navigating the emergent risk landscape with scalable data...

Copyright © 2011-14 Risk Economics, Inc.

Navigating the Emergent Risk Landscape with Scalable Data Analytics:

Forensic RiskTech as the Strategic Response to Opportunities and Threats from Disruptive Technology

SCOR Re April 2014

David K.A. Mordecai Risk Economics, Inc. www.riskecon.com

Engaging "Crowds, Herds, Packs, and Tribes" via Forensic Statistical Inference/Control (i.e. Crowdsourcing coupled with ML) to Thwart Aliens, Zombies, Vampires & Robots

//Vampires

» Aliens: New Disruptive Entrants (invasive species)

» Zombies: Metabolic Diseases, Morbidity, Frailty; GMOs, Environmental Toxicology, Noise Pollution, etc.; Civil Unrest, Vandalism, etc. AND Vampires: Epidemics/Pandemics; Predatory crimes (fraud, terrorism, etc).

» Robots: Technology-related (i.e. CyberRisk) threats

QUESTION: Regarding developing an analytics dashboard for early warning and navigation system for emergent risk exposures underlying casualty, personal, financial and various specialty lines, what can be learned from case studies of climate.com, PERILS, and LLMA?

Copyright © 2014 Risk Economics Inc.

Innovative Strategic Industry Responses to Nat Cat Risk

http://binaryoptions.com/wp-content/uploads/Florida-US-Indemnity-ILW.jpg http://www.perils.org/dms/perils/img/content/Methodology_centered_460.png

http://gigaom2.files.wordpress.com/2013/02/climate_ch3-1_v2-1.jpg?w=708&h=298

Copyright © 2014 Risk Economics, Inc. 2

Emerging Environmental Threats: Water Contamination & Severe Drought http://www.mnn.com/sites/default/files/user-1071/figure1.jpg

http://graphics8.nytimes.com/images/2013/11/12/business/economy/12economix-climate-change/12economix-climate-change-blog480.jpg

http://www.sccwrp.org/images/ResearchAreas/Contaminants/EmergingContaminantsOfConcern/EffectsOnBiota/EffectsOnCoastalFish/EffectsOnCoastalFish_Figure3.jpg

http://toxics.usgs.gov/photo_gallery/photos/emc/frequency_graph_lg.gif


A Disruptive Data-based Innovation in Response to Weather Risk

http://3.bp.blogspot.com/-p4k1J1VXpRk/UlKh6w_d2aI/AAAAAAAAQp8/t_H1UqhuKhg/s1600/climat.png

http://gigaom2.files.wordpress.com/2013/02/climate_ch3-1_v2-1.jpg?w=708&h=298


Disruptive New Entrants: Both Capital and Technology Based

http://www.crunchbase.com/company/praedicat


Economics of Tort Escalation: 3% of Fortune 500 Annual Net Profits

http://failsafebydesign.files.wordpress.com/2012/02/chart.jpg http://www.uiowa.edu/~cyberlaw/elp00/dsfig1.gif

http://www.legalreforminthenews.com/speakers/cost_of_abuse/TortCostsPieChart.jpg


Tort Cost Economic Drivers

http://legalfinancejournal.com/wp-content/uploads/2011/09/2005-Median-Damages-Awarded-To-Successful-Plaintiff-By-Trial-Type.jpg


Expanding Litigation Information Technology (e.g. Ediscovery)


Cybersecurity and Corporate Espionage (More Broadly Defined)

© 2013-14 Risk Economics, Inc. 9

Commercial Demography and Tacit Collusion Network Externalities and Coalitions

An example of dynamic "network fragility" Another example of a complex interconnected network (in this case a hashtag community)

External and Internal Threats from “Fraud Risk” (whose fraud is it?)

© 2013-14 Risk Economics, Inc. 10

Social Media Risk at the Water Cooler

Outsourcing: Crowdsourcing and Cloud Services Whose IP is it Anyway?

© 2013 Risk Economics, Inc. 11

Product Liability and Contagion within the Ecommerce Value Chain


Sabermetrics (in Response to Increasing Intellectual Property Litigation?)

Copyright © 2014 Risk Economics, Inc.

13 Urbanization shifts demand (observed dynamics re: unemployment, crime)


Urbanization of Tort Liability: Greater Coupling => Less Diversification

http://www.thefocalpoint.com/uploads/images/article-comparing-and-contrasting_need_lrg.jpg

http://desertbeacon.files.wordpress.com/2013/07/product-liability-cases-2011.jpg

http://www.centralnewyorkinjurylawyer.com/assets_c/2009/12/pills-thumb-282x426-2371.jpg


Urban Density as an Exposure (Risk Loss and Liability) Amplifier


“Peer Effects” as Social Risk Propagators


Urban Exposure Amplification (Cont’d)

http://predoc.org/pars_docs/refs/158/157446/157446_html_m50439fd2.gif

https://lh3.googleusercontent.com/JCEQVEPetDkW9pKS8t_E4mFCeVvvhklRaF_Fasbyr6AOAozjg4S6y843ceJ31GSh9FACoCfpC3Hzz1aN2UlkX8p63BQjYzrBMMWrI202S9hzsiKLFlY


Navigating these Risks

18

• Harnessing massive and dynamic datasets for underwriting,pricing, loss mitigation and claims management

• The need to navigate the Deep Web in order to respond to theevolving risk landscape

• New data forensics i.e. metrics/analytics to explore deepstructure and collective behavior, and hence reflect futuredynamics• In 2012, 2.5 exabytes (2.5 billion gigabytes) created daily; that

number doubles every month)• In 2013, mobile data traffic = 1.4 million terabytes per month

(Source: iGR)• 2018 Projections: 13.5 million terabytes per month

• Mostly unstructured data (i.e. text, audio, video, usage)which requires special tools (e.g. machine learning)


Demographics, Sociometrics and Psychometrics matter

19

Actively monitoring consumer behavior, lifestyle choices, and aggregated preferences (e.g. food consumption) at the right level of granularity can be predictive of both short- and long-tailed loss development. Copyright © 2011-14 Risk Economics, Inc.

Case in Point: Correspondences Between Business Conditions, Unemployment, and Insurance claims

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Graph 1: Initial Claims vs Continuing Claims RecessionsInitial ClaimsInitial Claims 4Wk MAContinuing ClaimsCont Claims 4Wk MA

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Current Expansion 2001 Expansion1991 Expansion 1982 Expansion1975 Expansion

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Graph 3: Continuing Claims (000s)

For example, there are currently unexploited techniques for analyzing statistical relationships between changing unemployment insurance claims relative to workers compensation and disability claims. What is the result when adjusted for food-related morbidity?

Source: NBER, ETA and Risk EconomicsTM calculations Copyright © 2011-14 Risk Economics, Inc.

Disability versus Earnings

22

Relative earnings of permanent partial disability claimants as a proportion of earnings of comparison workers in five states

SOURCE: Robert T. Reville, Leslie I. Boden, Jeffrey E. Biddle, and Christopher Mardesich, An Evaluation of New Mexico Workers' Compensation Permanent Partial Disability and Return to Work, MR-1414-ICJ (Santa Monica, CA: RAND Institute for Civil Justice, 2001)

What this graph does not tell you is that disability claims increase during periods of high unemployment, i.e. poor business conditions. In other words, disability and workers compensation may be serving as a substitutes or supplements to unemployment insurance.

Question: what specific industry and occupational interactions may be predictive of (food-related) frailty?


Socio-Informatics: Network Analysis, Data-Mining and Text-Mining


These analytic methods are applicable to many risks: Energy and power and natural resource markets; Social systems and integrated modeling of interactions between natural and social processes; national security topics, e.g. distributed adaptive network control and terrorist networks; supply chain dynamics; biological systems, including pandemics; industrial and macroeconomic structures (trade and capital flows); other geopolitical, socioeconomic, legislative, regulatory, commercial, financial market and policy issues.

Source: CI Chicago

Data Mining has Economic Forensic Implications for Tort, Litigation, and Claims Settlement for a wide range of Supply-

Chain Risks including Food-related Casualty Liabilities

24 Copyright © 2011-14 Risk Economics, Inc.

Agent-Based Modelling

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Realistic “bottom-up” models of real-world strategic interactions, structural shifts, and complex evolving dynamics to inform underwriting for more intelligent risk management and loss mitigation

Source: CI Chicago


Does “God” Play With Fuzzy Dice?A stochastic game is a dynamic game played by one or more players with probabilistic transitions, such that at each stage

the probability distribution for that new random state depends on the previous state and the actions chosen by the players

Representation of a finite-state machine (example): an FSM that determines whether a binary number has an odd or even number of0’s. FSM can be non-deterministic (i.e. stochastic). Also state machines can be infinite (i.e. continuous)

on the previous state and the actions chosen by the players

Probabilistic parameters of a Hidden Markov model What if certain parameters (example of the simplest dynamic Bayesian framework):x states, y possible observations;a state transition probabilities, b output probabilities

f p(e.g. transition states) are fuzzy, time-varying, etc.?

Model underlying the Kalman filter. Squares represent matrices. Ellipses represent multivariate normal distributions (with the mean and covariance matrix enclosed). Unenclosed values are vectors. In the simpleUnenclosed values are vectors. In the simplecase, the various matrices are constant with time, and thus the subscripts are dropped, but the Kalman filter allows any of them to change each time step 0

17 Copyright ©2012 Risk Economics, Inc.

A stochastic game is a dynamic game played by one or more players with probabilistic transitions, such that at each stage

g y g p y yp y p g

the probability distribution for that new random state dependsp y f pthe previous state and the actions chosen by the playersthe previous state and the actions chosen by the players

Bayesian Graphical Models

A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional d d i i di t d li h (DAG)dependencies via a directed acyclic graph (DAG).

Bayesian networks that model sequences of variables are called dynamic Bayesian networks. Generalized Bayesian networks that can represent and solve decision problems under uncertainty by combining probabilistic inference and utility maximization are called decision networks,

l d fl d Effi irelevance diagrams, or influence diagrams. Efficientalgorithms exist that perform inference and learning in Bayesian networks.

A Markov random field (“MRF”), Markov network or undirected graphical model: a graphical model in which a set of Markov random variables are described by an undirected graph and is similar to a Bayesian network in its representation of dependencies An MRF canvariables are described by an undirected graph, and is similar to a Bayesian network in its representation of dependencies. An MRF canrepresent certain dependencies that a Bayesian network cannot (such as cyclic dependencies), but cannot represent certain dependenciesthat a Bayesian network can (e.g. induced dependencies). The classic MRF is the Ising model a statistical model of ferromagnetism

A log-linear model is a Markov random field with feature functions fk such that the full-joint distribution can be written as shown with partition function z, and where Xis the set of possible assignments of values to all the network's random variables

18 Copyright ©2012 Risk Economics, Inc.

A Markov random field (“MRF”), Markov network or undirected graphical model:

Generalized Bayesiany y ynetworks that can represent and solve decision problemsp punder uncertainty by combining probabilistic inferencey y g pand utility maximization are called decision networks,y

l d fl drelevance diagrams, or infln uence diagrams

A Bayesian network

CASE STUDIES AND EXAMPLES APPENDIX: FOR Q&A ONLY


Market Failure and Social Cost

• A Jevons problem: when technology adoption results inoveruse, misuse, and/or abuse (unintentional or purposive)ultimately resulting in negative marginal benefit

• Example: Emergent food system risks (e.g. GMOuncertainty; cross-contamination, antibiotic resistance (e.g.MRSA, XDR-TB), steroids (e.g. RGBH), inter-speciestransference risks):• Are related to issues of “appropriate technology”

adoption and natural resource competition• Represent a Jevons problem with broad implications for

incentives and governance of collective action

Copyright © 2012-14 Risk Economics, Inc. 29

Growing Tensions and Stresses

• A matter of fundamental rights versus property rights: rule of law andaccess to basic needs

• Economies of scale escalating competition for resources(consumption, development, growth)

• Cause of action: negligence or worse?• “Natural” Catastrophes … Force Majeure or Man-made? (Japan)

• Carrying capacity and limits to growth: biodiversity example economic and environmental fragility (food, soil, air and water quality,wastewater)• Amplified impact and tighter tolerances mean less room for error

• Aggregate consumption grows with population growth and growth inper capita consumption


Current (and Future) State of Affairs

• Environmental catastrophes related to food safety and security,pandemics, catastrophic collapse, etc. (short-run cost ofmorbidity and mortality related to wide-scale foodcontamination, compounded by long-run cost of morbidityrelated to obesity et al)

• Corresponds with water and sanitation issues resulting ingeopolitical and socioeconomic instability civil unrest• Potentially worse than BP, Exxon Valdez, Bhopal, Fukushima• Complex and severe government response (regulatory,

legislative)• Ensuing civil litigation and criminal prosecution, mass torts,

class actions


The Economic Implications of Catastrophic Environmental Collapse

• Man-made catastrophes: both unintentional anddeliberate (Richard Posner)• Technological and biological

• What might be the lessons from Agent-Based ModelSimulations (generative social science) re: carryingcapacity and limits to growth

• What economics might say about:• Efficient rationing of increasingly scarce resources

with shifting wealth and income disparities• Moral hazard• Adverse selection

• So, what does the data say about emerging risk?


World Population by Age Group 1950-2050

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Does this reflect the economic realities of an aging population?

Source: UNPP and Risk Economics® calculations Copyright © 2011-14 Risk Economics, Inc.

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01020304050607080

Old-agedependencyratioChilddependencyratio

Global Dependency Ratios: 1950-2050

Source: UNPP and Risk Economics calculations

0.05.0

10.015.020.025.030.035.040.045.050.0

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% Total Healthcare Spending as a Percentage

of GDP, 1960-2008, and Projections


Liabilities (claim payments) grow with increasing personal consumption related to life, health and medical conditions of target population (claimants)

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Life, Health, Annuity and Medical Liabilities(Driven By Longevity, Health and

Medical Consumption Trends)

Invested Premiums

(Net of Profit Distributions)

Surplus (Reserves)

Fixed Income, Equities, Real Estate, etc.


Notional amount of claim increases with growth of relevant (medical) consumption

Present value of claim payments increase with declining yields (interest rates)


As rates fall the value of a liability stream rises: One-time upward shocks to the relevant consumption shift the present value (“PV”) up. Convexity, the rate of interest rate sensitivity, increases significantly with duration (i.e. growth in liability payments, and the tenor of obligations linked to longevity

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For a 20-Year stream of liabilities (with 0% growth), the PV increases by around 10% as rates fall from 5% to 4%. For a 20-Year stream of liabilities (with 10% growth), the PV increases by around 15% as rates fall from 5% to 4%. For a 40-Year stream of liabilities (0% growth), the PV increases by around 20% as rates fall from 5% to 4%. For a 40-Year stream of liabilities (0% growth), the PV increases by around 25% as rates fall from 5% to 4%. In other words: Interest rate (i.e. yield) sensitivity of the liabilities (and hence surplus) depends upon the growth in the notional amount of claims payments


Correlation affects the present value of payouts and hence of surplus: The more negative the correlation between rates and relevant consumption, the

greater the impact of surplus (for a given level of claims payments)

38

Life, Health, Annuity and Medical Liabilities

(Driven By Longevity, Health and Medical Consumption

Trends)

Invested Premiums (Net of Profit Distributions)

Surplus (Reserves) Fixed Income, Equities, Real Estate, etc.


Case Study: GMO-Related Externalities Require a Systemic Approach

• Economics of epidemiology and environmental toxicology related to the configuration of the supply chain for food production and distribution (e.g. GMO technology) pose extensive and material exposure to insurance: • Regulation and legislation • Public action • Mediation, arbitration and litigation: liability rules, damages • Precedents and case studies: pollution regulations and toxic

torts (e.g. asbestos, benzene, PCBs, etc.); tobacco litigation and smoking legislation; 2007-?? financial crisis

• Commonly manifested in insurance economics as: moral hazard, adverse selection: i.e. risk-shifting


Emerging Infectious Diseases and Food Contamination

40 Source: Scientific American


41

Pathogens and pharmaceutically active compounds in manure, bio-solids, and other byproducts can be transmitted to animals and humans through food supplies, water, and possibly air. Animals on farms can also be re-infected not only via water and air, but also from other vectors such as birds, rodents, and insects that can directly infect the animal or contaminate animal feeds or water.

According to USDA estimates: - Animal production in the U.S. is valued at

over $100 billion annually. - The amount of manure generated in the

U.S. at CAFOs and AFOs is estimated to exceed 335 million tons of dry matter per year.


42 Source: CDC Division of Diabetes Translation. National Diabetes Surveillance System

<4.5% 4.5-5.9% 6.0-7.4% 7.5-8.9% >9.0%

<14.0% 14.0-17.9% 18.0-21.9% 22.0-25.9% >26.0%


2

Global Asset Allocation & Alternative InvestmentsLongevity risk and portfolio allocationJune 5, 2009

J.P. Morgan Securities Ltd.Ruy Ribeiro (44-20) [email protected]

Vadim di Pietro (44-20) [email protected]

The payout to the hedger who goes long longevity is simplythe opposite:

F - q75-84

If the realized mortality rate turns out to be equal to theforward price, no exchange of payments is made. If mortalityrates turn out to be higher than the forward price, the hedgermakes a payment to the investor, and vice versa.

In this note, we show that longevity risk in securities suchas mortality forwards is largely uncorrelated to equitiesand bonds, and is thus a good diversifier for investors.

Indeed, a number of funds invested in life-related productsperformed well in 2008, while most asset classes, includingmany alternatives, posted negative returns. We argue thatgoing short longevity adds significant value to investors’portfolios, even if the expected excess return on the invest-ment were close to zero.

Being short longevity is also a hedge against pandemics andwar. The World Bank estimates that a severe flu pandemiccould kill up to 70mn people and contract global GDP by upto 4.8%. This would cause a severe drop in equity prices, butbenefit short longevity positions. Thus, being short longev-ity could add value to investors’ portfolios, even if expectedexcess returns were below zero. In practice, however,shorting longevity is likely to provide positive excessreturns as the market is net short longevity, and also due toliquidity premia.

This paper is organized as follows. First, we discuss thedeterminants of longevity risk premium. Second, we analyzethe correlation between asset prices and changes in mortal-ity rates, for various holding periods, and also between assetprices and returns on the longevity component of lifeannuity returns. Third, we examine tail dependence bylooking at extreme historical events. Fourth, we use a mean-variance portfolio optimization approach to quantify therequired rate of return for shorting longevity. Finally, weconclude with several caveats.

What determines risk premium?How is the equilibrium mortality forward price determined?This depends in large part on the market’s expectations offuture mortality rates. For example, based on a given fore-casting model, the market’s best estimate of 75 to 84 year-olds realized mortality rate in the year 2016 could be 5.5%. Ofcourse, there is uncertainty around this forecast and theforward price will be adjusted to incorporate a risk premium.We define the longevity risk premium from the point of

view of the investor who is short longevity, and it is thusequal to the expected future mortality rate minus the forwardprice:

RP = E[q75-84] - F

The sign and magnitude of the risk premium will depend inpart on how keen institutions are to offset their longevityrisk. Note that the payoff to the hedging institutions isnegatively correlated to the value of their existing exposure:when mortality rates increase, losses from the mortalityforward are offset by a decrease in the value of the hedger’spre-exiting liabilities. When mortality rates decrease, theincreased value of their liabilities is offset by a profit fromthe mortality forward. The desire for institutions to offsettheir longevity risk will tend to push forward mortality ratesbelow expected future mortality rates. In the above example,hedgers may be willing to accept a forward price of 5%, evenif such a price implies a negative expected excess return ontheir long longevity position.

The other factor that affects the sign and magnitude of thelongevity risk premium is how eager investors are to goshort longevity, which in turn depends on the portfoliobenefits of being short longevity. We find that going shortlongevity adds significant value to investors’ portfolios,even if the expected excess return on the investment wereclose to zero. In other words, in the above example, investorsshould be willing to accept a forward price not much belowthe best estimate of 5.5%.

In the above example, the actual equilibrium forward pricewould lie somewhere between 5% and 5.5%. Exactly wherewill depend on how important hedging and diversificationbenefits are to hedgers and investors as well as the relativebargaining powers of both groups.

To be sure, the above arguments assume investors hold thelongevity investment to maturity. Otherwise a liquiditypremium would also be required. Given that the market isrelatively new, investors may also require a novelty premiumto take on longevity exposure.

Correlation to equities and bondsThe systematic risk in longevity-linked securities dependson correlations to standard asset classes. Several factorssuggest a negative correlation between being short longev-ity and long equities. An increase in economic activity leadsto more resources being available for health care, which inturn increases life expectancy. Conversely, a significanteconomic contraction can limit resources to health care and

The sign and magnitude of the risk premium will depend inpart on how keen institutions are to offset their longevityrisk. Note that the payoff to the hedging institutions isnegatively correlated to the value of their existing exposure:when mortality rates increase, losses from the mortalityforward are offset by a decrease in the value of the hedger’spre-exiting liabilities. When mortality rates decrease, theincreased value of their liabilities is offset by a profit fromthe mortality forward. The desire for institutions to offsettheir longevity risk will tend to push forward mortality ratesbelow expected future mortality rates. In the above example,hedgers may be willing to accept a forward price of 5%, evenif such a price implies a negative expected excess return ontheir long longevity position.

5

Global Asset Allocation & Alternative InvestmentsLongevity risk and portfolio allocationJune 5, 2009

J.P. Morgan Securities Ltd.Ruy Ribeiro (44-20) [email protected]

Vadim di Pietro (44-20) [email protected]

Longevity and markets in extreme eventsThe above analysis shows that there is very low correlationbetween longevity and equities/bonds. The correlationanalysis, however, ignores dependence during tail events.Below we show that being short longevity can be a goodequity hedge during extreme events.

According to a recent World Bank study, a severe globalpandemic would cause global GDP to contract by up to 4.8%in just one year.5 A more moderate pandemic, similar to the1957 Asian flu, would contract global GDP by about 2%. It isreasonable to expect very poor equity performance in bothscenarios.6 During the SARS epidemic, for example, HongKong equities sold off sharply, particularly those sectorsthat were directly affected, including transportation (Chart2). Most of those losses were recovered as fears of a moreserious outbreak abated. While the SARS epidemic claimed774 lives, the World Bank estimates that a major pandemiccould kill up to 70mn people.

In order to capture the impact of infrequent events, weanalyze the tail relation between mortality rates and equitymarkets based on annual US mortality rates and DJIA returnsgoing back to 1900.7

Tables 4 and 5 summarize stock market performance follow-ing extreme increases and decreases in mortality rates. Aspike up in mortality rates is associated with same-yearnegative equity returns (row 2) and is also followed bybelow average annual returns over the next five years (row3). Symmetric results are obtained for large improvements inmortality rates, with the caveat that only 3 such events areobserved. These results suggest that while mortality andequities exhibit low correlation in normal periods, they havea larger dependence in extreme events.

For completeness, we also analyzed mortality rate changesfollowing large stock market declines, but found no clearrelation (Table 6).

Chart 2: HK transportation equities were hit by SARSCathay Pacific, HKD

Source: J.P. Morgan, Bloomberg.

Table 4: Equity returns following spikes in US mortality ratesAnnual data, 1900-2008, log change in US male mortality across all age groups

Source: J.P. Morgan. Equity returns are price returns for DJ-Industrial Average. We consider extreme eventsto be those where the change in mortality rate (average of log change in US male mortality across all agegroups) represented more than a 0.5 standard deviation move relative to the previous year, and to anaverage of mortality rates over the previous 5 years.

Table 5: Equity returns following large declines in US mortality ratesAnnual data, 1900-2008, log change in US male mortality across all age groups

Source: J.P. Morgan. Equity returns are price returns for DJ-Industrial Average. We consider extreme eventsto be those where the change in mortality rate (average of log change in US male mortality across all agegroups) represented more than a 0.5 standard deviation move relative to the previous year, and to anaverage of mortality rates over the previous 5 years.

Table 6: Mortality rates following extreme negative equity returnsAnnual data, 1900-2008, log change in US male mortality across all age groups

Source: J.P. Morgan. Equity returns are price returns for DJ-Industrial Average. We consider extreme eventsto be those where equity returns represented more than a 0.5 standard deviation move relative to the longterm average.

5 A. Burns, D. van der Mensbrugghe, and H. Timmer, Evaluating the Economic Consequences ofAvian Influenza, World Bank, 2008.

6 The impact of the 1918 Spanish flu, which claimed the lives of 50-100mn people, on equitymarkets is clouded as the negative effects of the flu were offset by the ending of World War I.While US equity markets did post positive returns in 1918, this likely represented a reversal ofthe severe declines seen in 1917, when the US entered the war.

7 Note that spikes in mortality in one year are often followed by significant declines in the next,due to negative autocorrelation in mortality rate changes. One explanation is that those whosurvived in the previous year are likely to be healthier than average, or similarly that those whowere going to die in the next year got pulled forward. To filter out extreme changes in mortalityrates that merely represented a reversal of previous extreme changes we consider changesrelative to two lookback periods. Specifically, we consider extreme events to be those wherethe change in mortality rate (average of log change across all age groups) represented more thana 0.5 standard deviation move relative to the previous year, and to an average of mortality ratesover the previous 5 years. This leaves us with 9 extreme increases and 3 extreme decreasesin mortality rates going back to 1900.

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Dec-02 Mar-03 Jun-03

Equity declines Equity returnsMortality rate improvement

Sample average 4.87% 1.60%Average when return is very negative -23.30% 1.39%Next 5 years - 1.46%Number of cases 27 27

Mortality declinesMortality rate improvement Equity returns

Sample average 1.60% 4.87%Average when mortality declines 9.24% 13.18%Next 5 years - 9.96%Number of cases 3 3

Mortality spikesMortality rate improvement Equity returns

Sample average 1.60% 4.87%Average when mortality spikes up -5.83% -4.57%Next 5 years - -1.41%Number of cases 9 9

According to a recent World Bank study, a severe globalpandemic would cause global GDP to contract by up to 4.8%in just one year. A more moderate pandemic, similar to the5

1957 Asian flu, would contract global GDP by about 2%. It isreasonable to expect very poor equity performance in bothscenarios. During the SARS epidemic, for example, Hong6

Kong equities sold off sharply, particularly those sectorsthat were directly affected, including transportation (Chart2). Most of those losses were recovered as fears of a moreserious outbreak abated. While the SARS epidemic claimed774 lives, the World Bank estimates that a major pandemiccould kill up to 70mn people.

Objective: To Mitigate Adverse Impact of Unexpected Demographic (Population Mortality and Longevity) Risks of Insurers and Pensions(Population Mortality and Longevity) Risks of Insurers and Pensions

D hiForward Discount Curve

Longevity/MortalityDemographic

Cohorts(Age, Region, Gender, etc.)

Forward

Expected‘Short’ (‘Long’) Mortality‘Long’ (‘Short’) Longevity

Breakeven DiscountRate(s)

Facility CedingMaturtities

Crediting Rate(s)versus

Present Valueof

1 3 5 7 10 15 … 30

Counterpartiesof

ExpectedPopulation

Mortality/Longevity

Net Population Exposureof

Life, Health,Annuity, PensionLiabilities and

DemographicsUnderlying

Ceded LiabilitiesLife, Health,

Annuity, PensionLiabilities, andLiabilities, and

LT Casualty Medical LT Casualty Medical

9/29/2010 Copyright 2010 Risk Economics Limited

Mortality

‘Short’ MortalityBreakeven

Discount

Rate

Th t d t lit i k (f th l t l ti h t ) d l i d th b fit li biliti

MaturtitiesThe unexpected mortality risk (for the relevant population cohorts) underlying death benefit liabilitiessystematically increases the present value of those liabilities relative to their corresponding investment reserves.

Similarly the unexpected longevity risk (for the relevant cohorts) underlying life health annuity andSimilarly, the unexpected longevity risk (for the relevant cohorts) underlying life, health, annuity, andpension liabilities also systematically increases the present value of those liabilities relative to their corresponding investment reserves.9/29/2010 3Copyright 2010 Risk Economics Limited

The paths of the mortality improvements for the relevant population cohorts are non-stationary, i.e. they evolve over time in accordance with changing conditions (socio-economic developments, consumption patterns, health trends, medical innovations).

And, What About Morbidity? Obesity, Syndrome X, Diabetes, Cardio Pulmonary …

9/29/2010 4Copyright 2010 Risk Economics Limited

Can these evolving paths of mortality improvements for the relevant population cohorts that form a shifting landscape be dynamically mapped to price the risk of unexpected

mortality and longevity risk relative to investment returns?

9/29/2010 5Copyright 2010 Risk Economics Limited

Other ConsiderationsO e Co s de a o s

• Life/Health Exposures– Longevity and mortality trends– Chronic health conditions

• Medical costs– Impact on:– Social Security and MedicareSocial Security and Medicare– Pension, life, health and casualty– Impacts of shifting demographics on:– Labor supply– Savings/investment– Consumer spending– Trade

• OutlooksOutlooks– Short term– Intermediate term– Long term

9/29/2010 Copyright 2010 Risk Economics Limited 7

Related Matterse a ed a e s

• Emergent Complex Exposures• Environmental Trends• Water• Waste (solids and wastewater)• Pollution• Food safety and consumption issues• Food safety and consumption issues• Agricultural fragility and health trends• Emerging insurance risks• Global security and national safety issuesy y

So What’s It Going To Take? … Rigorous Analytics, Prudent Instrument Design, Good Governance, …

A firm grasp of the Risk Economics

9/29/2010 Copyright 2010 Risk Economics Limited 8

www.riskecon.com

• Risk Economics, Inc. (RiskEcon®) provides advisory services and testimonial expertise on complex issues related to financial instrument valuation models and frameworks, market and industry standards and practices for the financing, risk management, active trading, and hedging of OTC derivatives and structured products

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