Catastrophe bonds and financial risk: Securing capital and rule through contingency

Geoforum 45 (2013) 30–40

Contents lists available at SciVerse ScienceDirect

Geoforum

journal homepage: www.elsevier .com/locate /geoforum

Catastrophe bonds and financial risk: Securing capital and rule through contingency

Leigh JohnsonDepartment of Geography, University of Zürich, Winterthurerstrasse 190, 8507 Zürich, Switzerland

a r t i c l e i n f o

Article history:Available online 5 July 2012

Keywords:Catastrophe bondsInsuranceRiskContingencyFinancialization

0016-7185/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.geoforum.2012.04.003

E-mail address: [email protected] P. 228, in Ewald (1991), pp. 204–205.

a b s t r a c t

This paper uses the example of catastrophe bonds to investigate how exposures to geophysical, biological,and meteorological catastrophic events are constituted as securitizable and exchangeable financial risksin the insurance-linked securities (ILS) market. It discusses the techniques of catastrophe modeling as apivotal mobile methodology for the calculation and creation of contingent assets out of the fabric ofinsured environmental and financial vulnerabilities. Catastrophe models are shown to enable economicexchange of contingent futures belonging to ontologically and geographically disparate orders. Pensionfunds are then introduced to illustrate how biological lives and retirement savings have become deeplyentangled in the creation and extension of the ILS market. Pension funds are both major institutionalinvestors in catastrophe bonds and also the principal sellers of ‘‘longevity risk’’ posed by pensioners.The extent to which labor both profits from and embodies securitized insurance risks illustrates thegrowing importance and ambivalence of contingency as a modality of accumulation and rule.

� 2012 Elsevier Ltd. All rights reserved.

‘‘Man first thought of insuring his shipping against the risks of nav-igation. Then he insured his houses, his harvests, and his goods ofall kinds against risk of fire. Then as the idea of capital. . .graduallyemerged in a clear form out of the confused notions that previouslyobscured them, man understood that he himself was a capitalwhich death could prematurely destroy. . .He then devised lifeinsurance. . .against the premature destruction of human capital.Next he realized that if human capital can be destroyed, it can alsobe condemned to disuse through illness, infirmity and old age, andso he devised accident, sickness and pension insurance. [This] is thetrue popular form of insurance.’’

-Chauffon (1884), Les Assurances, leur passé, leur present, leuravenir1

‘‘Why have the Guardians of New Zealand Superannuation investedin catastrophe bonds?. . . [Because] they are a strong diversificationplay and offer attractive risk-adjusted returns... [funds] will mostlybe invested in securities that cover US hurricanes and earthquakes,with some products covering European wind storms and Japaneseearthquakes.’’

-Guardians of the New Zealand Superannuation Fund (2010),in a fact sheet for pensioners of the state

ll rights reserved.

1. Introduction

This contribution positions the ‘‘natures of risk’’ (Baldwin andStanley, 2012) in the context of a global insurance industry thatis increasingly interdigitated with capital markets. In the past dec-ade, trading has grown rapidly in the alternative asset class ofinsurance-linked securities (ILS), typically bonds, swaps, and fu-tures whose rate of return depends on whether or not a pre-spec-ified insurance loss trigger occurs within a certain time period. Ofthese products, catastrophe bonds are the most well known. Ashedge funds and institutional investors search for profitableinvestment vehicles with low correlation with the rest of the mar-ket, they are increasingly turning to these risks issued by property-and life-insurers. This paper examines how phenomena as diverseas earthquakes, hurricanes and influenza pandemics are epistemo-logically constituted as risks forming part of the same asset class,the characteristics of which trouble typical conceptual dichotomiesbetween assets and liabilities, and in between geophysical natureand human life. After considering the methods by which thesemore-than-human phenomena are produced as risks – that is tosay, how their contingencies are calculated, abstracted, and putinto circulation as financial instruments – the paper turns to theirentwinement with the techniques and institutions governingeveryday life. This process is most visible in public pension funds’investment in catastrophe bonds and private sector pension plans’sale of longevity risk through insurance-linked securitization. Thisopposition demonstrates how the same mobile methodologiesused to commodify these myriad risks are both binding labor tofinancialized physical landscapes and revaluing labor through thelens of contingency.

http://dx.doi.org/10.1016/j.geoforum.2012.04.003

mailto:[email protected]

http://dx.doi.org/10.1016/j.geoforum.2012.04.003

http://www.sciencedirect.com/science/journal/00167185

http://www.elsevier.com/locate/geoforum

L. Johnson / Geoforum 45 (2013) 30–40 31

1.1. Insurance technology

The institution of insurance, hailed as one of the principleindicators and results of modernity’s triumph over pre-modern no-tions of fate (Bernstein, 1998), is also championed by economistsand historians as a necessary condition for industrialization,urbanization, and economic development (cf. Wasow and Hill,1986; Pearson, 2004). It is a technology whose genealogy is inti-mately bound with those of probability, risk calculation, and theconstitution of the population as an object of liberal governance(Hacking, 1990; Ewald, 1991; Rose, 1999). Many scholars haveconceptualized insurance with reference to Foucault’s analytic ofgovernmentality; that is to say, as a component part of the‘‘ensemble formed by the institutions, procedures, analysis andreflections, the calculations and tactics that allow the exercise of[governmental] power, which has as its target population, as itsprincipal form of knowledge political economy, and its essentialtechnical means apparatuses of security’’ (Foucault, 1991, p. 102).Governmentality has thus been deployed to analyze the develop-ment of life, health, worker’s compensation, and pension insurance– the ‘‘true popular forms’’ of insurance, as Chauffon calls them inthe epigraph – as technologies which mobilized the statistical reg-ularities of populations and sentiments of liberal solidarity to pro-vide financial indemnification and security (Simon, 1987; Defert,1991; O’Malley, 2002). Economic sociologists and historians haveshown how the institutions of life insurance constantly negotiateand reflect changing social norms concerning morality, ownership,and responsibility (Zelizer, 1979; Knights and Vurdubakis, 1993;Clark, 1999; Quinn, 2008). It follows that the neoliberal eclipse ofthe welfare state has reworked many insurance technologies quitedramatically, as individual subjects are increasingly charged withtheir own self-care through private market mechanisms. Recentwork has traced how the logic of neoliberal financialization has re-shaped the terrain of life insurance and annuity markets and thesubjectivity of financial consumers themselves (Martin, 2002;Langley, 2008; French and Kneale, 2009, forthcoming).

In comparison, the institution of property insurance has re-mained relatively unexamined (the primary exceptions beingsociological treatments by Bougen (2003) and Ericson et al.,(2003); not coincidentally, both texts were written in the after-math of 9/11). Although natural hazards researchers investigatingsettlement patterns, environmental vulnerability, and post-disas-ter recovery have shown significant interest in the impacts of prop-erty insurance (Burton et al., 1993; Palm, 1995; Wisner et al.,2004), there has been relatively little critical consideration of theproperty insurance industry in terms of either the political econo-mies of its operation or the governmental rationalities it employs(but see Ericson and Doyle, 2004; Johnson, 2010; Sturm and Oh,2010). In the field of critical security studies, Lobo-Guerrero(2011) has performed perhaps the most historically wide-ranginganalysis of both life and property insurance as a technology of risk.He insists that ‘‘whereas insurance against natural or man-madecatastrophic events is usually presented as a form of reparationof damage on buildings and infrastructure’’, its real significance isas a form of biopolitics – which, following Foucault, he understandsas the exercise of power for ‘‘the protection and promotion offorms of life’’ (p. 5). Given these silences and provocations, this pa-per takes the capital-market-driven intermingling of the life andproperty insurance sectors in the form of catastrophe bonds asan entrée to consider the hybrid techniques of capital accumula-tion and rule they make visible.

I examine how the concept of ‘‘financial risk’’ is being mappedonto phenomena as ontologically disparate as epidemics, seismic-ity, demographic aging, and meteorological extremes, explicitlyfor the purpose of commensuration and exchange in the market. Isuggest that the ILS market demonstrates that these ‘‘natures of

risk’’ are produced and made equivalent through mobile technolo-gies of assessment and simulation – they do not preexist a very par-ticular neoliberal mode of assembling and ordering social, political,and environmental contingencies. This ordering has requireddiscursive work to stabilize the concept of ‘‘financial risk’’ and trans-form ideas of responsibility, liability, and acceptable sources ofprofit (de Goede, 2005). Meanwhile, it has necessitated the enroll-ment of new expert actors such as seismologists, meteorologists,and epidemiologists to authorize the commensurability of differentregimes of contingency within the domain of financial exchange.The resulting entanglements bring new problems to light that mightinflect ongoing discussions of the commodification of nature on theone hand, and the financialization of biopolitical rule on the other.

Throughout the work I attempt to bring ideas from Foucault’slater lectures (2008) on biopolitics and the development of neolib-eral governmental rationalities into generative conversation withbroadly Marxian analysis of nature, capital, and labor in the tradi-tion of historical–geographical materialism (Harvey, 1982, 1996;Smith, 2008 [1984]). In this attempt, I find that Lemke’s (2001) per-ceptive reading of Foucault’s ‘‘Birth of Biopolitics’’ lectures opensup new terrain. Lemke suggests that governmentality’s analyticalstrength ‘‘consists of the fact that it construes neo-liberalism notjust as ideological rhetoric or as a political-economic reality, butabove all as a political project that endeavors to create a social real-ity that it suggests already exists’’ (p. 203). In the same vein, Brownpoints out that the extension of neoliberalism is a deeply norma-tive, ‘‘constructivist project: it does not presume the ontologicalgivenness of a thoroughgoing economic rationality for all domainsof society but rather takes as its task the development, dissemina-tion, and institutionalization of such a rationality’’ (2005, pp. 40–41). Such normative projects are also often performative endeavors,as Mitchell (2007) has shown with respect to neoliberal develop-ment economists’ ‘‘natural experiments’’ establishing private prop-erty regimes. The relevant question here is then, how and with whattools are empirical social relations coaxed to more closely resembleidealized normative ones? The discursive and material constitutionof abstract ‘‘financial risk’’ out of entangled social, biological, andphysical relations allows us to observe this process in action, andto question its self-apparent logic.

1.2. Risk as register or mechanics?

The register of ‘‘risk’’ has become something of a master narra-tive through which the contemporary moment is articulated, per-vading academic discussions of the economy, finance, andeveryday life. Among many other theoretical incarnations, riskhas been recently conceptualized as a new form of money (LiPumaand Lee, 2004), a method of differentiation and capital accumulation(Martin, 2006), a mode of rule (O’Malley, 2004) and a device for bio-political securitization (Dillon, 2008). This is of course to say noth-ing of its proliferation in discussions of ecology, health, climatechange, and terrorism. If the single concept of ‘‘risk’’ can accomplishall of these tasks for authors working within broadly similar theo-retical frameworks, it seems fair to ask whether invoking ‘‘risk’’ asa sort of register has contributed to the loss of its meaning and ana-lytical purchase. Conceptual flexibility of application seems to comeat the expense of precision about the social and material relationsthat configure specific risks as such – their actually existingmechanics. Many discussions of the role risk plays in mediatingrelationships between individuals, capital accumulation, and ruleseem to resort to either high abstraction or anecdotalism.

It is in relation to these tendencies that this research endeavorsto keep the scales, geographies, and precarities of insurance riskswithin our analytical field of vision. This requires methodologicalattention to the sites, institutions, and discourses that mediatethese risks. Research for this paper – part of a larger study of

32 L. Johnson / Geoforum 45 (2013) 30–40

knowledge production in the reinsurance industry in the context ofclimate change – involved sixteen interviews with modelers,broker–dealers, reinsurers, analysts, and investors; attendanceand direct observation at four industry meetings in the US andthe City of London ranging from half-day to nearly week-longevents; and the collection of market data from consultancies, ded-icated ILS industry newsletters and proprietary data compilers,weblogs, and reports from reinsurance and ILS broker–dealer firms.This work took place over a 2-year period from mid-2008 throughmid-2010.

This methodological attention to processes and actors insiststhat specific phenomena are not inherently fungible as abstractrisk; rather, they are made to be so. Thus, they are contingent ratherthan necessary relations. Contra Beck’s (1992, 1999) position thatcontemporary being is defined by a ‘‘world risk society’’ in whichobjective material threats of our own industrial creation exist out-side our means of ordering and explaining them, this research pro-ceeds from the position that risks – be they environmental,financial, or both – are always mediated by social relations. Theyare actively constructed through relations of knowledge andpower, which influence how subjects, environments, and institu-tions bear or embody the particular risks of interest (Baker andSimon, 2002; Ericson et al., 2003; Zaloom, 2004,). Luhmann’s socio-logical analysis is instructive: risks are ‘‘not facts!’’, and identifyinga risk as such is ‘‘a matter of reconstructing a phenomenon of mul-tiple contingency’’, singling out particular causal chains amongmany (2005, pp. 15–16). As it is mediated by the insurance form,Ewald argues, risk designates not a specific event, but ‘‘a specificmode of treatment of certain events capable of happening to agroup of individuals – or, more exactly, to values or capitals pos-sessed or represented by a collectivity of individuals: that is tosay, a population’’ (1991, p. 199). As the teleological epigraph byChauffon demonstrates, the domain of values that could be under-stood as being at risk, and thus made subject to insurance technol-ogy, coevolved alongside the transformation of subjectivity itself.This process of expanding recognition of insurable values holdsstriking similarities to the financial transformations reconfiguringthe insurance market today.

Economic geographers have frequently noted the significance ofinsurance companies’ and pension funds’ investments in shaping theglobal economy, arguing that the dynamics of investing these mas-sive capital reserves drove the emergence of the new internationalfinancial system and fundamentally reshaped urban and regionaldevelopment (cf. Leyshon and Thrift, 1997; Clark, 2000; Monk,2009, among many). Meanwhile, the ongoing transformation of(re)insurers’ and pension funds’ actuarial and underwriting prac-tices has gone somewhat less remarked within economic geogra-phy. While their investment divisions were orchestrating a globalexpansion of their portfolios, (re)insurers and actuarial firms devel-oped increasingly technical methods for identifying, isolating, andpricing risks. These ranged from micro-rating practices – akin tocredit scoring – applied to individuals and neighborhoods (Squires,1997; Baker and Simon, 2002; Ericson et al., 2003), to the computa-tionally intensive catastrophe modeling of insurance portfolios(Grossi and Kunreuther, 2005; Collier, 2008), to the recognitionand quantification of corporate legacy costs such as the ‘‘longevityrisk’’ posed by long-lived pension fund beneficiaries (Loeys et al.,2007; Monk, 2008). The process of unbundling complex physical,biological, and economic relations to create a common metric offinancial risk in turn allowed for their recomposition as tradablefinancial products.

The growing domain of processes and events that are analyzedand hedged as ‘‘financial risks’’ signal both new forms of risk gov-ernance and new patterns of accumulation. In an inquiry into theimplications of financialization and risk management for labor,Martin and colleagues ask: ‘‘[if] finance is now a central conduit

of risk shifting and social change...how is financialization seeingthe possessive relations between persons and things dissolvedand reassembled?’’ (2008, p. 121). Using a process-based under-standing of financialization, the remainder of this paper elaboratesone answer to this question.

Among the myriad meanings of ‘‘financialization’’ that eco-nomic geographers have identified (French et al., 2011; Lee et al.,2009), here I understand the term to have two interlocking andcomplementary referents. First, financialization empirically de-scribes and periodizes a recent economic shift; second, it identifiesa process by which objects and relations are rendered financial. Inthe first sense, financialization refers to a shift in the dominant pat-tern of accumulation within economies of the Global North – espe-cially the US and UK – since the 1970s, in which ‘‘profits accrueprimarily through financial channels rather than through tradeand commodity production’’ (Krippner, 2005, p. 174, followingArrighi (1994) but see Christophers (2011)). In the second sense,financialization refers to processes by which specific possessiverelations between people and things are transformed into relationsthat motivate and/or require financial activities, where ‘‘financial’’refers to transactions of liquid capital ‘‘in expectation of futureinterest, dividends, or capital gains’’ (Krippner, 2005, p. 174–175). In this paper, I generally employ the second meaning offinancialization – its active verb form, so to speak – although inSection 4 I am also concerned to understand how these processesproceed from and deepen the shifts in accumulation patterns iden-tified by the first meaning. The paper proceeds as follows. Section 2summarizes the ILS market, its structure, size, and product typol-ogy, and discusses its ontological mixing of ‘‘life’’ and ‘‘non-life’’,the two traditional branches of insurance business. Section 3 elab-orates the calculative logics and institutions at work to make suchan ontological choreography possible. Section 4 examines the com-plex position of pension funds in ILS asset creation and risk defini-tion, and Section 5 concludes the paper.

2. ‘‘Life’’ and ‘‘Non-life’’ in the capital markets

The insurance-linked securities market developed amidstwaves of financial deregulation, insurance market consolidation,and growing financial losses to natural catastrophes in the 1990s.Here I lay out the basic features of the market that are most impor-tant for the paper’s discussion of securitization, commensurationand exchange. Securitization is the process by which a financialinstitution pools its contractual liabilities and packages them tosell onwards as a financial product (a security), which investorspurchase based on expectations of future income streams. In ex-change, investors assume the liabilities associated with the perfor-mance of the income stream, which are transferred off the originalinstitution’s balance sheet. Elsewhere, I have explored the emer-gence of ILS within the context of growing asset-backed securitiza-tion trends of the last 15 years (Johnson, 2011). There I discuss theeconomic geography of the market and its recent transformationsin greater detail, and suggest that ILS constitutes a significantmethod by which capital markets access previously untapped pre-mium incomes from the world of insurance risks. In the case ofcatastrophe bonds, place-bound environmental vulnerabilities offixed capital have been turned into an exploitable and diversifyingasset class, which financial capital strategically moves into or outof depending on the rates of return.

Catastrophe bonds (hereafter ‘‘cat bonds’’) are the most iconicILS products. At the end of 2011, active capital at risk in non-lifeinsurance catastrophe bonds amounted to nearly US $13 billion(Willis, 2012). They are usually sponsored by an insurer or reinsurerseeking to transfer part of its concentrated and hazard-specificgeographical exposures off of its portfolio and into the larger and

Table 1Commonly securitized insurance exposures and locations. Sources: Trading Risk (2010), Swiss Re (2009a), Barrieu and Albertini (2009) and Aon Benfield (2010).

Non-life (property/liability) Life

Peril Most common regions securitizeda Phenomenon Regarding insured individuals in

Hurricanes/cyclones US (esp. Florida)b Extreme mortality Europe, North America, JapanEarthquake, fire following earthquake US (esp. California)b, Japanc Longevity Europe, (esp. England and Wales)Winter storms Europec Life settlements US, EuropeWildfire USThunderstorm, tornado/hail North AmericaAutomobile US, Europe

a Other countries and regions have been covered on an occasional basis: Australia, New Zealand, and Canada for multiperil; Taiwan, Mexico, Turkey, Greece, Israel, Cyprus,Portugal and Central America [countries undisclosed] for earthquake; UK for flood, and Mexico for hurricane.

b ‘‘Peak perils’’, hazards linked to particular geographical regions for which bonds command an additional premium due to the intersection of high concentrations ofproperty values and exposure to particularly destructive natural hazards.

c Japan earthquake and European winter storm are lesser ‘‘peaks’’ than US wind and earthquake.


more liquid capital markets, absorbing losses and preventingbankruptcies from spreading throughout the global reinsuranceindustry2 following a large catastrophe (Jaffee and Russell, 1997).Cat bonds typically offer investors relatively high rates of return inexchange for their principal, which is held in reserve to protect thesponsoring insurer or reinsurer from specific catastrophic loss eventsthat exceed a pre-determined ‘‘trigger’’ value.

Insurers’ and reinsurers’ exposures to earthquakes, tropical cy-clones and winter storms are all common targets for securitization(see Table 1).3 Despite differences in the underlying perils and geog-raphies, the legal and regulatory structure of most cat bonds is rela-tively similar. The sponsoring insurer or reinsurer creates anindependent Special Purpose Vehicle offshore, to which it pays pre-miums directly, and which acts as its exclusive reinsurer. In turn, theSPV issues bond notes to investors and deposits their principal in atrust. Investors receive payments from the trust at specified intervalsat a benchmark interest rate plus a spread. At the date of bond matu-rity (an average of 3 years), investors receive their principal if nocatastrophes have triggered a payout to the sponsor. If the bondhas been triggered, the SPV transfers part or all of the funds in thetrust to the sponsor to cover losses.

Like many packaged securities, cat bonds are often offered in‘‘tranches’’, allowing investors to select between notes with higherand lower expected losses. Expected losses are calculated by acatastrophe modeling firm contracted to model the sponsor’s port-folio (see Section 3.1). Senior tranches, which often receive ‘‘invest-ment-grade’’ ratings by credit rating agencies, cover segments ofthe sponsor’s portfolio with lower expected losses. Accordingly,these notes pay lower returns to investors. Part of the principalin such senior tranches may be protected, ensuring that investorsdo not lose their entire investment even if the bond is triggered.Institutional investors with relatively lower ‘‘risk appetites’’, suchas pension funds, often purchase these senior notes. The notes of-fered in junior tranches cover the segments of the sponsor’s port-folio deemed to have higher expected losses. Accordingly, theypay investors significantly higher returns, but often do not receiveinvestment-grade ratings and feature no protection of the princi-pal. These junior offerings are typically targeted to more risk-toler-ant specialized buyers such as hedge funds and dedicatedcatastrophe funds.

2 Reinsurers provide additional insurance cover to primary insurance companies,typically for very large losses in the property-liability sector. Global catastrophereinsurance capacity was $205 billion in 2009 (Swiss Re, 2009a, p. 41).

3 Because catastrophe bonds are sold in unregistered private sales to ‘‘QualifiedInstitutional Buyers’’ according to terms set in Rule 144 of the US Securities Act of1933, there is no single public clearinghouse of information about investors or bondissuance. Nevertheless, it is possible to construct a general picture using the freelyavailable reports produced by a number of broker-dealers and consultancies.

2.1. Pandemic and extreme mortality

The process of securitization has also been extended into therealm of life insurance.4 Extreme mortality is perhaps the most exo-tic addition to the list of perils securitized through the catastrophebond structure. These bonds are designed to cover the massive lifeinsurance payments that (re)insurers could be saddled with in theevent of a deadly pandemic or an extremely fatal natural disaster.5

They have become increasingly sought after by dedicated cat inves-tors looking to diversify their portfolios, and by life insurers andreinsurers concerned about emerging strains of infectious diseases– SARS, avian flu, and H1N1 among them.

Amidst fears of an escalating H1N1 pandemic in November2009, and again in June 2010, Swiss Reinsurance completed issu-ances of ‘‘Vita Capital IV’’, extreme mortality notes totaling US$125 million. Investors’ collateral is invested in highly rated secu-rities issued by the International Bank for Reconstruction andDevelopment, from which they will be paid the IBRD notes’ interestin addition to a spread of between 5.25 and 6.5%. The bonds triggerif, during any 2-year period between issuance and its maturity in2014, the combined mortality index exceeds predefined percent-ages of the base year’s mortality level in the US and UK. Swiss Rehas securitized a total of $1.4 billion of mortality exposure throughsimilar offerings in the past (Swiss Re, 2009b), making it by far thelargest issuer in the market, whose combined volume is nearly $3billion. The company projects that the extreme mortality bondmarket will grow to between $5 and $20 billion within 10 years,pointing towards ‘‘significant untapped opportunities. . .supportedby increasing pandemic concerns’’ (Swiss Re, 2009a, p. 3).

The manipulation of nature through industrialization and glob-alization are explicitly invoked in the industry’s recognition of pan-demic risk. Biological emergence is subtly cultivated as both threatand opportunity. Harkening to Beck’s formulation of multiplying‘‘man-made’’ risks, a recent Lloyd’s report articulates this ambiva-lent position:

As national borders open up to wider and more profitableexchange, we also expose ourselves to a wider range of patho-gens and parasites. The fact that we have reduced natural diver-sity in order to expand our production and transport capacitymultiplies the danger because diversity is nature’s best defense

4 One major segment of the ILS market not discussed here is life securitization asused for capital restructuring within an insurance company (‘‘embedded valuesecuritizations’’), rather than as an instrument for risk transfer. This is similar tostandard corporate bond issuance.

5 They are not explicitly called ‘‘pandemic’’ securitizations for the simple reasonthat the trigger for the bond is based upon mortality rates, rather than individuacauses of death (since the latter trigger would be prohibitively unwieldy toadminister).

l

ig. 1. Stylized exceedance probability curve, depicting the likelihood y that aonetary loss of magnitude x will occur in any given year. The inverse of the

robability is the expected ‘‘return period’’ in years. Loss estimates for the 100 and50 year return periods are often used as benchmarks for portfolio comparison andpital reserving.


against disease. Past wide-scale and devastating pandemicshave occurred before the rise of the emergence of globalizednetworks of trade, people and services. The full impact of a pan-demic today is therefore unknown. (Goldin, 2010, p. 21)

Bodies that are ‘‘radically open to the world’’ do not pose justbiosecurity risks (Braun, 2007, p. 17) but financial ones as well.These in turn have provoked financial engineering responses thatbring life itself into the rubric of catastrophe securitization.

Because extreme mortality can be modeled in essentially thesame way as other catastrophe risks (as discussed in Section 3),it has become another of many offerings in a menu of perils fromwhich investors select and mix. This blending muddies the tradi-tional industry distinctions between ‘‘life’’ and ‘‘non-life’’ insur-ance, typically treated as highly distinct and immiscible businesslines. Generally, the life insurance sector includes annuities, creditlife, and fixed-term life insurance policies that link payments to anindividual’s life or mortality; the ‘‘non-life’’ (or ‘‘property casualty’’in the US) sector covers a vast array of losses including commercialand industrial property, homeowners, automobile, worker’s com-pensation, health, liability, and business interruption.

The speed and intent with which geophysical and human bio-logical phenomena have been brought together in capital marketsshould alert us to the necessity of considering them all together, asa border crossing between the human and non-human which thelogic of securitization has no trouble in traversing on a daily, oreven an hourly basis, as is evidenced by trading in secondary mar-kets. The blending of ‘‘life’’ and ‘‘non-life’’ within ILS illustrates theflexibility and mobility of a system that is not itself unsettled bythe ontological differences of the objects underwritten.

The work of establishing and maintaining their equivalence,however, is no small task. It is a sort of precarious ‘‘ontological cho-reography’’, the term Thompson (2005) uses to describe the com-plex terrains of personhood, science, and capital invoked in thework of assisted reproductive technology. She argues that ‘‘whatmight appear to be an undifferentiated hybrid mess is actually adeftly balanced coming together of things that are generally consid-ered parts of different ontological orders’’ (p. 8, italics mine). The fol-lowing section considers how such choreography is orchestrated inILS markets.

3. Technologies of tradable contingent futures

In order to create a viable market in which risks arising fromsuch an array of geophysical, meteorological, and biological eventscan be bought and sold, they must be made to appear equivalent inform. In this regard, both the market institutions and financial con-cepts that make such equivalence possible demand our attention. Ifthe techniques of insurance operating through the calculus ofprobabilities represent ‘‘a schema of rationality, a way of breakingdown, rearranging, ordering certain elements of reality’’ (Ewald,1991, p. 199), then catastrophe models are preeminent tools inthe contemporary dismantling and rearranging of risk. Other schol-ars have suggested that cat modeling should be understood as apowerful technique for making uncertain environmental futuresimaginable and governable (cf. O’Malley, 2003; Collier, 2008;Randalls, 2009). While seconding these arguments, here I empha-size modeling as a tremendously successful sociotechnical styleof reasoning and translation that renders these contingent futuresexchangeable in the market.

Credible risk estimation is only possible due to the ongoingtechnical development of catastrophe modeling capabilities forspecific perils. Originating in the late 1980s and early 1990s, catmodels have become the dominant risk assessment methodologyin property/liability insurance and reinsurance. Following stagger-ing losses from hurricane Andrew in 1992, it became clear that

methods of writing business using underwriters’ prior experienceand estimations were woefully inadequate. Computationally inten-sive cat models began to be more widely used by (re)insurers toestimate possible losses, gauge capital adequacy, and produce‘‘actuarially fair’’ risk prices for property insurance. In the case ofhurricanes, for example, models produce thousands of simulatedstorm tracks, wind speeds, and precipitation intensities (the ‘‘haz-ard module’’). These storm attributes are then matched to esti-mated damage curves for specific building stock in the givenregion (the ‘‘vulnerability module’’), and finally used to generateloss estimates by analyzing the specific geographical distribution,structure of reinsurance contracts, and size of the (re)insurer’sportfolio (the ‘‘financial module’’) (Grossi and Kunreuther, 2005).

This cat modeling logic has been absolutely central to the devel-opment of the catastrophe bond market. Functioning as the insur-ance industry’s technically specialized supplement to credit ratingagencies, catastrophe modeling firms couple enormous propertyand claims databases with constantly revised geophysical modelsand parameterizations. Teams at the major modeling firms (RiskManagement Solutions, AIR Worldwide, and to a lesser degree,EQECAT) are hired by bond sponsors to produce the risk analysisportions of bond offering circulars. Models overlay stochastic perilsimulations upon extremely detailed geographical datasets ofbuilding stock. The probabilistic output is presented in the offeringcircular, which sets out the terms of the issuance and provides anassessment of the likelihood of losses on the bond. But cat modelsare not simply used by (re)insurers and rating agencies; all threemajor modeling firms now also offer specific ILS investor-targetedsoftware platforms for analyzing cat bond offerings. The structureof the models allows modules to be removed and inserted at will,enabling risk commodification and comparison across spaces andperils.

3.1. The ‘‘grammar’’ of catastrophe risk: translation and abstraction

The particular ‘‘grammar’’ of catastrophe risk modeling itself re-quires some explanation, insofar as it invokes and creates a sharedset of meanings that can be applied across perils. Indeed it is ac-tively cultivated as an extension of language itself, as the directorof research at one modeling firm demonstrated in a presentationto clients. Referring to the logical relation between hazard, vulner-ability, and financial modules within catastrophe models, he said:‘‘The grammar of cat modeling is like a sentence... it’s actually dee-

Fmp2ca


ply structured into how we think.’’But far from simply invoking some ur-language, establishing

this ‘‘grammar’’ of catastrophe risk is always an ongoing work oftranslation and abstraction. Drawing on Luhmann (1989),Robertson (2006) has nicely interpreted a similar kind of scientifictranslation afoot in ecosystem services assessment: ‘‘Because eachspecialized knowledge system has established unique standards ofproof and verity, ecological information can never enter into legalor capital logics qua ecological information. Instead it must betranslated into speech and codes appropriate to law and capitalby people who occupy forums of articulation’’ (p. 370).

For property (re)insurance as well as catastrophe bond transac-tions, formalized speech and codes turn myriad geophysical andbiological phenomena into ‘‘the nature that capital can see’’(Robertson, 2006), presenting model results in terms of theabstraction of ‘‘expected loss’’. This number represents the likeli-hood that financial losses on a bond or portfolio (or, for annual ex-pected loss, losses within any given year) will meet or exceed acertain amount. The expected loss is the inverse of the ‘‘return per-iod’’, the projected number of years between events of a certainmagnitude. These figures are typically presented in the form ofan exceedance probability curve, as pictured in Fig. 1.

A ‘‘1% expected loss’’ of $150 billion industry-wide, for instance,means that the model in question projects that peril X occurring inregion Z would cause industry-wide losses of $150 billion on aver-age every 100 years (by comparison, insurance industry lossesfrom Hurricane Katrina were just over $40 billion). Modeling ex-pected losses for insurance-linked securities is further complicatedby the fact that the model must also take into account how thebond payment is ‘‘triggered’’.6 A bond with a ‘‘parametric’’ or ‘‘in-dex’’ trigger is more straightforward to model than one triggeredby a specific company’s losses, since an individual company’s partic-ular losses to a given event are highly contingent upon the event’sfootprint, the exact geographic location of insured properties, andthe particulars of policy structures and deductibles.

The expected loss also figures into the calculation of the ‘‘riskmultiple’’, a term expressing the expected excess return (profit) di-vided by the expected loss. The risk multiple is a simple heuristicused by investors to compare across bonds with highly disparaterates of return and expected losses – a measure of the relative pay-ment an investor receives to carry risk irrespective of the size of thepotential loss, the ontological character of the underlying phenome-non, or the geographical location where it unfolds. An analogy mightbe drawn with the metric of Global Warming Potential, whichbenchmarks the relative radiative forcing of various greenhousegases against that of CO2. MacKenzie (2009a) shows that this workof commensuration, however imperfect and disputed, was funda-mental to the creation of an operational market for certified emis-sions reductions. Despite the potential errors in the estimation ofthe risk multiple – linked as it is to the accuracy with which the ex-pected loss can be calculated or known – its metrological and com-mensurating functions are central to the process of market making.

In making diverse catastrophe exposures fungible, financial riskitself seems to gain ontological status. The process bears anextraordinary resemblance to Marx’s account of abstraction, com-modification, and fetishization (1967 [1867]). Significantly, it is theprocess of establishing equivalence that creates the asset, not vice ver-

6 Bonds can be structured to trigger payouts in a number of different waysIndemnity triggers are based on the sponsoring (re)insurer’s losses in the specifiedgeographical region exceeding a monetary value as a result of the peril coveredIncreasingly, cat bonds are written with parametric triggers based on indices ormeasures of the physical properties of the event itself, or indices of industry-wideloss. A third trigger device is modeled loss, in which the payout comes only after aspecified catastrophe modeling firm runs a post-event deterministic model (usingparameters of the actual event) and returns a modeled loss amount that exceeds aparticular monetary value (Hagedorn et al., 2009).

7 Although beyond the scope of the current paper, the extension of risk as ananalytic of terrorism demonstrates the political traction of this ontological ordering(see Martin, 2006; Aradau and Van Munster, 2007; Amoore and De Goede, 2008). Theextraordinary aspirations of this analytic often become most apparent at themoments it fails – as it did, for example, in the hastily abandoned 2003 proposafrom the US Department of Defense’s Defense Advance Research Projects Agency for afutures market for Middle Eastern political events including assassinations andterrorist attacks (Ericson, 2008). This ‘‘Policy Analysis Market’’ was supposed tomarshal financial markets’ efficiency and price discovery mechanisms to generatenew intelligence for the state’s political and counter-terrorism efforts (Looney, 2004)

.

.

sa; the relationships of liability and protection embedded in a bonddo not themselves constitute an asset prior to their assessment viacat model. A bond does not exist as a tradable commodity or in-come stream unless and until it has been modeled and assignedan expected loss. Models thus perform a bringing-into-being offinancial risk, accomplished through repeated simulation of thedestruction of exchange values.

Catastrophe models are instantiations of the sort of new com-putational and geographic technology that Leyshon and Thrift ar-gue have emerged in lockstep with financialization to ‘‘make newclasses of risk and new geographies apparent’’ (2007, p. 107). Theirreliance on highly-credentialed scientists, engineers, and GIS spe-cialists provides another example of the importance of ‘‘new formsof expertise, fuelled by computing power and software’’ (p. 101) infacilitating the aggregation of new income streams ‘‘from existingobligations which were never before considered to be capable ofcoalescence’’ (p. 103).

The practice of building and running these models thus consti-tutes a contingent ‘‘geography of asset creation and destruction’’(Lee et al., 2009, p. 740; see also Pike and Pollard, 2010). This sug-gests a role for catastrophe models – and the scientists and engi-neers who build them – in the production and financialization ofspace. This is particularly important in light of the expected dou-bling of global catastrophe reinsurance capacity, from US $205 bil-lion in 2009 to $410 billion in 2019 (Swiss Re, 2009a, p. 41). If thedynamics of capitalist investment and disinvestment tend to treatthe globe as an uneven ‘‘profit surface’’ (Smith, 2008 [1984], p.197), then catastrophe models are tools of significant importancein plotting the topography of this surface and determining which‘‘regionalization[s] of value’’ are worthy of investment, and whichoffer either insufficient returns or inordinate risk (Leyshon andThrift, 2007, p. 103).

The rapid extension of catastrophe modeling and ILS markets tonew ‘‘emerging’’ risks demonstrates the incredible power of thisontological ordering. New models now on the market include pan-demic flu, catastrophic mortality, longevity risk, terrorism, and‘‘litagion’’ – a clever neologism referring to ‘‘litigation contagion’’.Of these, only the first three have been successfully used for capitalmarket securitization purposes, whereas terrorism catastrophemodels are regularly used for analyzing property, liability, andworkers compensation portfolios in cities (Randalls, 2009), andlitagion models are marketed for corporate and product liability,life, and health.7 The fact that these phenomena are modeled doesnot mean that they are necessarily well understood. Because suchrisk calculations are in high demand from insurers and financialmarkets, modeling firms are engaged in a constant competition tograb market share by creating models for new perils and new re-gions. (Re)insurers and investors constantly seeking new ways to im-prove their risk/return ratio provide a ready market.

3.2. Contingency and the exchange of financialized catastrophe risk

‘‘Science liberates capital!’’ exclaimed the director of analytics ata major reinsurance brokerage to a room full of model users frominsurance and reinsurance companies. His point was that theapplication of cutting edge geophysical research to the business

l

.

Fig. 2. Simulated ‘‘class correlation matrix’’ output from Risk Management Solu-tions’ software for ILS investors, Miu.

Fig. 3. Value at Risk and Tail Value at Risk in a hypothetical cat bond portfolio,simulated in promotional material for Risk Management Solutions’ Miu platform.


of (re)insurance and ILS through catastrophe modeling allowsmoney to circulate more efficiently. Indeed the whole point ofthe translation and commensuration described above is to facili-tate efficient market exchange.

Cat models have become the chief technologies creating trad-able contingent futures. Return period, expected loss, and probabil-ity of trigger are some of the metrics that investors use to diversifyportfolios between perils, for instance mixing ‘‘some Cal quakewith a little Japan wind’’, as one fund manager phrased it. Modelingsoftware tailored to ILS promises investors particular insight intothe correlation of exposures between several bonds (see Fig. 2).Platforms such as Risk Management Solutions’ Miu provide a mod-eled ‘‘library’’ of all active cat bonds, allowing funds to scenario testand monitor aggregate exposures in their ILS portfolios throughmeasures such as Value at Risk and Tail Value at Risk (see Fig. 3).8

These model metrics’ importance to the entire ILS market pro-vides a startlingly concrete example of Randy Martin’s argumentabout risk management as a means of surplus extraction: ‘‘intricatequantifications of risk refine the process of differentiation of valueby breaking up or bringing together what were once treated as sta-ble affiliations of people and place, geography and history. . .finan-cialization is the process by which social affiliations arereconfigured to extract wealth as an ends by means of risk manage-ment’’ (2006, p. 6). Martin’s emphasis on the role of differentiationis useful: contrary to readings of financial risk as ‘‘single, homoge-nous whole’’ (LiPuma and Lee, 2004, p. 146), the ILS market does

8 Value at Risk (VaR) models estimate a firm’s possible losses under ‘‘ordinaryconditions’’, usually defined as 5% and 1% probabilities. VaR measures are commonlyused in firms’ internal risk management procedures to determine the maximumallowable size of an individual investment. Recent commentators have blamed theaccumulation of huge unrecognized financial exposures in the run-up to 2008partially on underlying flaws in VaR and related models, which they fault forassuming that probability distributions can be well defined even for ‘‘tail’’ regions,and that future market behavior will resemble the past. Additional criticisms includetheir failure to account for systemic risk, their tendency to give management a falsesense of confidence, and their inability to represent how traders shape and exploitmarkets based on their own theoretical expectations (cf. Taleb, 2007, MacKenzie,2009b). The growing currency of Tail Value at Risk, designed to calculate worst caselosses in a manner that accounts for the particular shape of a distribution’s tail, istestament to the financial system’s need to stabilize and recuperate the concept offinancial risk itself.

not equalize difference, as we can see from the continuing higher re-turns and popularity of peak peril bonds flagged in Table 1.

Instead, as metrics of risk, the commensurating technologies ofthe ILS market make differentiated worlds ‘‘fungible in terms of themeasure of their exposure to contingency’’ (Dillon, 2008, p. 311).Exposure to contingency is not non-value, or even devaluation, butrather the chance of de- or re-valuation at some point in the future.The centrality of contingency to ILS asset creation does not sit easilywithin existing typologies of the commodification and neoliberal-ization of nature. It seems clear enough that ILS markets, in whichhedging and speculating on natural catastrophes constitute part ofan accumulation strategy, are part and parcel of the ‘‘vertical integra-tion of nature into capital’’ that Smith notes entails ‘‘not just the pro-duction of nature ‘all the way down’, but its simultaneousfinancialization ‘all the way up’’’ (2007, p. 33). But if we pause to con-sider what the specific process of insurance-linked securitization in-volves, it appears quite distinct from the acts of commodification,privatization, dispossession, deregulation, and reregulation that‘‘neoliberal natures’’ scholars have considered (for overviews seeCastree, 2003, 2008; McCarthy and Prudham, 2004; Bakker, 2009).9

Let us take the example of a catastrophe bond that securitizesthe threat of earthquake damages in California. What exactly isbeing turned into a title that can circulate through financial mar-kets? It is not a title to use, pollute, or harvest natural resources,nor is it an ecosystem service meant to compensate for some deg-radation. It is not a material commodity, service, or a claim on theexchange value of a commodity or service. In fact it is a ‘‘contingentclaim’’, one that pays one amount, but that could pay somethingdifferent in a different state of the world – a world in which anearthquake of a particular strength occurs in California. What laysthe basis for the creation of ILS assets and their exchange is theimagination of the possibility of monetary loss resulting from anearthquake, and the resulting imperative to hedge the exposureto earthquakes on a given company’s balance sheet.

This brings us face to face with options theory, the quantitativefinancial revolution begun with the Black–Scholes–Merton optionspricing formula. Meister has provocatively framed the crucialimportance of options theory as ‘‘the way capitalism rethought itself

9 Although there may be some indirect parallels with the counterfactual work ofstablishing carbon credits accruing from ‘‘avoided deforestation’’ under UN-REDDhemes. Dempsey (2012) also confronts a related problem of valuing the contingent

ossibility of loss.

escp


in the late 20th century. If the kernel of capital was the option formrather than the commodity form, then there seemed to be no mys-tery of surplus value – you were simply in the money’’ (in Dimock(2011, p. 10)).10 The ILS market resolutely operates within this worldof options rather than commodities, using options theory to price catbonds as well as traditional catastrophe reinsurance contracts(Merton, 1998, p. 337). The financial security provided by cat bondsto their sponsors is in essence a ‘‘call option’’ on insurance coverageat the pre-catastrophe price in the post-catastrophe market world.11

Although the technologies of ILS market making unquestionablyrely on a similar ‘‘calculus of measurement and abstraction [that]underlies much of the. . .neoliberalization of nature’’ (Robertson,2011, p. 2), the object of abstraction – contingency – is qualita-tively different. Catastrophe models and the offering documentscirculated to market catastrophe bonds do not set out to createcoherent ‘‘value-bearing abstractions from physical processes’’(Robertson, 2011, p. 2, emphasis mine). Instead, they simulateand abstract the particular liquidity-destroying possibilities of phys-ical processes into their general form, financial risk.

Of course, this sort of intricate (and one might add ‘‘infinite’’)exercise in quantification only operates in the specific register ofmoney. The only assets whose exposure is securitized throughcat bonds are, obviously, those that are insured. Insofar as modelsare able to estimate loss of life, illness, or injury, these quantitiesare relevant only to the extent that these lives are insured (throughlife insurance, workers compensation, etc.). On the other hand, ofcourse, political ecologists have classically shown that the regionsand peoples most devastated by ‘‘natural’’ disasters and faminesare those already in the most economically and physically vulner-able positions, almost entirely without insurance coverage, leavingthem the least able to prepare for and recover from disaster (for in-stance Wisner et al., 2004). The elision of other socially and polit-ically relevant consequences of natural catastrophes thus becomesa matter of course. The possibility of monetary loss, that is to say,financial risk, is the only metric that is logically relevant and com-mensurable to the investors, (re)insurers, and rating agencies whoare the consumers of model results. Or as the quantitative traderand philosopher Ayache has put it quite matter-of-factly: ‘‘In themarket, the only states of the world are prices. . . [and] differentprices are the only thing that can suggest that the actual worldcould have been different’’ (2010, p. 40).

4. Risk as labor’s assets, labor’s assets as liability

‘‘Securitizing [life biopolitically] will in large part be conductedthrough the regulation of life’s exposure to, and its productiveand profitable exploitation of, contingent happenings and effects.’’(Dillon, 2008, p. 315)

This section takes a brief look at a major set of ILS investors –pension funds – to demonstrate how labor’s assets are becomingarbiters and bearers of financial contingency. On the one hand, la-bor’s assets are profiting from and driving the reconfiguration ofthe ‘‘natures of risk’’ through ILS, while on the other hand, the rub-ric of contingency is being extended to evaluate pensioners’ biolog-ical lives and securitize the ‘‘longevity risk’’ that they pose. Aslabor’s livelihoods and life itself become more entangled in the cre-ation and extension of the ILS market, pension funds are key sitesat which new modalities of both biopolitical rule and accumulation

10 There is clearly a great deal more to be said about the recalcitrant question ovalue and securitization of contingent natural phenomena than is possible here (seeCooper, 2010; although they do not consider nature, Bryan et al. (2009) provide abulwark for such analysis.)

11 I am indebted to Bob Meister for pointing this out.

12 This list was primarily compiled from the industry newsletter Trading Risk’s‘‘market data’’ list.

f

are becoming visible – via the regulation of exposure to and prof-itable exploitation of contingency.

4.1. Catastrophe risk as asset: pension plans as investors

The demographic bulge of retirees has motivated pension fundmanagers on the perpetual ‘‘search for yield’’ to seek out the higherreturns promised by catastrophe bonds. Pension funds with publi-cally disclosed ILS investments represent quite significant pools ofcapital with large memberships. They include: the American aca-demic employees fund TIAA-CREF, the Ontario Teachers’ PensionPlan, the Pennsylvania Public School Employee Retirement System,the BBC Pension Scheme, the New Zealand Superannuation Fund,the Danish pension fund manager PKA, the Swedish National Pen-sion Fund AP3, the largest Dutch pension fund APG, and Pimco, aglobal private investment management company that managesretirement accounts for many millions of public sector workers.12

Many more pension funds are invested in dedicated catastrophehedge funds but their names remain private. One of the largest ded-icated cat funds recently announced that it is managing $340 millioninvested by eleven UK pension funds, opining ‘‘double digit returnsagainst very manageable volatility is very attractive [for pensionfunds]’’ (Nephila Capital, 2010). Another catastrophe fund managerprojected that more large pension funds will enter the market inthe coming years, in particular to assume the well-paying risks inpeak peril zones (De Caro, in Aon Benfield (2010, p. 33)). Just a fewmonths later, news broke that State Farm Mutual had issued anew bond in ‘‘an exclusive $250 million trade with a single inves-tor... senior sources [say] that the entire transaction was bought bya single multi-asset manager which is thought to be the Ontario Tea-cher’s Pension Plan’’ (Trading Risk, 2010, p. 4). The notes provideState Farm with indemnity coverage for losses from US and Canadahurricanes, earthquakes, thunderstorms, winter storms, and wildfire.

Two larger global trends within pension management providethe backdrop for funds’ move into ILS. First, pension funds’asset allocations continue to shift away from government bondsand towards alternative assets such as hedge funds, private equity,real estate, and asset-backed securities and derivatives. This pat-tern is motivated by a search for excess returns as funds expand,more members reach retirement age, and pensioners live longerlives. Rising longevity, or the increase in average life expectancy,is typically considered one of the major accomplishments of mod-ern medical science and public health regimes – a triumph of bio-political rule. But pensioners’ increasing longevity also extends theobligation of pension plans to fulfill their promises beyond thenumber of years initially projected. So as funds mature, ‘‘their needto push the envelope of existing investment norms and practicesgrows, resulting in increasingly speculative behavior and the fran-tic search for financial innovations. . . [E]ver more specialized andsophisticated asset categories are demanded and constructed.’’(Engelen, 2003, p. 1366). The dynamic search for yield emergesas a market-based solution to the biopolitical problems of demo-graphic bulge and increasing longevity.

Second, the rise of more complex investment structures andproducts has made fund management more dependent on financialexpertise; investments are increasingly the purview of internal orexternal professional fund managers (Tonks, 2006). As profession-alization has intensified, trustees have become more accustomedto managers using techniques of mathematical portfolio optimiza-tion to maximize returns given a certain risk tolerance (Clark andUrwin, 2008). This has made some boards more amenable toinvestments in asset classes – such as cat bonds – they might have


otherwise considered too risky or opaque. The director of onefund’s tactical assets team interviewed for this research describedhis ILS pitch to his trustee board, made in the high-paying marketafter hurricane Katrina: ‘‘I presented it and they approved. Themath is just very compelling!’’.13

More and more trustee boards and fund management teams arefinding ‘‘the math’’ compelling. The institutional investor category,which includes pension funds, sovereign wealth funds, and foun-dations, accounted for 44% of investors in issuances handled bythe broker-dealer Aon Benfield in the year ended June 30 2011(compared with 31% a year earlier), marking the first time thatthe institutional investors surpassed dedicated catastrophe fundsas the largest block of investors (Aon Benfield, 2011).14 This figureprobably significantly understates the full extent of pension fundinvolvement, since billions of pension fund dollars are invested inhedge funds and dedicated cat funds, which themselves usuallycomprise between a third and a half of the ILS investor pool in anygiven year.

And so it has happened that catastrophe bonds are increasinglypresent – albeit largely invisible barring a major loss – in the pen-sion assets and retirement accounts of workers and state beneficia-ries. For instance, a major public sector pension plan interviewedfor this research in early 2010 had over 250,000 members and$95 billion in assets under management; $1.3 billion of these as-sets were invested in ILS products including over $500 million inpure cat bonds. Given that total cat bonds ‘‘on risk’’ at the timeamounted to less than $15 billion, this meant that nearly four per-cent of the entire global cat bond market’s risk lay in the retire-ment accounts of these public employees. This throws intodramatic relief the geographical and occupational concentrationof these risks and returns, and their (usually unrecognized) rele-vance to everyday lives.

4.2. Longevity – labor’s embodied risk

At the same time that some pension funds are moving assetsinto ILS, the liabilities of many defined benefit (DB) pension fundsthreaten to outweigh their assets, if they do not already.15 This hascrystallized in the peculiar form of longevity risk, which is itselfincreasingly being transferred from private DB plans to capital mar-kets through the securitization of life insurance. Longevity risk refersto the uncertainty surrounding the cash flow profile of future pay-ments to pensioners, since mortality rates have consistently declinedfaster than actuarial models have predicted they would (Turner,2006). For instance, by one estimate, each year of additional lifeexpectancy at age 65 raises the value of UK pension liabilities byat least three percent (Biffis and Blake, 2009).

Longevity risk has been transferred primarily through ‘‘buy-in/buy-out’’ deals in which life insurers acted as intermediaries be-tween pension plans and investment banks and private equityfunds willing to assume the plans’ liabilities. In the mid-2000s,investment banks and private equity funds set up new life insur-ance companies with the express intent of buying out UK corporate

13 ILS is still by no means a mainstream asset class for pension funds. Among thefactors that discourage pension fund investments are lack of trust and/or under-standing in the structure of the bond or safety of collateral, perceived reputationalrisk if a major loss is suffered, and distrust or general disbelief in the validity ofcatastrophe modeling results. The fact that catastrophe modeling is such an integralcomponent of ILS also acts as a barrier to entry for smaller funds without access tomodeling expertise or the resources to license models.

14 To my knowledge, Aon Benfield is the only firm to publish these data; however,interviewees from other firms tended to estimate the same proportional breakdownof market share by investor category as have past Aon Benfield reports.

15 There are highly developed discussions in the economic geography and pensionsliteratures treating the DB crisis, including accounting methods, plan financing, andintergenerational equity (for instance Blackburn, 2006; Clark and Monk, 2007).

DB liabilities. Goldman Sachs even set up its own life insurancecompany to do buy-outs, claiming the issue ‘‘was beginning to im-pede its mergers and acquisitions activities. . .the best way to dealwith pension liabilities was to remove them altogether from thebalance sheets of takeover targets’’ (Blake et al., 2010, p. 135). Thenin 2009, pension plan managers began using longevity swaps tohedge plan risk and the strategy quickly took off.16 By year’s end,longevity swaps with counterparties such as Deutsche Bank, UBS,and Swiss Re totaled £4.1 billion ($6.3 billion). Swaps were projectedto top £10 billion in 2010 as shareholder anxieties about corporatepension obligations mounted and forced corporations to pursue ‘‘der-isking’’ strategies (Hymans Robertson, 2010). Here it is instructive toconsider how pension funds as shareholders may amplify this trend.As institutional investors, pension funds as shareholders and bond-holders have pressured corporate management for the implementa-tion of drastic cost cutting and corporate restructuring (Harmes,1998) and fair-value accounting. Dixon and Monk point out the ironythat ‘‘pension funds as investors are pricing [defined benefit] pensionfunds as corporate liabilities out of existence’’ (2009, p. 626).

Both buy-in/buy-outs and longevity swaps demonstrate thearticulation of life insurance-linked securitization17 with thedynamics of financialization and ‘‘coupon-pool capitalism’’ (Froudet al., 2002). Returning to the metaphor of grammar in Section 3,the ‘‘grammar’’ of longevity risk posits itself as a logical, objective,and apolitical measure based in that most quintessential of statisticalenterprises, demography. In the process, it enables the sort of cross-firm and cross-class comparisons that allow financial markets toprice a firm’s legacy costs (labors’ accumulated entitlements) againstcompeting market opportunities (Monk, 2008; Bryan et al., 2009).

5. Conclusions

This ‘‘search for yield’’ is the form taken by the relative valuationprocesses that commensurate different future asset values and pro-vide us with our unit of measure. In effect, hedge funds and pensionfunds [are] emerging as the custodians of the valuation of capital.They. . .oversee the unit of measurement because they are at thecenter of the commensuration process that binds all asset marketstogether. Once it was gold, then it was central banks. Going forwardit is hedge funds and pension funds. Finance creates the socialworld in the image of capital, and the social world becomes theanchor of finance. (Bryan, 2011, p. 3)

As life expectancies have come to circulate through the sameforums of articulation as earthquake probabilities, ‘‘life’’ and‘‘non-life’’ meet in the capital markets. Here, as Bryan indicates,pension funds and hedge funds occupy center stage as the new‘‘custodians of value’’, commensurating a panoply of contingent fu-tures with the help of scientific expertise and computational mod-eling. To the extent that different segments of labor both bear andembody financial risks – pensioners alternately profit from orthemselves constitute securitized insurance risks – contingency ap-pears as an increasingly important yet ambivalent modality ofaccumulation and rule. What has become of the biopolitical prom-ise of security that insurance is supposed to fulfill? In the midst ofintricate assessments of risk that break down and reassemble thequalities of populations, the built environment, and biogeophysicalprocesses for the purpose of surplus value extraction, doesanything remain in ILS markets of insurance as a technology of sol-

6 A longevity swap is essentially a derivative of a longevity bond in whichayments based on the actual observed survival rates are exchanged for a series ofre-specified fixed payments (Coughlan, 2009, p. 269).7 Although longevity bonds mimicking the structure of cat bonds have not yet

ained market traction despite proposals for government issuance of such notes,ortality rates are very much the subject of financialization in other forms such asrvivor swaps and futures contracts (Coughlan, 2009).

1

pp

1

gmsu


idarity (Simon, 1987) – the ‘‘true popular form’’ that Chauffon cel-ebrates in the epigraph?

As the entire ILS apparatus further advances the financializationof both geophysical nature and life itself, it raises some importanttheoretical questions that remain to be answered. Rather than sim-ply treating ILS markets as incidental ‘‘mystifications’’, we shouldconsider whether they accomplish an even more complete realsubsumption of labor and nature to capital (Marx, 1967). Particu-larly given the ambivalence of contingency with relation to pen-sion funds’ investments and liabilities, what relation do ILSmarkets have to the subsumption of social reproduction? Orperhaps the analytic of subsumption itself requires reworking toaccount for the securitization of contingent futures.

These cases demonstrate the tremendous flexibility and mobil-ity of financialized insurance risk as a conceptual device for joiningontologically and geographically disparate orders under the rubricof contingency. As an ordering that functions through abstractionand commensuration of contingent socionatural futures, it is botha tool of rule and of capital accumulation. The immense efforts tocalculate and abstract financialized insurance risk from contingentgeophysical and biological processes are evident in the continualexpansion of catastrophe modeling into new perils and new re-gions. This parallels the unending search for yield from pensionfunds ‘‘pushing the envelope’’ to find and construct new asset cat-egories. But as the financial crisis of 2008 threw into sharp relief,codifying and valuing exposure to contingency is a fraught andprecarious process. In the attempt to identify new avenues foraccumulation and expand a population’s security, such exercisesmay accomplish quite the opposite. In this regard, it remains tobe seen whether there is a ‘‘true popular form’’ of insurance tobe found in ILS.

Acknowledgments

Tremendous thanks are due to the fund managers, brokers,reinsurance executives, and modelers who shared their time andcandor. I am also grateful to two anonymous reviewers, and to An-drew Baldwin and Anna Stanley for organizing and intellectuallymotivating the session on ‘‘Natures of Risk’’ at the 2010 AAG meet-ings, where this paper was originally presented. Bob Meister, ScottPrudham, Richard Walker, and Michael Watts all provided invalu-able support and feedback on earlier drafts. The Reinsurance Asso-ciation of America, Risk Management Solutions, and EQECAT allgenerously facilitated access to their meetings and Trading Riskgranted invaluable access to their publications. This material isbased upon work supported by the National Science Foundationunder Grant No. 0928711, an earlier NSF Graduate Research Fel-lowship, and the Society for Women Geographers.

References

Amoore, L., De Goede, M., 2008. Governing by risk in the war on terror. In: De Goede,M., Amoore, L. (Eds.), Risk and the War on Terror. Routledge, New York, pp. 5–12.

Aon Benfield, 2010. Insurance-Linked Securities Annual Report: MarketMomentum. Aon Benfield Securities, Chicago.

Aon Benfield, 2011. Insurance-Linked securities Annual Report: Consistency andConfidence. Aon Benfield Securities, Chicago.

Aradau, C., Van Munster, R., 2007. Governing terrorism through risk: takingprecautions, (un)knowing the future. European Journal of InternationalRelations 13 (1), 89–115.

Arrighi, G., 1994. The Long 20th Century: Money, Power and the Origins of OurTimes. Verso, London.

Ayache, E., 2010. The End of Probability. Wilmott magazine, November, pp. 40–44.Baker, T., Simon, J., 2002. Embracing Risk: The Changing Culture of Insurance and

Responsibility. University of Chicago Press, Chicago.Bakker, K., 2009. Neoliberal nature, ecological fixes, and the pitfalls of comparative

research. Environment and Planning A 41, 1781–1787.Baldwin, A., Stanley, A., 2012. Introduction: The Natures of Risk. Geoforum.

Barrieu, P., Albertini, L. (Eds.), 2009. Handbook of Insurance-Linked Securities.Wiley, Chichester.

Beck, U., 1992. The Risk Society: Towards a New Modernity. Sage, London.Beck, U., 1999. World Risk Society. Wiley-Blackwell, London.Bernstein, P., 1998. Against the Gods: The Remarkable Story of Risk. John Wiley and

Sons, New York.Biffis, E., Blake, D., 2009. Mortality-Linked Securities and Derivatives. Discussion

Paper PI-0829. Cass Business School.Blackburn, R., 2006. Age Shock: How Finance is Failing Us. Verso, London.Blake, D., De Waegenaere, A., MacMinn, R., Nijman, T., 2010. Longevity risk and

capital markets: the 2008–2009 update. Insurance: Mathematics andEconomics 46, 135–138.

Bougen, P., 2003. Catastrophe risk. Economy and Society 32, 253–274.Braun, B., 2007. Biopolitics and the molecularization of life. Cultural Geographies 14

(1), 6–28.Brown, W., 2005. Edgework: critical essays on knowledge and politics. Princeton

University Press, Princeton.Bryan, D., 2011. Going Forward: The Perpetual Crisis of Finance. Rethinking

Capitalism Newsletter (1), pp. 1–3.Bryan, D., Martin, R., Rafferty, M., 2009. Financialization and Marx: giving labor and

capital a financial makeover. Review of Radical Political Economics 41 (4), 458–472.

Burton, I., Kates, R., White, G., 1993. The Environment as Hazard, second ed.Guilford, New York.

Castree, N., 2003. Commodifying what nature? Progress in Human Geography 27(3), 273–297.

Castree, N., 2008. Neoliberalising nature: the logics of deregulation andreregulation. Environment and Planning A 40 (1), 131–152.

Chauffon, A., 1884. Les Assurances, leur passé, leur present, leur avenir, Paris.Christophers, B., 2011. Anaemic geographies of financialisation. New Political

Economy, 1–21.Clark, G., 1999. Betting on Lives: The Culture of Life Insurance in England, 1695–

1775. University of Manchester Press, Manchester.Clark, G.L., 2000. Pension Fund Capitalism. Oxford University Press, Oxford.Clark, G.L., Monk, A., 2007. The ‘crisis’ in defined benefit corporate pension liabilities

Part I: scope of the problem. Pensions 12, 43–54.Clark, G.L., Urwin, R., 2008. Best-practice pension fund governance. Journal of Asset

Management 9 (1), 2–21.Collier, S., 2008. Enacting catastrophe: preparedness, insurance, budgetary

rationalization. Economy and Society 37, 224–250.Cooper, M., 2010. Turbulent worlds financial markets and environmental crisis.

Theory, Culture & Society 27 (2–3), 167–190.Coughlan, G., 2009. Longevity risk transfer: indices and capital market solutions. In:

Barrieu, P., Albertini, L. (Eds.), Handbook of Insurance-Linked Securities. Wiley,Chichester.

de Goede, M., 2005. Virtue, Fortune, and Faith: A Genealogy of Finance. University ofMinnesota Press, Minneapolis.

Defert, D., 1991. ‘Popular Life’ and insurance technology. In: Burchell, G., Gordon, D.,Miller, P. (Eds.), The Foucault Effect: Studies in Governmentality. University ofChicago Press, Chicago, pp. 211–234.

Dempsey, J., 2012. Is biodiversity loss a material risk? Tracking the changingmeanings and materialities of biodiversity conservation. Geoforum. 45, 41–51.

Dillon, M., 2008. Underwriting security. Security Dialogue 39 (2–3), 309–332.Dimock, P., 2011. Interview with Robert Meister: Capitalism, Confession, and the

Initiative. Rethinking Capitalism Newsletter (1), pp. 8–12.Dixon, A.D., Monk, A.H.B., 2009. The power of finance: accounting

harmonization’s effect on pension provision. Journal of EconomicGeography 9 (5), 619–639.

Engelen, E., 2003. The logic of funding European pension restructuring and thedangers of financialisation. Environment and Planning A 35 (8), 1357–1372.

Ericson, R., 2008. The State of Preemption: managing terrorism through counterlaw. In: De Goede, M., Amoore, L. (Eds.), Risk and the War on Terror. Routledge,New York, pp. 57–66.

Ericson, R., Doyle, A., 2004. Uncertain Business: Risk, Insurance and the Limits ofKnowledge. University of Toronto Press, Toronto.

Ericson, R., Doyle, A., Barry, D., 2003. Insurance as Governance. University of TorontoPress, Toronto.

Ewald, F., 1991. Insurance and Risk. In: Gordon, C., Burchell, G., Miller, P. (Eds.), TheFoucault Effect: Studies in Governmentality. University of Chicago Press,Chicago, pp. 197–210.

Foucault, M., 1991. Governmentality. In: Gordon, C., Burchell, G., Miller, P. (Eds.),The Foucault Effect: Studies in Governmentality. University of Chicago Press,Chicago, pp. 87–104.

Foucault, M., 2008. The Birth of Biopolitics: Lectures at the College de France 1978–1979. Picador, New York.

French, S., Kneale, J., forthcoming. Speculating on careless lives: annuitising thebiofinancial subject. Journal of Cultural Economy.

French, S., Kneale, J., 2009. Excessive financialisation: insuring lifestyles, enliveningsubjects, and everyday spaces of biosocial excess. Environment and Planning D:Society and Space 27 (6), 1030–1053.

French, S., Leyshon, A., Wainwright, T., 2011. Financializing space, spacingfinancialization. Progress in Human Geography 35 (6), 798–819.

Froud, J., Johal, S., Williams, K., 2002. Financialisation and the coupon pool. Capital &Class 26 (3), 119–151.

Goldin, I., 2010. Lloyd’s 360 Risk Insight: Globalisation and Risks for Business.Lloyd’s, London.


Grossi, P., Kunreuther, H., 2005. Catastrophe Modeling: A New Approach toManaging Risk. Springer, New York.

Guardians of New Zealand Superannuation Fund, 2010. Fact Sheet on CatastropheBonds. Document 103295. New Zealand Superannuation Fund, Auckland, NewZealand. <http://www.nzsuper.co.nz/files/Catastrophe%20Bonds%20Fact%20Sheet.pdf>.

Hacking, I., 1990. The Taming of Chance. Cambridge University Press, Cambridge.Hagedorn, D., Heigl, C., Mueller, A., Seidler, G., 2009. Choice of triggers. In: Barrieu,

P., Albertini, L. (Eds.), The Handbook of Insurance-Linked Securities. Wiley,Chichester, pp. 37–48.

Harmes, A., 1998. Institutional investors and the reproduction of neoliberalism.Review of International Political Economy 5, 92–121.

Harvey, D., 1982. The Limits to Capital. Basil-Blackwell, Oxford.Harvey, D., 1996. Justice, Nature and the Geography of Difference. Blackwell,

Oxford.Hymans Robertson, 2010. Managing Pension Scheme Risk: Buy-Outs, Buy-Ins and

Longevity Hedging, Q4 2009 and 2009 Update. Hymans Robertson LLP, London.Jaffee, D.M., Russell, T., 1997. Catastrophe insurance, capital markets, and

uninsurable risks. The Journal of Risk and Insurance 64 (2), 205–230.Johnson, L., 2010. Climate change and the risk industry: the multiplication of fear

and value. In: Peet, R., Robbins, P., Watts, M. (Eds.), Global Political Ecology.Routledge, London.

Johnson, L., 2011. Insuring Climate Change? Science, Fear and Value in ReinsuranceMarkets. PhD Dissertation. Department of Geography, University of California,Berkeley.

Knights, D., Vurdubakis, T., 1993. Calculations of risk: towards an understanding ofinsurance as a moral and political technology. Accounting, Organizations andSociety 18 (7–8), 729–764.

Krippner, G., 2005. The financialization of the American economy. Socio-EconomicReview 3 (2), 173–208.

Langley, P., 2008. The Everyday Life of Global Finance. Saving and Borrowing inAnglo-America. Oxford University Press, New York.

Lee, R., Clark, G.L., Pollard, J., Leyshon, A., 2009. The remit of financial geography –before and after the crisis. Journal of Economic Geography 9 (5), 723–747.

Lemke, T., 2001. ‘The birth of bio-politics’: Michel Foucault’s lecture at the Collegede France on neo-liberal governmentality. Economy and Society 30 (2), 190–207.

Leyshon, A., Thrift, N., 1997. Money/space: Geographies of MonetaryTransformation. Routledge, London.

Leyshon, A., Thrift, N., 2007. The capitalization of almost everything. Theory, Culture& Society 24 (7–8), 97–115.

LiPuma, E., Lee, B., 2004. Financial Derivatives and the Globalization of Risk. DukeUniversity Press, Durham.

Lobo-Guerrero, L., 2011. Insuring Security: Biopolitics, Security and Risk. Routledge,New York.

Loeys, J., Panigirtzoglou, N., Ribeiro, R., 2007. Longevity: A Market in the Making. J.P.Morgan Securities, New York.

Looney, R., 2004. DARPA’s policy analysis market for intelligence: outside the box oroff the wall? International Journal of Intelligence and Counter Intelligence 17(3), 405–419.

Luhmann, N., 1989. Ecological Communication. University of Chicago Press,Chicago.

Luhmann, N., 2005. Risk: A Sociological Theory. Aldine Transaction, New Brunswick.MacKenzie, D., 2009a. Making things the same: gases, emission rights and the

politics of carbon markets. Accounting, Organizations and Society 34 (3–4),440–455.

MacKenzie, D., 2009b. All those arrows: review of fool’s gold: how unrestrainedgreed corrupted a dream, shattered global markets and unleashed acatastrophe, by Gillian Tett. London Review of Books 31 (12), 20–22.

Martin, R., 2002. The Financialization of Daily Life. Temple University Press,Philadelphia.

Martin, R., 2006. American War and the Financial Logic of Risk Management. DukeUniversity Press, Durham.

Martin, R., Rafferty, M., Bryan, D., 2008. Financialization, risk and labour.Competition and Change 12, 120–132.

Marx, K., 1967 (1867). Capital, vol. I. International Publishers, New York.McCarthy, J., Prudham, S., 2004. Neoliberal nature and the nature of neoliberalism.

Geoforum 35 (3), 275–283.Merton, R.C., 1998. Applications of option-pricing theory: twenty-five years later.

The American Economic Review 88 (3), 323–349.

Mitchell, T., 2007. The properties of markets. In: MacKenzie, D., Muniesa, F., Siu, L.(Eds.), Do Economists Make Markets? On the Performativity of Economics.Princeton University Press, Princeton, pp. 244–275.

Monk, A., 2008. The knot of contracts: the corporate geography of legacy costs.Economic Geography 84 (2), 211–235.

Monk, A., 2009. The geography of pension liabilities and fund governance in theUnited States. Environment and Planning A 41 (4), 859–878.

Nephila Capital, 2010. UK Pensions Allocate $300 m to Bermudan Catastrophe RiskHedge Fund. Press Release, Hamilton, Bermuda (9 March).

O’Malley, P., 2002. Imagining insurance: risk, thrift, and life insurance in Britain. In:Baker, T., Simon, J. (Eds.), Embracing Risk: The Changing Culture of Insuranceand Responsibility. University of Chicago Press, Chicago, pp. 97–115.

O’Malley, P., 2003. Governable catastrophes: a comment on Bougen. Economy andSociety 32, 275–279.

O’Malley, P., 2004. Risk, Uncertainty and Government. GlassHouse Press, London.Palm, R., 1995. Catastrophic earthquake insurance: patterns of adoption. Economic

Geography 71, 119–131.Pearson, R., 2004. Insuring the Industrial Revolution: Fire Insurance in Great Britain,

1700–1850. Ashgate, London.Pike, A., Pollard, J., 2010. Economic geographies of financialization. Economic

Geography 86 (1), 29–51.Quinn, S., 2008. The transformation of morals in markets: death, benefits, and the

exchange of life insurance policies. American Journal of Sociology 114 (3), 738–780.

Randalls, S., 2009. Managing financial risks in urban environments. In: Clark, G.,Dixon, A., Monk, A. (Eds.), Managing Financial Risks: From Global to Local.Oxford University Press, Oxford, pp. 209–229.

Robertson, M., 2006. The nature that capital can see: science, state, and market inthe commodification of ecosystem services. Environment and Planning D:Society and Space 24 (3), 367–387.

Robertson, M., 2011. Measurement and alienation: making a world of ecosystemservices. Transactions of the Institute of British Geographers (OnlineFirst), 1–16.

Rose, N., 1999. The Powers of Freedom. Cambridge University Press, Cambridge.Simon, J., 1987. The emergence of a risk society: insurance, law, and the state.

Socialist Review 95, 61–89.Smith, N., 2007. Nature as accumulation strategy. In: Panitch, L., Leys, C. (Eds.),

Socialist Register 2007: Coming to Terms with Nature. Monthly Review Press,New York, pp. 16–36.

Smith, N., 2008 [1984]. Uneven Development. University of Georgia Press, Athens.Squires, G. (Ed.), 1997. Insurance Redlining: Disinvestment, Reinvestment, and the

Evolving Role of Financial Institutions. The Urban Institute Press, Washington,DC.

Sturm, T., Oh, E., 2010. Natural disasters as the end of the insurance industry? Scalarcompetitive strategies, alternative risk transfers, and the economic crisis.Geoforum 41 (1), 154–163.

Swiss Reinsurance, 2009a. Sigma Report: The role of Indices in TransferringInsurance Risks to the Capital Markets, Sigma. Swiss Reinsurance, Zurich.

Swiss Reinsurance, 2009b. Press Release: Swiss Re Obtains USD 75 Million ofExtreme Mortality Risk Protection Through New Vita Capital Programme. SwissReinsurance, Zurich.

Taleb, N., 2007. The Black Swan: The Impact of the Highly Improbable. RandomHouse, New York.

Thompson, C., 2005. Making Parents : The Ontological Choreography ofReproductive Technologies. MIT Press, Cambridge, MA.

Tonks, I., 2006. Pension fund management and investment performance. In: Clark,G., Munnell, A., Orszag, M. (Eds.), The Oxford Handbook of Pensions andRetirement Income. Oxford University Press, Oxford.

Trading Risk, 2010. Secretive $250mn Merna Re III Sold to Single Fund, Trading Risk.Insider Publications, London, p. 4.

Turner, A., 2006. Pensions, risks, and capital markets. Journal of Risk and Insurance73 (4), 559–574.

Wasow, B., Hill, R., 1986. The Insurance Industry in Economic Development. NewYork University Press, New York.

Willis, 2012. ILS Market Update: Strong Close to Year Pushes 2011 Issuance Volumeto Over $4 Billion. Willis Capital Markets and Advisory, Chicago.

Wisner, B., Blaikie, P., Cannon, T., Davis, I., 2004. At Risk: Natural Hazards, People’sVulnerability and Disasters. Routledge, New York.

Zaloom, C., 2004. The productive life of risk. Cultural Anthropology 19 (3), 365–391.Zelizer, V., 1979. Morals and Markets: The Development of Life Insurance in the

United States. Columbia University Press, New York.

http://www.nzsuper.co.nz/files/Catastrophe%20Bonds%20Fact%20Sheet.pdf

http://www.nzsuper.co.nz/files/Catastrophe%20Bonds%20Fact%20Sheet.pdf

Date post:	05-Jan-2017
Category:	Documents
Upload:	leigh
View:	212 times
Download:	0 times

Catastrophe bonds and financial risk: Securing capital and rule through contingency

Documents