Financing Catastrophic Risk:
Mortality Bond Case Study
Andrew LinfootRegional Director, Asia PacificAgenda• Convergence of Insurance and Capital Markets• An overview of major catastrophes• Case Study: Tartan Capital2

Convergence of Insurance and Capital Markets• What insurer ?
••••Distributes product, that is has
Manufactured, using its people, that relies on aPromise, that is supported by its
Capital.
Finance/treasurySales• How is this different to a bank ?
• Which areas get most of management’s attention ?
• Who are the real experts in managing capital ???

3

Development/Servicing
Advertising/BrandingConvergence of Insurance and Capital Markets• Should Capital be ‘Outsourced’ ?

4Liability Value

Capital Structure

Catastrophic Deviationfrom Economic Reserve

Debt Financing

Moderate Deviationfrom Economic Reserve

Mezzanine
Financing/Preferred
Equity Financing

Normal Deviation fromEconomic Reserve

Equity Financing(Economic Capital)

Economic Reserve

Self-Financing

Securitization Overview• Securitization has developed in recent years into an important tool for capital management byfinancial institutions• Originated by the banking industry in the 1970s and 1980s.
• In a typical securitization a financial institution sells assets with reasonably predictable cash
flows to a special purpose finance entity
 Assets include residential mortgages, auto loans and credit card receivables
 Assets must have relatively identifiable, though not necessarily low, default risk

• The finance entity then sells debt securities backed by the cash flows to the capital markets
 Single-class offering: All investors receive a pro rata interest in the incoming revenues from the assetpool Multi-class offering: Two or more classes or tranches are granted different (and in some casesuncertain) claims, each with its own pay-out and risk characteristics• The proceeds of the issuance are transferred to the financial institution to be used for capitalrelief or other purposes5Insurance Securitizations• Securitization offers insurers and reinsurers access to financing that will improve the insurer’s
capital or liquidity position at a cost that should be favorable in comparison with other sources• Insurance securitizations are similar to securitizations by other financial institutions in many
respects:
 Assets with predictable cash flows and default risk are transferred to a special purpose financial entity
 Insurance-linked securities (“ILS”) are sold to the capital markets based on those cash flows• Insurance securitizations differ from securitizations by other financial institutions in one
significant respect: securitizations can be used as another means of managing insurance risk.
• Capital markets take on risk of insurance losses in excess of expected losses--but limited to theinvestment in the ILS

6

Economic Benefits of Securitization• Provides lower cost financing• Reduces capital needs• Lowers overall cost of capital• Increases velocity of the balance sheet• An alternative to the traditional “buy and hold” strategy, using the insurer’s capital to backinsurance risk
• Securitizations permit insurers to redeploy capital in an efficient manner• Diversification of capital• Transfer of risk
• Securitizations typically use “bankruptcy remote” vehicles• ILS are typically non-recourse to the sponsoring insurer

7

All Life Insurance Securitization Transactions
EV Securitization
Excess Reserve Securitization
Life Settlements

Hannover Re VCommission Financing

Ballantyne Re

Orkney IIOrkney
FLAC Holdings LLC

Mortality / Longevity

Vita I
LILAC
Structures
Prudential Holdings LLC
Class B Stock

LegacyRiver Lake
Insurance Co.

Mutual Securitisation p.l.c.
Hannover Re III

River Lake II-ii
River Lake I-iii
Box Hill

River Lake I-ii

Golden Gate
River Lake II/Genworth

Hannover Re IV
Clark-Bardes

Hannover Re II

TartanFirst BritishAm/BannerVita II
Hannover Re I

1995

81996

Queensgate

BritishAm/Banner

America Skandia
Transactions
Prudential
Holdings LLC IHC Debt

1997

1998

1999

2000

Hannover ReVIALPSMONY Holdings LLCGracechurch LifeFinance PLC

River Lake III

Allstate
Timberlake

2001

2002

2003

2004

2005

2006

2007

Mortality Cat Bond Issuance••••

92003 Vita 1:
2005 Vita 2:
2006 Tartan:
2006 Osiris:

Swiss Re
Swiss Re
Scottish Re
Axa

$400M
$362M
$155M
$442M

World Natural Disasters
Natural Disasters with highest death toll since 1750 (excluding drought)
1. Flood

3.7 M

1931

Yangtze-Kiang River, China

2. Flood

2M

1959

N. China

3. Flood

900K

1877

Huang He (Hwang Ho or Yellow) River N. China

4. Flood500K

1939

Hunan province China

5. Cyclone, Flood

3-500K

1970Ganges Delta isles, Bangladesh

6. Earthquake

255-655K 1976

Tanashan (Tianjin) Earthquake- E. China (8.2)

7. Earthquake

300K

1850

Sichuan, China

8. E’quake, tsunami

225K+

2004

Indian Ocean Earthquake (9.0) + SE Asia tsumani

9. Earthquake

200K

1927

Xining (Nanshan), China (8.3)

10. E’quake, Landslide 200K

1920

Gansu (Kansu), China (8.6)

source: http://www.geocities.com/dtmcbride/hist/disasters-war.html
10World Pandemics
Death toll from Influenza Pandemics
DateStrain

1918-19 H1N1 - Spanish Flu
1957-58 H2N2 - Asian Flu1968-69 H3N2 - Hong Kong
1997H5N1, H9N2, H7N7, H7N2
H7N3 - Avian influenza(Bird Flu)

USWorld

500,000 21-50 M
60,000 1 M
40,000 750 K

none

60

Note 1: Some sources report up to 4 Million worldwide flu deaths in 1957-58

source: http://www.geocities.com/dtmcbride/hist/disasters-war.html
11Case Study: Tartan Capital Limited• Scottish Re is a relatively young (8 year old) life reinsurance specialist. The
largest portfolio is in the US.
• Scottish Re Group sponsored a catastrophic mortality risk securitization throughTartan Capital Limited• The transaction provides Scottish Re Group two tranches of 3-year, collateralizedprotection• The transaction provides Scottish Re with coverage against losses from extreme
mortality in the U.S. on an indexed basis• The transaction will cover losses in excess of the trigger threshold up to the limit ofeach tranche• A shelf is being established for potential additional issuance in the future• The company’s motivations for entering into the transaction include:• Mitigation of the impact of extreme mortality events• Execution of stated objective of increasing capital efficiency through the use ofsecuritizations

12Illustrative Issuance Structure
FGIC

Guarantee
Premium

SALIC (Cayman)

Quarterly, Fixed
Payments(Class A Interest andPrincipal Guarantee)

Interest

Tartan Capital
LimitedPrincipal, if no trigger event Class A
Proceeds

Proceeds
Payout if
triggered

3m LIBOR – 10 bps

Collateral AccountInvestment
IncomeSwap
3m LIBOR – 10 bps
Agreement

Goldman Sachs

13

Investors

Class B
Investors

Program Summary
Issuer:

14

Class A Notes

Class B Notes

Tartan Capital Limited

Tartan Capital Limited

Offered Amount:$75 mm$80 mmTerm:

3 Years

3 Years

Trigger Level:

115%

110%

Exhaustion Level:

120%

115%

Interest Spread:L + 19 bps

L + 300 bps

Rating:

AAA/Aaa

BBB/Baa3

Index Construction: Age/Sex
 The following weights are selected to create a profile of mortality risks bygender and age groupings
Age Weights: Male

65-74
2.3%

75-84
0.60%

55-64
9.8%

45-54
21.4%

85+
0.10%

1-4
0.0%

5-14
0.1%

75-84
0.40%
15-24
0.4%

35-44
26.0%

85+
0.10%

1-40%

65-74
0.8%

5-14
0.1% 15-24
0.4%

25-34
8.2%

Male = 68.8% of Total
15Age Weights: Female

55-64
2.7%

25-34
6.1%

45-54
7.8%
35-44
12.7%

Female = 31.2% of Total

Index Definition: Calculation FormulaThe Index Value for Measurement Period ending on December 31 of year
t can be expressed as:(qt +qt −1 )´=
´ 100%, where12 ´ (q2005+ q2004 )1Index Valuet(2qt = ∑ Wx ,m qm, x ,t + Wx , f qf , x ,tx)

Wx,m is the weight applied at age group x to male Mortality Rates
Wx,f is the weight applied at age group x to female Mortality Rates
qm,x,t is the Mortality Rate for males of age group x in calendar year t
qf,x,t is the Mortality Rate for females of age group x in calendar year t16

Index Definition: Loss Payment
The Index Value is defined over a consecutive 2 year period
The Index Value is based on age and gender weighted death rates for theUnited States constructed from publicly available data sources, as definedat inception (CDC)
For any Class, a Trigger Event is deemed to have occurred when theCalculation Agent delivers a Calculation Report where the Index Valueexceeds the respective Trigger Level
If a Trigger Event has occurred, the percentage of the original principal
amount lost increases linearly between the Trigger Level and Exhaustion
Level, calculated as:
Loss Percentage =Index Value – Trigger Level
Exhaustion Level – Trigger Levelx 100%,

provided that the Loss Percentage
shall not be less than 0% or greater than 100%

The Reporting Agent is the Centre for Disease Control (CDC)
The Calculation Agent is Milliman Limited.

17Expert Modeling Approach: Overview

Baseline Component
 Expected mortality
 Expected volatility250,000
simulations

Pandemic Component Additional mortality due
to potential diseasecalamity

250,000
simulations

Terrorism Component Additional mortality due
to potential non-disease
events18

250,000
simulations

Combined Model

Results Analyzer

 Combines Baseline
Mortality, Pandemicand Terrorism
scenarios into an
Index Value Determines
probability of loss
and extent of loss

Expert Modeling Approach: Pandemic Component• Pandemic Component – infectious disease epidemics:• Frequency and severity modeled separately based on historical occurrences of infectious
disease epidemics• Binomial modeled frequency of 7.4% per year, based on estimated 31 occurrences in the past
420 years, or 1 every 13.5 years on average• Modeled severity of epidemic event as the percentage of excess mortality fitted to 4 influenza
and 1 SARS occurrences over the last century• Data points used: Adjusted 1918-20, 1918-20, 1957-58, 1968, 1977 and SARS 2003
• The flu severity data are based on U.S. population experience

19Expert Modeling Approach: Pandemic Component% Excess
Mortality fromPercentile Epidemic
Adjusted 1918-20
1918-2019572003 SARS
1968
1977

0.0%
3.2
27.4
51.6
75.8
100.0

ExcessMortality Rate ‰from Fitted Curve53.56%
26.55
6.92
3.23
1.78
1.08

2.90461.4396
0.37530.1753
0.09670.0583

Excess Mortality

Disease Model: Fitted Severity Curve
60%
50%
40%
30%
20%
10%
0%
0%

20

10%

20%

30%

40%

50%

60%

Percentile

70%

80%

90%

100%

Expert Modeling Approach: Terrorism Component
Frequency:• Frequency of terrorist events is chosen from a normal distribution, with an expected number of 6.8
events per quarter and standard deviation of 4.3• This is based on a total of 163 recorded terrorist events world-wide between 1999 and 2004, where
all events involved U.S. citizens or property (excluding Iraq and Afghanistan)• 1999-2003 data is from the U.S. State Department. 2004 data is from the National Counterterrorism
Center, the U.S. government organization established in 2004 to serve as a “knowledge bank” oninternational terrorism
Severity:• The probabilities of success, failure and escalation at all levels are determined by fitting an
exponential distribution to the number of casualties from the data described aboveTerrorism Model: Number of Reported Terrorist
EventsTerrorism Model: Reported Deaths from Terrorist
EventsNumber of Deaths

Number of Events

20151050

1 2 3 4 1 2 3 4 1 2 3 41 2 3 41 2 3 4 1 2 3 41999

2120002001

2002

Quarter

2003

2004

3,500
3,000
2,500
2,000
1,500
1,000
500
0

1 2 3 4 1 2 3 4 1 2 3 41 2 3 41 2 3 4 1 2 3 41999

2000

2001

2002

Quarter

2003

2004

Impact of Historical Events on the Index••••

Several historical events have caused large numbers of deaths of U.S. citizens
Most of these events produced fewer deaths than would be required to reach any Trigger
Level for the Notes
Estimated magnitudes of historical events required to reach the respective
Trigger Levels for each Class, assuming that the Index is based on a reference year
immediately prior to each event, are shown below

Est. Magnitude1 to reach Trigger Level
Historical Occurrence

22

Class BClass A

•Influenza Epidemic2 (1918-20)

0.7x

1.0x

•World War II3 (1942-45)

2.1x

3.2x

•Korean War3 (1950-53)

17x25x•Vietnam War4 (1968-69)

13x20x•AIDS (1995)

3.9x5.8x•September 11 (2001)

105x

158x1)
2)
3)
4)

Actual magnitude will vary depending on actual concentrations by age groupings.
Based on 29.6% increase in Index Value calculated using 1917 – 1920 CDC historical data.
Includes US military deaths only, averaged over 4 years.
Includes US military deaths only, based on worst years of 1968 andand 1969.

Mortality in Historical Natural Disasters•There have been a number of devastating natural disasters in the U.S. over thepast 100 years•

While these natural disasters have resulted in large amounts of property
damage, such events would not have resulted in a large enough number ofdeaths to cause a loss to any Class of the Notes:
Natural Disasters in Recent United States History1

23

Event TypeYearFatalities•Hurricanes Katrina/Rita/Wilma

2005

1,326

•Heat Wave

1995

670

•Tornado

1984

600

•Winter Storm

1983

500

•Tornado

1984

328

(1) Source: World Health Organization, Emergency Event Database

Distribution of Maximum Index Value The graph below shows the distribution of the maximum Index Value over the threeyear projection period for the 250,000 model scenarios The results are summarized with an emphasis on the extreme right rail where thesevere mortality scenarios are located130125120%Index Value

120115%115110%

110

105

1000%

99.99%

99.9
5%

99.90%

99.8
5%

99.80%

99.7
5%

99.5
0%

99%

97%

96%

95%

90%

75%

50%

98%

Percentile

100.
0

24

25%

10%

95

Tartan Capital Loss Profile
The following tables show the cumulative probability of loss of each Class of Notes overa three-year risk period, as well as the average loss (given that a loss has occurred)
expressed as a percentage of the original principal amount
Summary of Modeling Results (bps)
Total Risk Period

Annualized Results

Class AClass BClass AClass B

•Estimated Probability ofAttachment

29 bps88 bps

10 bps

29 bps•Estimated Probability ofExhaustion

7 bps

29 bps

2 bps

10 bps•Estimated Expected Loss

16 bps54 bps
5 bps
18 bpsEstimated Average Principal Loss
Given a Loss Has Occurred

Cumulative Probability of Loss (bps)25•Year
1

Class A
0 bps

Class B
0 bps

ClassA% of Principal Loss53%•221 bps

62 bps

B61%•329 bps

88 bpsAttachment Probability Attribution Analysis••The estimated probability of attachment over the Risk Period for Class A Notes is 29 bps
and for Class B Notes is 88 bps. The graphs below depict the contribution by cause ofeach scenario in which the trigger level was reachedIn over 95% of these scenarios, the attachment may be attributed to the Pandemic
Component alone
Class A (bps)Class B (bps)
1.8

0.76
28.08

P andemic

26

0.28

0.04

0.08

88.48

29.32
0.68
85.56

0.16

Terro rism

0.36

P andemic & P andemic & Co mbinatio n
B aseline
Terro rism

To tal

P andemic

Terro rism

P andemic & P andemic & Co mbinatio n
B aseline
Terro rism

To talAcknowledgement

This presentation draws on material prepared by, among others, Goldman, Sachs & Co., andMilliman Limited. While all care has been taken, any errors contained within are theresponsibility of the author.

27

Financing Catastrophic Risk:
Mortality Bond Case Study
Andrew LinfootRegional Director, Asia Pacific