

40

60

80

100

120

140

160

180 1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

US$ bn.

Trend total losses

Total losses (in 2007 values)

Insured losses (in 2007 values)

Trend insured losses

(5)

Great

Flood

Disasters 1950 – 2007

US$ bn. _{US$ bn.} 0 10 20 30 40 50 60 70 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Trend total losses

Total losses (in 2007 values)

Insured losses (in 2007 values)

Trend insured losses

Note: Storm surges are not considered “Flood disasters”, but rather belong to “Windstorm disasters”. In the same way tsunamis belong to “Earthquake disasters”

Great Natural Disasters

The affected region's ability to help itself is distinctly overtaxed.

- Interregional or international assistance is necessary

- Thousands are killed

- Hundreds of thousands are made homeless

- Substantial economic losses

- Considerable insured losses

Why only “great natural disasters“ ?

. . . because neglecting the development in worldwide communication

activity during the past decades extremely biases the statistics of

ALL loss events. It may even lead to wrong trends.

(6)

Total losses [US$ bn]

0

5

10

15

20

25

30 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Total losses [US$ bn]

0

5

10

15

20

25

30 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Flood losses in Europe

–––

10-year running mean

Insured losses [US$ bn]

0

1

2

3

4

5

6

7 1980

1982 1984 1986

1988 1990

1992 1994

1996 1998 2000

2002 2004

2006

Flood losses in Europe

Insured losses [US$ bn]

0

1

2

3

4

5

6

7 1980 1982

1984 1986

1988 1990 1992

1994 1996 1998

2000 2002 2004

2006

(7)

Increase in the World’s Population

Value concentration in urban agglomerations/

megacities

Urban Population 1950: 30% - 2005: 50% - 2030: 60%)

Value concentration along coasts

2/3 of the world‘s population live within 200 km of the coast

1/3 within 50 km; 1/10 within 5 km

Risk awareness and risk perception

(false) feeling of safety behind a dyke

Climate change

Reasons for increasing losses

Number of loss events 1980 – 2006

Floods

Windstorms

0

50

100

150

200

250

300

350

19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06

Earthquakes

(8)

Risk =

Hazard

Values at risk

Vulnerability

f (

)

What is Risk?

Risk spreading and risk partnership

Insurance reduces the financial vulnerability of the insureds.

Public authorities

(state, community, NGOs)

Finance industry

(insurance and capital market)

People concerned

(private persons, companies)

Partnership

requires

(9)

Public authorities/organisations

basic prevention measures Æ - avoiding frequent losses

- mitigation during rare events

Finance/insurance industry

securing existence

Æ - prevention of ruinous

consequences for personal/

business property

People concerned/affected

actions during rare events Æ - loss reduction/limitation

Main tasks of the partners

Risk partnership

Public authorities

Æ

basic prevention measures

Main tasks of the partners

-

land-use regulations

-

ensure they are observed

-

technical flood control

-

observation networks

-

forecasting and warning

-

flood retention

(10)

People affected

Æ

loss prevention/reduction/limitation

-

proper construction

-

spot protection

-

appropriate behaviour

(alarm plan, checklist)

-

seeking/receiving information

-

maintaining risk awareness

Main tasks of the partners

Insurance

industry

Æ

securing existence

- assuming part of the risk

- proper risk assessment

- adequate contracts

- providing information

- accumulation control

- formation of natural perils reserves

- capacity through balance of risks

Main tasks of the partners

(11)

Re-insurers

Insurance clients

The international risk spreading system

Primary

insurers

Retrocession

Capital market

Example of reinsured losses

Hurricane Gilbert (Jamaica 1988)

economic loss:

2 000 mio US$

insured loss:

700 mio US$

typical percentages of reinsured losses: weak markets:

>90%

strong markets

<50%

(12)

… in a more holistic way . . .

Insureds

Insurers

State

Reinsurers

Capital market,

Banks

International donors,

Development banks

Risk partnership

Reinsurers are more than just a source of insurance payments.

We share our expertise in

- hazard identification

- risk assessment

- PML assessment

(Probable Maximum Loss)

Annex A

- risk reduction and accumulation control

- insurance solutions

for example:

Obligatory insurance

Annex B

Micro-insurance

Annex C

Cat bonds

Annex D

(13)

Topics Geo - Annual review of natural catastrophes

First edition in 1994

- German

- Englisch

- French

- Spanish

- Italian

Available in 5 languages :

For many other pulications on natural

hazards, natural dissasters and related

topics, please go to

http://www.munichre.com

Æ TOPICS and SOLUTIONS Æ Georisks

1998

China

4,160

30,700

1,000

(3%)

1996

China

3,048

24,000

445 (2%)

1993

United States

48 21,000

1,270

(6%)

2002

Europe

37 21,000

3,400

(16%)

1995

North Korea

68 15,000

(<1%)

1991

China

2,628

13,600

410 (3%)

1993

China

3,300

11,000

(<1%)

1994

Italy

68 9,300

65 (1%)

1993

Asia

2,953

8,500

(<1%)

2000

Italy, Switzerland

38 8,500

470 (6%)

2007

United Kingdom 9

8,000

6,000

(75%)

Year Country/Region Fatalities

Economic

Insured

losses

(mio US$)

Costliest inland flood events since 1990

Flood insurance aspects

(14)

Shares of insured losses are low

because

- insurance penetration is low due to

lack of risk awareness

- in some countries people cannot

afford insurance

- damage to infrastructure (dykes,

bridges, roads etc.) is usually

not insured

Calculation of the premium for a single risk:

Annual premium

=

Average annual loss expectation

=

Risk

=

Hazard

x

Exposed values

x

Vulnerability

probability

values exposed to

expected loss

of the event

the natural event

(percent of value)

Flood insurance

(15)

Approaches to a solution

-

information about the individual exposure

-

definition of zones according to exposure level

(country-wide for all areas)

-

exclusion of particularly exposed areas

-

adequately structured insurance contracts

including deductibles and limits

-

encouragement of measures for individual loss

reduction

-

insurance package including coverage for other

natural hazards (geographical spread of risk)

Flood insurance

Hazard zonation

countrywide available

(insurance industry)

- Germany

- Austria

- Italy

Æ

EU flood directive

requires members

of EU to develop

hazard & risk maps

Æ

Munich Re has

developed simplistic

flood hazard maps

worldwide

(16)

GK1

GK2

GK3

GK4

Flood insurance

GK 4, high hazard:

Flooded at least once in 10 years

*

GK 3, medium hazard:

Flooded at least once in 10 to 50 years

*

GK 2, low hazard

Flooded at least once in 50 to 200 years*

(

_{≈ area protected by dykes)}

GK 1, very low hazard:

Flooded less than once in 200 years*

* statistical mean

Hazard zonation:

Flood risk classes in the German system ZÜRS

A

Only those, who subjectively feel threatened by a

flood, have interest in insurance cover;

a large portion of them is in fact exposed

to a high risk and experiences losses more

or less regularly.

B

The others feel safe and do not want to get insured.

If the portfolio mainly consists of members of group A

, the spatial

and temporal risk compensation is not guaranteed anymore.

sum of premiums

sum of payments

from all clients

to the affected clients

( + yields)

( + administrative costs + profits )

=

Principle of the insurance

Adverse selection

(17)

-

The insured bears a part of the loss, e.g.

- a fixed amount

- a percentage of the loss

- a percentage of the sum insured

Deductibles

Flood insurance

⇒

Reduction of the number of losses by

⇒ deductible: 500 €

51%

Percentage of the

total number of losses

(b

uildings)

example: Windstorm “Lothar” 1999 - Germany

Deductibles

(18)

⇒

Reduction of the total loss amount by:

35%

deductible: 500 €

Flood insurance

Deductibles

example: Windstorm “Lothar” 1999 - Germany

Percentage of the

total losses amount

(b

uildings)

Effect of deductibles

- The insured pays a portion of the loss himself.

- The number of minor losses and the

administrative

costs

will be drastically

reduced

.

- The total effort of the insurers will be reduced.

-

Premiums

can be kept at a low level or be

reduced.

- Insureds are

motivated to loss reduction

measures in

order to keep their own losses low.

- The total loss/costs are reduced.

(19)

Examples:

East Asia:

STF (Storm-Tempest-Flood) covers

United Kingdom: All-Natural-Peril covers

France:

Catastrophe pool

“Full” natural peril insurance packages:

Flood insurance

typical example: „Extended Elementary Peril Insurance“ in Germany

Flood

is insured together with:

- Earthquake

- Subsidence

- Landslide

- Snow pressure

- Avalanche

- Volcanic eruption

not insured perils:

- Groundwater

- Storm surge

Flood insurance

other natural peril insurance packages

not included peril:

(20)

Storm surge

Risk

=

Annual loss expectation

=

Frequency x Loss

0 x

∞

Risk

=

very uncertain

result

Calculation of a risk-adequate

premium is almost impossible.

How much can a natural

catastrophe cost us?

(21)

For example,

a large-scale flood

event as in August

2002 in the Elbe and

Danube catchments

in Germany?

return period

For example,

(22)

return period

WANTED:

The

Probable

*

Maximum Losses

(PML)

that a portfolio, i.e.

a company may face

* “Probable” depends on the

company’s risk policy, but

also on legal requirements

(e.g. Solvency II)

… but they cost money.

Why?

Because they must be made available quickly after big

events and cannot be tied up in long-term, high-interest

investments.

This aspect also constitutes a portion of the premium

required.

For storm surges, this portion is (much) larger than the

portion related to the loss expectation of a single risk.

More details of

PML modeling

Æ

ANNEX A

(23)

- Losses from floods have increased and reached new dimensions.

- Public authorities, affected people and enterprises, and the

insurance industry must work together in a risk partnership.

- Insurers and reinsurers are capable of providing the financial support

needed to recover from disaster while, at the same time, pursuing

their business.

- The insurance industry can considerably contribute to risk reduction

and loss reduction.

- They must protect their own business by accumulation control.

- New instruments have been developed for insuring the poor and

for coping with extremely large single losses.

- It is of crucial importance to create – and constantly maintain –

proper risk awareness on all levels of the society.

Conclusions

Base:

R

isk

=

H

azard

x

E

xposed values

x

V

ulnerability

Aspects: Pricing

Capacity, accumulation control

- Types of flooding, frequency, time of occurrence

- Existing general preparedness measures: observation,

early warning, flood control systems etc.

- Land-use policy and its enforcement

- Availability of hazard maps

- Loss event experience

- Distribution of values (spatial, vertical)

- Site protection

- Individual preparedness level

- Construction type (material, number of stories etc.)

- Resiliency of exposed values to water

- Average loss rate, average loss

- Awareness and education level, familiarity with floods

H

azard

E

xposed values

V

ulnerability

„What does an insurance company focus on

(24)

ANNEXES

A: PML assessment and cat modeling

B: Obligatory insurance

C: Cat bonds

D: Micro-insurance

ANNEX A

PML Assessment and

Catastrophe Modeling

(25)

How much can a natural

catastrophe cost?

return period

WANTED:

The

Probable

Maximum Losses

(PML)

that a portfolio, i.e.

a company may face

(26)

To obtain (estimate) the loss of a single event, we have to combine:

+

vulnerability

loss ratio (in % of s.i.)

water depth

+

liability distribution

0 50100150200 kilometers

event scenario

Calculation of a PML curve

Values at risk Vulnerability

Hazard

Superposition of

flooded areas

with

built-up

areas

PML calculation

(27)

Spatial Analysis tool:

Geographical Information System (GIS)

Settlement area

Flooded area

Result: inundated settlement areas

Inundated settlement area

Loss Analysis:

per postcode-sector

A

_s

=

total settled area

A

_f

=

flooded part of settled area

SI

= sum insured

(28)

Situation during a River Flood

Damage ratio

1. Ratio of the number of

affected policies

and the

total number of policies in the hazard zone

Damage ratio

building not affected

building affected

(29)

Loss: L = SI x A

_f

/

A

_s

x R

_D

Loss Analysis:

per postcode-sector

R

_D

= damage ratio

A

_s

=

total settled area

A

_f

=

flooded part of settled area

SI

= sum insured

Result: probable losses per postcode sector

Summing up the losses

from each postcode sector

(separately for each line of business:

individual, commercial, industrial;

buildings, contents, business interruption)

Accumulation analysis

(30)

loss

1 2 3 4 5 6 . .

event

but as we have only a

VERY

limited number

of historic events,

we need to generate

more events

stochastically

from historic events, we have some loss experience

0

0 1500

3000

4500

6000

7500

9000

10500

12000

13500

15000

EventID

S

cha

d

en

loss

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

event

(31)

0

1

10

100 1000

10000

100000

WKP

Sc

h

ade

n

return period (years)

loss

1 3 2 4 5 6

empirical PML curve

How high is

200-year loss?

0

1

10

100 1000

10000

100000

WKP

Sc

h

ade

n

return period (years)

loss

1 3 2 4 5 6

empirical PML curve

What is the

return period

of a given

historic event?

(32)

ANNEX B

Obligatory flood insurance

-

Obligatory

flood insurance

required by the state for basic

needs, with limited cover (e.g. €100,000) plus

-

Voluntary

flood insurance for the

losses exceeding the limit offered

by the private insurance market.

Possible solution

(33)

Legal problems

- Freedom of contract

This kind of insurance is not for protecting a third party.

- Right of equality

There are different hazard levels.

- Scope of cover

Who/What must be included?

Buildings/Contents?

Private/Commercial/Industrial objects?

- Enforcement

What happens if someone is not willing to buy insurance?

Obligatory flood insurance

- Risk perception

Great catastrophes happen very seldom; why pay premiums

for something that may happen in one’s lifetime with a less

than 10% chance?

- Public acceptance

Many do not feel threatened; these people feel they have to

pay for the problems of others.

- Risk-adequate premiums and deductibles

Both need to be high; the consequence is that people pay a lot,

but do not get compensated for small and moderate losses.

- Moral hazard

Insureds tend to (ab)use the insurance, there is no or only a

reduced incentive for preparedness and prevention measures.

(34)

Incentives/enforcement

- Insurance package

Include several/all natural perils; including “storm” would also

prevent problems in distinguishing between wind and water

losses (e.g. Asia: STF cover).

- No compensation by the state without insurance

- No state loans without insurance protection

- No free loans for recovery without insurance protection

- Tax incentives

- Low-cost construction measures for upgrading

existing structures

(e.g. subsidies)

- Creating/raising risk awareness by information and

education

Obligatory flood insurance

Solutions in some selected countries

- Great Britain

all peril cover

- Germany

multiple peril cover

(as extended coverage)

- France, Spain

obligatory cover

- Switzerland

mixed system

- The Netherlands

exclusion

(35)

- (At least) high-frequency events must be subject to

risk-adequate insurance conditions.

- In a great catastrophe, accumulated losses can exceed

the capacity of the insurance market.

- Any type of obligatory insurance requires a guarantee

by the state, because losses can exceed any limit.

- Limiting the state guarantee is against the protection

goal and must be ruled out.

- The guarantee must cover all loss scenarios

(e.g. small- and large-scale).

Obligatory flood insurance

Principles (1)

- The obligatory insurance can be managed

- by the state

(e.g. USA, France)

or

- by the insurance industry

(e.g. California Earthquake

Agency - CEA)

;

- In any case it will be some kind of a pool/fund solution.

- The risk of the state’s guarantee must be seen in the

light of climate change.

- A fund must be built up for the catastrophe situation.

Obligatory flood insurance

(36)

Building up a fund

Example:

The Netherlands with 16,000,000 inhabitants

If

€ 1 per capita/month

were collected, the fund would

consist after

ten years

of approximately

Euro 2bn *

( €1 x 12 x 10 x 16,000,000 = €1.92 bn)

The probability that a

- 100-year event occurs in these ten years is 9.6%

- 500-year event occurs in these ten years is 2.0%

*

This assumes that no payments have to be made during this period, i.e.

only flood losses occur that can be handled by the insurance industry.

Revenues are also neglected

.

Obligatory flood insurance

Guarantee by the state

Spread of risk among

individuals and over

time

Minimization of

disruptive effect of

loss events

Minimization of loss

cost

Private insurance

National solidarity

Combination of both

Claims management

Liquidity for quick

rebuilding

Risk management

Claims management

Responsible behaviour

Legal framework

Interests:

Possible solutions:

Flood insurance: Summary

(37)

Pros:

Cons:

Pure solidarity solutions

Flood insurance: Summary

- Availability of cover can be guaranteed

- Affordability of cover through society-wide sharing of risk

- No immediate incentives for responsible behaviour and

risk management

(but can be introduced)

- Consequence: higher overall loss cost

- Fairness: What about “Right of compensation” in the

case of avoidable losses?

- Liquidity in the case of two-digit billion euro losses?

ANNEX C

Cat bonds

(38)

Cat bonds

We have

Alternative Risk Transfer (ART):

Catastrophe bonds (Cat bonds)

Æ

new financial instruments, which supplement the classical

distribution of risk via the reinsurance market.

Æ

geared to very large potential losses

Æ

exclusively used in developed countries

potential of record

losses from single

natural catastrophes

huge amounts of

money in the

international

financial markets

The investor buys a share of the cat bond.

If the catastrophic event for which the bond is issued does

- not occur:

the invested

capital plus high interest is paid back

to the investor at maturity.

- occur:

the investor

loses its principal

or a portion of it.

Cat bonds

How does it work?

A specified risk (e.g. losses from a hurricane in Florida) is transferred

from a

risk carrier (sponsor)

reinsurers, large companies such as a national

railroad company

to

investors

hedge funds, specialised catastrophe-oriented

funds, asset managers, life insurers, reinsurers,

banks, pension funds

(39)

Cat bonds

How is “occurrence” (triggering of the cat bond) defined?

- by a certain loss to the sponsor

(indemnity trigger)

- by a market loss

(industry loss index trigger)

- if a set of certain defined physical threshold values (e.g. wind

speeds at certain points or discharges) are exceeded

(parametric trigger)

.

Current situation:

- only one cat bond has been triggered so far (by hurricane Katrina),

- trigger points still quite high,

(Example: Munich Re issued a cat bond in 2007; it covers

covers: hurricane losses in 26 US states until 2010.

trigger: market loss (total insured losses) > US$ 35bn)

- increasing experience will probably reduce prices and enhance the

competitiveness of cat bonds,

- diversity of cat bonds in terms of expected loss levels, trigger

types, and underlying perils will grow.

(40)

Today, the availability of huge amounts of money in the international financial markets,

the potential of record losses from single natural catastrophes, and the need

for special products have led to the dawning of new financial instruments,

which supplement the classical distribution of risk via the reinsurance

market: these are called catastrophe bonds (cat bonds). Through a cat bond,

a specified risk (e.g. losses from a hurricane in Florida) is transferred from a

risk carrier (sponsor) to investors. The sponsor is usually a member of the

(re-)insurance industry, but may also be a large company (such as a national

railroad company). The investor buys a share of the bond and earns interest

on the purchased notes. If the catastrophic event for which the bond is

issued does not occur during its lifetime, the invested capital is paid back to

the investor at maturity. If it does occur, however, the investor loses its

principal or a portion of it. The definition of the "occurrence", i.e. the trigger

of the cat bond, can be defined in different ways: a) by a certain loss to the

sponsor (indemnity trigger), b) by a market loss (industry loss index trigger),

or c) if a set of certain defined physical threshold values (e.g. wind speeds at

certain points or discharges) are exceeded (parametric trigger). Munich Re

issued a bond in 2007 that covers hurricane losses in 26 US states until 2010.

The investors of that note will lose their invested capital if the market loss

(total insured losses) in any one event exceeds US$ 35bn.

Cat bonds (1)

Cat bonds are usually high-interest, but also high-risk investments and are practically

only purchased by professional investors. Investors choose to invest in

catastrophe bonds because their return is largely uncorrelated with the

return on other investments, so cat bonds help investors achieve

diversification. Investors who participate in this market include hedge funds

specialised catastrophe-oriented funds, asset managers, life insurers,

reinsurers, banks, and pension funds. Only one cat bond has been triggered

so far (by Hurricane Katrina), because the trigger points were set quite high

in the past. Over the coming years, increasing experience with cat bonds on

the part of both investors and sponsors could reduce prices and thereby

enhance the competitiveness of these financial instruments. Furthermore, it

is also possible that the diversity of cat bonds in terms of expected loss

levels, trigger types, and underlying perils will grow in the future.

Cat bonds are geared to very large potential losses and are therefore an instrument