The palgrave handbook of unconventional risk transfer

THE PALGRAVE HANDBOOK
OF UNCONVENTIONAL
RISK TRANSFER
Edited by Maurizio Pompella and Nicos A Scordis


The Palgrave Handbook of Unconventional
Risk Transfer


Maurizio Pompella  •  Nicos A Scordis
Editors

The Palgrave
Handbook of
Unconventional
Risk Transfer


Editors
Maurizio Pompella
School of Economics and Management
University of Siena
Siena, Italy

Nicos A Scordis
Tobin College of Business
St. John’s University
New York, New York, USA

ISBN 978-3-319-59296-1    ISBN 978-3-319-59297-8 (eBook)

DOI 10.1007/978-3-319-59297-8
Library of Congress Control Number: 2017947702
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To Giuseppe


Foreword

There will always be risk. It is one of the global economy’s certainties, alongside
such self-evident ones as death and taxes. For this reason alone, a volume covering
any aspect of risk transfer is welcome, but a book that addresses unconventional risk

transfer is rarer in the field of economic literature and hence still more welcome.
There is much fascinating detail in this book. Perhaps a few words of context,
within the confines of a foreword, may help set the scene for the reader. Risk has
always been with us it is true, but equally true is the fact that there are those who
wish more, rather than less, of it. This makes the market. Consider that every
time someone deals in an instrument as ubiquitous as the humble Eurodollar
contract, that transaction represents the coming together of two parties with
diametrically opposing views. One person’s risk exposure is another’s risk opportunity. Of course the conventional or “vanilla” methods of risk transfer are more
than suitable for a majority of the world’s participants in finance, energy, commodities, weather and other “asset classes”. But often large risk exposures that
cannot be mitigated using vanilla methods but yet cannot for one reason or
another be left unhedged require unconventional methods if they are to be dealt
with. And if one can find a ready and willing counterparty, it is inevitable in a
free market that these methods will be developed and pursued.
In 2002, I was working in structured finance at JPMorgan Chase Bank
when one of the deals we brought to the market was what we thought the
world’s first synthetic Collateralised Debt Obligation (“Robeco CSO”), which
utilised credit default swaps to transfer risk via a pooled vehicle; this was an
unconventional risk transfer but at the same time an investment product, and
the technology became quite commonplace within a year or two. One year’s
unconventional risk management approach is next year’s routine transaction.
(In fact I believe a firm called Dolmen Securities beat us to that “world’s first”
vii


viii  Foreword

title with a transaction called “Blue Chip CDO”, but unfortunately that deal
did not feature in Risk magazine like our one did!).
This is a welcome feature of a globalised free market environment, where innovation thrives and, once in a while, produces genuine benefits for society. There
will always be innovation in finance, some of it very useful and some of it merely

navel gazing, but in the whole, such practice produces value. To use an obscure
analogy from the world of military aviation, for every Supermarine Spitfire there
was first a Boulton Paul Defiant. It is a truism that only very rarely does one arrive
at the quality product without sampling some duds along the way.
The asset classes described in this book are many and varied, and often it is the
more esoteric products that call for unconventional methods to be applied. This
is understandable when one has a paucity of market players. This is a specialised
business, but often dealing in very important areas. Without the unconventional
approaches to risk management noted in this book, one would risk inefficiencies
in production and delivery, with consequent knock-­­on impact on the customer.
So it is to be welcomed that innovation in risk transfer is something that, nearly
ten years after the global financial crash of 2008, remains to the fore.
All good textbooks should present a solution as well as the problem. I was
particularly impressed to see the dissection of various approaches to structuring
risk transfer across different product types, which forms the bulk of the latter
parts of the book. I am sure this material will be of value to practitioners. But
irrespective of one’s own background, for all true students of risk management,
be they in the finance, insurance, weather or energy industry or elsewhere, this
Handbook is a worthwhile addition to the economics literature. The editors are
to be commended for their work in bringing to our attention this collection of
leading-edge thinking in the exotic world of unconventional risk transfer.
Kent Business School, University of Kent
Canterbury, UK
30 January 2017

Moorad Choudhry

Moorad Choudhry  is the former CEO of Habib Bank AG Zurich and was
previously the Treasurer of Royal Bank of Scotland (RBS) Corporate Banking,
Europe Arab Bank and KBC Financial Products. He has over 30 years’ experience in the city and began his career at the London Stock Exchange. Choudhry

is a visiting professor at the University of Kent Business School, where he teaches
on the MSc Finance programme. He is a fellow of the Chartered Institute of
Securities & Investment and of the London Institute of Banking and Finance.
He obtained his MBA at Henley Business School and his PhD from Birkbeck,
University of London. He is the author of The Principles of Banking (2012).


Contents

1Introduction   1
Maurizio Pompella and Nicos A. Scordis

Part I  Risk Management Strategies and Perspectives

   5

2A Theoretical Perspective on Risk Management   7
Richard Friberg
3A Practical Perspective on Corporate Risk Management  35
Nicos A. Scordis and Annette Hofmann

Part II  Conventional vs Unconventional Transfer

  55

4Reinsurance, Insurability and the New Paradigms
of Unconventional Risk Transfer  57
Maurizio Pompella
5Enterprise Risk Management and the Risk Management
Process 109

Greg Niehaus

ix


x  Contents

6Credit Risk Transfer with Single-Name Credit Default
Swaps 143
Christopher L. Culp, Andria van der Merwe, and
Bettina J. Stärkle

Part III  Risks by Class

 187

7Natural Hazards 189
Joanna Faure Walker
8Anthropic Perils and Man-Made Risks 241
Gordon Woo
9Mortality and Longevity Risk 269
Erzsébet Kovács and Péter Vékás
10Country Risk: Case Study on Crises Examples and 
Lessons Learnt   299
Vasily Solodkov and Yana Tsyganova

Part IV Vulnerability, Market Solutions and Societal
Implications

 327

11Disaster Vulnerability 329
Joern Birkmann, Linda Sorg, and Torsten Welle
12Insurance-Linked Securities: Structured and Market
Solutions 357
Annette Hofmann and David Pooser
13Longevity Risk Transfer 375
Douglas Anderson and Steven Baxter


 Contents 
  

Part V  Risk Modelling and Stress Testing

xi

 435

14Quantitative Man-Made Risks’ Modelling 437
Gordon Woo
15Pandemic Risk Modelling 463
Dominic Smith
16Assembling Individual ILS into an Optimal Portfolio 497
Morton Lane
17Stress Testing with Bayesian Nets and Related Techniques:
Meeting the Engineering Challenges 537
Riccardo Rebonato
Index 577

List of Figures

Fig. 2.1
Fig. 2.2
Fig. 2.3
Fig. 2.4
Fig. 2.5
Fig. 2.6
Fig. 2.7
Fig. 2.8
Fig. 3.1
Fig. 4.1
Fig. 4.2
Fig. 4.3
Fig. 4.4
Fig. 4.5
Fig. 4.6
Fig. 4.7
Fig. 4.8
Fig. 4.9
Fig. 4.10
Fig. 4.11
Fig. 4.12
Fig. 4.13
Fig. 4.14
Fig. 4.15

Linear profits
9

The case of strictly concave profits
12
Profits under flexibility—strictly convex profits
13
An example of real options: profit function for a firm that
mothballs production if s <  s 14
Hedging with derivatives—a stylized example
20
Operational hedging—a stylized example
22
Flexibility—a stylized example
26
Managing liquidity—a stylized example
27
An illustration of different risk optimization decisions
resulting from different risk metrics
44
Quota share reinsurance
60
Surplus share reinsurance
61
Excess of loss reinsurance
61
Iterative risk filtering process
69
Basic securitization process
71
Triggering trade-off
75
Life settlement process

77
Life settlement payoffs
78
Life settlement profit sharing
79
Population pyramids (males and females), low- and high-income
countries. Source: World Bank
80
Traditional risk routing
83
Industrial enterprises risks
86
Steps in the Enterprise Risk Management process
87
Risk and residual uncertainty costs
90
Enterprise value with and without risk92

xiii


xiv 

List of Figures

Fig. 4.16 (a) Pooling and exposure limits. (b) Franchise deductible
structure. (c) Vanishing deductible
94
Fig. 4.17 New way of transferring risk
99

Fig. 4.18 Risk warehousing and financial intermediation approach
101
Fig. 5.1 A visual representation of differences in standard deviation
112
Fig. 5.2 Two possible outcomes for workers’ compensation costs
114
Fig. 5.3 An illustration of assumptions
118
Fig. 5.4 A visualization of a firm’s ERM programme
132
Fig. 5.5 An illustration of the concept of risk appetite
137
Fig. 5.6 A trade-off between risk and expected return
138
Fig. 6.1 Semi-annual notional amounts of all types of CDSs outstanding 147
Fig. 6.2 BIS-reported totals and compressed trading volumes
149
Fig. 6.3 Weekly gross notional amounts outstanding
150
Fig. 6.4 Gross notional amounts outstanding of single-name CDSs
by type of reference entity
153
Fig. 6.5 Gross notional amounts outstanding of single-name CDSs
based on corporate and sovereign reference entities
154
Fig. 6.6 Summary of credit ratings
156
Fig. 6.7 Total gross notional amounts of CDS protection bought
158
Fig. 7.1 Basic structure of composite volcanoes and shield volcanoes.

Figure adapted from Faure Walker (2016a)
200
Fig. 7.2 Different categories of landslide. Figure adapted from Faure
Walker (2016b)
206
Fig. 7.3 Anatomy of a cyclone. Figure adapted from Faure
Walker (2016c)
212
Fig. 7.4 Direction of most intense winds. Figure shows example of a
westward travelling cyclone in the northern hemisphere.
Figure adapted from Faure Walker (2016c)
212
Fig. 7.5 Flood hydrograph shows how water flow in a river channel
responds to rainfall. Note the shape of the hydrograph depends
on the particular characteristics of the channel
225
Fig. 9.1 Life expectancy at birth by World Bank region (1960–2014,
both sexes; data source: World Development Indicators,
World Bank, />Fig. 9.2 The hierarchical bottom-up solvency capital calculation
approach and the place of biometric risks in life and pension
insurance according to Solvency II
274
Fig. 9.3 Mean log-mortality rates in the Lee–Carter model
(England and Wales, ages 65–100, years 1961–2011)
278
Fig. 9.4 Past and forecasted values of the mortality index in the
Lee–Carter model along with their 95% confidence band
(England and Wales, ages 65–100, years 1961–2011)
279

  List of Figures 
  

Fig. 9.5
Fig. 9.6
Fig. 10.1
Fig. 10.2
Fig. 10.3
Fig. 10.4
Fig. 10.5
Fig. 10.6
Fig. 10.7
Fig. 11.1
Fig. 11.2
Fig. 11.3
Fig. 11.4
Fig. 11.5
Fig. 11.6
Fig. 11.7
Fig. 12.1
Fig. 12.2
Fig. 12.3
Fig. 13.1
Fig. 13.2
Fig. 13.3

Age-specific sensitivities in the Lee–Carter model (England and
Wales, ages 65–100, years 1961–2011)
Heat map of residuals in the Lee–Carter model (England and

Wales, ages 65–100, years 1961–2011)
Thailand macroeconomic overview before and after the
crisis of 1997. Source: World Bank, database: World
Development Indicators
USD/THB official exchange rate, inflation and interest rate.
Source: World Bank, database: World Development Indicators
Impact of currency board on Argentina’s economy.
Source: World Bank, database: World Development Indicators
Argentina’s Macroeconomic Statistics: 1991–2002. Source: IMF
statistics by country
The dynamics of USD/RUB exchange rate and oil prices
during 1998–2009. Source: Bank of Russia web page,
World Bank economic monitoring
EU budget expenditures and contributions by member
countries in 2007. Source: European Commission
EU budget expenditures and contributions by member
countries in 2015. Source: European Commission
The double structure of vulnerability (by Bohle 2001)
The progression of vulnerability (based on Wisner et al.
2004: 51)
Vulnerability within the hazard, exposure, capacities and
disaster risk nexus (based on Davidson 1997; Bollin et al. 2003)
The MOVE framework (see in detail Birkmann et al. 2014)
IPCC framework for systematising hazard, exposure,
vulnerability and risk (IPCC 2014)
Structure and indicators of the Urban Risk Index (based on
Birkmann et al. 2016)
Nexus—urban vulnerability, level of urbanisation and urban
growth (Birkmann et al. 2016b)
Broad overview of the ILS market

Structure of CAT bonds
Basis risk, credit risk, and moral hazard of catastrophe
financing tools
Profile of deaths by age for UK men and women aged
65 in 2014 (Club Vita)
Lifetimes for UK men and women aged 65 in 2014
(Club Vita)
Cashflows from an illustrative annuitant portfolio
(Hymans Robertson LLP)

xv

279
282
308
309
314
315
317
319
320
338
339
340
342
345
347
349
359
361

363
378
379
384


xvi 

List of Figures

Fig. 13.4 Smoking rates for UK adult men by socio-economic
classification (SEC) (Hymans Robertson LLP)
Fig. 13.5 Illustration of range of mark-to-model views (Hymans
Robertson LLP)
Fig. 13.6 Breakdown of longevity risk by source (Hymans Robertson LLP)
Fig. 13.7 Age profile of different longevity subrisks (Hymans
Robertson LLP)
Fig. 13.8 Prevailing assumptions for future longevity trends (Hymans
Robertson LLP)
Fig. 13.9 A social history of longevity (Hymans Robertson analysis,
based on UK population figures)
Fig. 13.10 The heart of a life insurer (Hymans Robertson LLP)
Fig. 13.11 Conceptual grouping of longevity trend models (Hymans
Robertson LLP)
Fig. 13.12 Structural stochastic models (Hymans Robertson LLP)
Fig. 13.13 Convergence of life expectancy outcomes between
socio-economic groups 2000–2010 (Club Vita)
Fig. 13.14 Longevity-related transactions originating from large
corporate pension funds in the UK (Hymans Robertson (2016);
note that 2016 relates to first 6 months only)

Fig. 13.15 (a) Capital dynamic for single-risk insurer (Hymans Robertson
LLP). (b) Capital dynamic with a diversifying risk (Hymans
Robertson LLP). (c) Capital dynamic with a negatively correlated
risk (Hymans Robertson LLP). (d) Capital dynamic with a
negatively correlated risk and “optimal” mix (Hymans Robertson
LLP). (e) Capital dynamic with negatively correlated risk and
optimal business model (Hymans Robertson LLP)
Fig. 13.16 Parties and cashflows involved in an indemnity swap (Hymans
Robertson LLP)
Fig. 13.17 Parties and cashflows involved in an index-based swap (Hymans
Robertson LLP, differences versus indemnity swap shown by
underlined text)
Fig. 13.18 Evolution of longevity trading market (2002–2016)
Fig. 13.19 Framework for assessing basis risk (Hymans Robertson (2014),
reproduced with permission of the Institute and Faculty of
Actuaries and the Life and Longevity Markets Association)
Fig. 13.20 (a) Barriers to longevity hedging (Hymans Robertson LLP
online survey of pension scheme trustees and their advisors,
June 2016). (b) Willingness to cede longevity risk (Hymans
Robertson LLP online survey of pension scheme trustees and
their advisors, June 2016)

386
387
389
390
391
392
394
395

396
398
400

406
412
412
416
421

424


  List of Figures 
  

xvii

Fig. 13.21 Breakeven inflation (Hymans Robertson graphic based on
data from Bank of England (.
uk/statistics/Pages/yieldcurve/archive.aspx) as of
January 31, 2017)
425
Fig. 13.22 Five practical steps to a deep and liquid longevity risk
trading market
427
Fig. 13.23 Protected cell structures
429
Fig. 15.1 Deaths from communicable diseases and other causes since
2000 (World Health Organization 2016)

464
Fig. 15.2 Proportion of global disease burden in respect of DALYs in
2015. Communicable diseases are separated into “infectious
and parasitic diseases” and “respiratory infectious” together
accounting for 19.1% of DALYs (World Health
Organization 2016)
465
Fig. 15.3 Deaths by cause in 2015 by World Bank classification of
economies (World Health Organization 2016)
465
Fig. 15.4 A simple SIR model. Susceptible members of the population
are infected at a rate corresponding to the force of infection, λ.
Infective people recover at a rate γ corresponding to the rate
of recovery
474
Fig. 15.5 Simple deterministic SIR and SEIR models for smallpox,
displaying populations of each compartment relative to the total
population476
Fig. 15.6 Results of the SIR model incorporating demographic
stochasticity for a single infective introduced to a community of
100 people
477
Fig. 15.7 A schematic of a metapopulation model and a network model
of infectious disease spread
478
Fig. 15.8 Annualised age-specific mortality rates for deaths attributed to
influenza or pneumonia per 100,000 cases in the USA for
1911–1917 and 1918. Notice the peak in mortality rates among
young adults. Source: CDC (Taubenberger and Morens 2006)
481

Fig. 15.9 Example of a structure of a probabilistic pandemic model
485
Fig. 16.1 ILS deals outstanding in “market” portfolio on
September 30, 2014 (9/30/2014). Grouped by peril and type
and ordered by original expected loss (EL), excess to remodelled
EL. N.B. AIR remodelling premiums and discounts to original
ELs. The entire figure is displayed in two parts
501
Fig. 16.2 Market risk profile. The left panel is based on 10,000 scenarios.
The right panel is based on the collapsed 20 scenarios
504
Fig. 16.3 Optimum solution only risk constraints—two deals
511
Fig. 16.4 Optimum solutions risk profile—two deals
512
Fig. 16.5 Optimum solution with limit of 10% of any deal
514


xviii 

List of Figures

Fig. 16.6 Risk profile for the optimal solution with limits of 10% of any
deal517
Fig. 16.7 Peril composition of optimum solution with limits of 10% of
any deal
518
Fig. 17.1 The Bayesian net Salviati received from Mephistopheles
548

Fig. 17.2 The Bayesian net associated with the John-slipping-on-path
scenario. A represents the probability of rain tomorrow;
B represents the probability of the sprinkler being on;
C denotes the probability of the pavement being wet; and
D is the probability of John slipping
552
Fig. 17.3 The effects of uncertainty in the inputs and in the outputs.
See the text for a description
554
Fig. 17.4 A measure of ‘distance’ between joint distributions (on the
y axis) and the rank of the input conditional probability
(x axis, in ascending order)
554
Fig. 17.5 A measure of ‘distance’ between joint distributions (on the
y axis), and the rank of the input conditional probability
(x axis, in ascending order)
555
Fig. 17.6 Bayesian Net
564
Fig. 17.7 Modification of Bayesian Net in Fig. 17.6
565


List of Tables

Table 4.1
Table 6.1
Table 7.1
Table 7.2
Table 7.3

Table 7.4
Table 7.5
Table 7.6
Table 7.7
Table 7.8
Table 9.1
Table 10.1
Table 10.2
Table 13.1
Table 13.2
Table 13.3
Table 14.1
Table 15.1

A taxonomy of risks and actors
Coupon payments for a one-year CDS on XYZ Corp.
with a 100 bp coupon and $25 mn notional amount
Ten worst earthquake events in terms of fatalities 1900–2016
(EM-DAT 2016)
Volcanic explosivity index (Newhall and Self 1982; Dorling
Kindersley Publishing Staff 2011)
Ten worst volcanic events in terms of fatalities 1900–2016
(EM-DAT 2016)
Tropical cyclone seasons by ocean basin
Fatalities from individual tropical cyclones
Enhanced Fujita Scale (descriptions taken directly from
NWS 2017; wind speeds are from Enhanced Fujita Scale
Recommendation Report 2006)
Mean number of tornadoes per month in the USA observed
between 1991 and 2010

Worst 10 floods between 1900 and 2016 in terms of fatalities
Assumed correlations among submodules of the life
underwriting risk module under Solvency II
Country risk sources
Country risk sources applicable to Asia, Russia, Argentina
and Great Britain
Longevity risk components
Diluting longevity risk via investment opportunities
Basel Committee recommendation for the development of
an orderly LRT market
The likelihood of a terrorist plot being interdicted
Pandemics since the middle ages

82
160
197
202
205
211
216
218
219
227
275
301
322
388
403
419
445

467
xix


xx 

List of Tables

Table 15.2 Transmission modes and zoonotic origins of infectious
diseases472
Table 15.3 Basic reproduction numbers of historical influenza pandemics
and other epidemic diseases (Taubenberger and Morens 2006;
Valleron 2010; Elderd et al. 2006)
475
Table 15.4 Comparison of the functional components of pandemic models
versus natural catastrophe models
488
Table 16.1 Overview of market portfolio (i.e., all outstandings by peril or
region)500
Table 16.2 Data sheet (partial) illustrating required inputs and
co-measures506
Table 16.3 Optimum solution with just risk constraints—solution on
two deals
510
Table 16.4 Optimal solution with limit of 10% of any deal versus
the market
516
Table 16.5a Optimum solution marginal values limit to 10% any deal
case—single peril listing
519

Table 16.5b Optimum solution marginal values limit to 10% any deal
case—multi-peril listing
521
Table 17.1 The correlation delivered by Mephistopheles at time T546
Table 17.2 The second instalment, delivered at time T + Δt, of the
Faustian bargain
547


1
Introduction
Maurizio Pompella and Nicos A. Scordis

The insurance industry has well demonstrated its ability to package risk for
sale to capital markets. In 1771 risk underwriters doing business in Edward
Lloyd’s coffee house joined in the Society of Lloyd’s. At its most basic, the
Society operated by packaging risk into syndicates which then sold layers of
that risk to individual investors. The appeal to investors, however, was limited
since the practice of the syndicates was to require additional payments, should
claims for losses exceed what investors originally bargained for. Beginning in
the late 1980s, however, insurers figured out how to package risk for sale to
investors in an attractive way. Insurers begun raising capital by selling debt to
investors collateralized by receivables on the insurer’s balance sheet. Insurers
call such instruments insurance-linked securities (ILS). Monarch Life (now
defunct) in 1987 was the first insurer to collateralize receivables from its
annuity policies, followed by General American (now a subsidiary of Met
Life), and then Prudential with a $445 million issue backed by policyholder
loans. American Skandia (acquired by Prudential in 2003) in 1997 issued
securities backed with fees from a cohort of variable life policies. In 1996
Hannover Re issued the first catastrophe bond (CatBond) soon followed by

a handful of other insurers with a dozen or so issues. At their most basic,
CatBonds is debt whose coupon (and even principal repayment) is indexed

M. Pompella (*)
University of Siena, Siena, Italy
N.A. Scordis
St. John’s University, New York, NY, USA
© The Author(s) 2017
M. Pompella, N.A. Scordis (eds.), The Palgrave Handbook of Unconventional
Risk Transfer, DOI 10.1007/978-3-319-59297-8_1

1


2 

M. Pompella and N.A. Scordis

to the intensity of a naturally occurring event. At the end of March 2016, the
outstanding market for CatBonds and other ILS instruments was $26.5 billion worth, with $2.2 billion of new capital issued from ten transactions in
the first quarter of 2016.
The growth in this unconventional risk transfer market is partly the result
of the great changes and crisis most economies have experienced over the past
20 years. We use the term ‘risk transfer’ to indicate that the managers of a firm
pay an external group of investors to take on the firm’s risk. We use the term
‘risk retention’ to indicate that the managers of a firm keep the risk within the
firm and thus implicitly the firm’s own investors pay for the risk. Therefore,
in our view, both risk transfer and risk retention are components of the firm’s
risk financing consideration.
The use of the term ILS is relatively recent (as far as the short history of

the unconventional risk transfer market goes). The early market used to refer
to the process of issuing ILS as securitization. The practice of ‘securitization’,
that is bundling future cash flows from similar risks into portfolios for sale to
investors, originated among investment bankers who applied the technique to
finance risk ‘off balance sheet’. Off balance sheet because at the inception of
the technique, the prevailing accounting regulations did not address how to
record the transaction, which allowed some leeway. The spread of securitization into the insurance and other sectors of the economy represents a period
of cross-sector or ‘diagonal’ risk transfer.
As a result, the traditional channels for trading risk made room for a series
of alternative channels that feed directly to investors in the capital markets,
many of whom are unrelated to the insurance industry. The insurance industry plays a central role in the success of these alternative channels by pushing
the development of dynamic models for analysing the probability and severity of an event. These models draw on seismology, meteorology, engineering,
actuarial, statistical and financial science. They deal with issues of simulating low-frequency events, quantifying associations among risks and balancing
the internal cleverness of the model with how well its results reflect reality.
While many insurers maintain their own models, they all begin the process
of pricing risk from vendor-provided models (from mostly Risk Management
Solutions (RMS), AIR Worldwide (AIR), or EQECAT). These models are
complex computing devices that are updated regularly with granular data by
postal code and in some cases even by smaller geographic divisions. The widespread use of vendor models has created a standard point of reference for
pricing risk, which legitimized, to investors, the purchase of ILS. The legitimization of ILS pricing further strengthened the legitimacy of models, which
perpetuates the legitimacy of ILS. The refinement of vendor models creates a


1 Introduction 

  3

general understanding that unpredictable risk can be converted into ­tradable
deals which can be compared with other deals for pricing and placement
with investors. Thus, even though each tradable ILS is unique in terms of

the underlying risks it encapsulates, all ILS are delineated in a consistent way.
It is challenging to parse the evolution of the risk markets from conventional insurance-only to today where insurance-centred and alternative, or
unconventional risk transfer markets coexist. There is a convergence between
different types of financial intermediaries, but their boundaries are fuzzy.
Indeed, regulations clearly segregate types of risk products, but the ideas and
processes that support the pricing, marketing and trade of these risk products
are impossible to allocate to respective risk products.
The title of this work refers to ‘unconventional risk transfer’. Conventional
applications of risk transfer almost always involve the use of the insurance
mechanism. The risk capacity of the insurance mechanism, however, as large as
it is, is still small in comparison with the risk capacity of the broader financial
and capital markets. This work frames and contextualizes the latest techniques
and strategies that are used to unlock the risk transfer capacity of the global
financial and capital markets. This work adds value to the literature because it
presents core topics that either allow unconventional applications of conventional risk transfer practices or enhance directly the pricing of unconventional
risk transfer practices. This duality in the works included in the Handbook is
what sets this publication apart. This Handbook is a collaborative work that
brings together experts from different disciplines and countries. On purpose,
each expert uses the version of English spelling that they feel most comfortabe
with. Also on purpose the Handbook preserves instances where different contributors discuss an identical concept from their discipline’s perspective. Such
spelling and instances are few in between as not to distract the reader, but they
do serve to underline the multi-disciplinary and multi-country nature of this
collaboartion.
The first two contributions set a framework for risk and its management,
from a conceptual and a practical perspective, respectively. Both of these contributions point out two challenges when dealing with risk. One, while individual risks can be measured, the interpretation of whether operating at a
given level of risk is prudent ultimately depends on the decision maker’s view
of the environment. Two, even though risks interact with each other and the
environment, there is insufficient ability to quantify such risk associations.
The contributions that follow—in Sect. II, which explores the origins of
unconventional risk transfer; Sect. III, which looks at risks according to their

class of origin; and Sect. IV, which examines society’s tolerability to risk, market solutions and the social implication that arise—provide context for indi-


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M. Pompella and N.A. Scordis

vidual risk transfer techniques and/or strategies. The last part of the work,
Sect. V, focuses on potential solutions to quantifying risk. The reader will
notice, however, that almost all of these contributions (directly or indirectly)
confront the two overarching challenges identified earlier in this work: How
to interpret risk and how to capture the interdependencies of risk.
The last two contributions, particularly, provide a solution to these two
overarching challenges. They both deal with bundling individual risks into a
portfolio and point the way forward. They both offer practical advice. One
of the contributions examines how to assemble a portfolio of risks that maximizes return without taking ‘excessive’ risk. The other contribution shows
why application of Bayesian nets lends itself particularly well to the need for
a practical way to stress test a portfolio of risks.
Maurizio Pompella  is Full Professor of Financial Intermediaries Economics at the
University of Siena, School of Economics and Management (SEM), Italy. He has
been a researcher, lecturer, senior lecturer and associate professor since 1991, and
is the Dean of MSc in Economics and Management of Financial Intermediaries.
Certified as a stand-by professor at the LUISS—Guido Carli in Rome in 2014,
he serves as an adjoint professor at Charles University in Prague (CZ) since 2012,
OMSU (Ogarev Mordovia State University of Saransk, RF) from 2015 and SibSU
(Siberian State University of Krasnoyarsk, RF) from 2016. He served as a book
reviewer for the Journal of Risk and Insurance, published by ARIA (American Risk
and Insurance Association), and contributed to the ARIA Newsletter. Pompella has
been teaching banking, finance and insurance at graduate and post-graduate levels in
Italy, Eastern Europe, Latin America, the Middle East, Russia and China. His areas

of expertise include insurance economics, banking and monetary economics, finance,
structured finance, alternative risk transfer, financial innovation and stability, and
project financing.
Nicos A. Scordis  is a professor at the School of Risk Management, Insurance and
Actuarial Science (SRM) at St. John’s University in New  York. His research helps
insurance managers judge the risk/uncertainty/value dynamic in their evolving operations. When he served as the chairperson of the SRM, he founded the Masters
in Risk Management and Insurance. He was sought out by the US Congress for
expert testimony on financial services integration. Scordis has a BSc in Insurance
from Florida State University, an MBA from the University of Georgia and a PhD
from the University of South Carolina.


Part I
Risk Management Strategies and
Perspectives


2
A Theoretical Perspective on Risk
Management
Richard Friberg

2.1 Introduction
The value of a firm and its profits are typically subject to a large number of
shocks. Prices of raw materials, actions by competitors and regulators, natural
disasters and geopolitical shocks all have the potential to hurt a firm. Managing
such risks is of key importance to many firms. In this chapter we will provide
a theoretical foundation for understanding why risk management can add
value to a firm and also characterize different means of managing risk—for
instance exploring hedging with derivatives and operational hedging.

The relevant literature is immense and parts of it quite technical—by
necessity our survey is therefore limited in scope. We try to present a broad
and encompassing view of the motivations for, and means by, which risk,
broadly construed, can be managed. We strive to survey the relevant theoretical literature with a light hand and give some references to indicate the
flavour of empirical work. To aid intuition we present the results in an essentially static setting, where we can equate firm profits with the value of the
firm.
We commence Sect. 2.2 with an analysis of whether a firm should manage risk or not. One view, often associated with Modigliani and Miller
(1958), stipulates that there is no reason for firms to manage risk and that
it is better that investors put together a portfolio that best represents their
R. Friberg (*)
Stockholm School of Economics, Stockholm, Sweden
© The Author(s) 2017
M. Pompella, N.A. Scordis (eds.), The Palgrave Handbook of Unconventional
Risk Transfer, DOI 10.1007/978-3-319-59297-8_2

7


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R. Friberg

preferences towards risk. This is indeed a key insight and it remains an
important backdrop for understanding when risk management by a firm is
motivated.
In public discussions there has been an increasing emphasis on the fact that
the future is becoming harder to predict and terms like “unknown unknowns,”
ambiguity and black swans are increasingly part of our vocabulary. In Sect.
2.3 we distinguish between risk on the one hand and uncertainty on the
other, in a distinction that dates back at least to Knight (1921). We define

risk as randomness that follows a probability distribution whereas uncertainty
pertains to randomness that cannot be described by a probability distribution.
Making a forward-looking decision is clearly much harder if we cannot rely
on probability distributions. While some may see this form of uncertainty as
an analytical dead end, we believe that the distinction is valuable, in particular
for understanding the limits of financial contracts as a means of managing
uncertainties.
Thus having set the stage, we turn to different ways of managing risk in
Sect. 2.4. We first examine hedging with derivatives and insurance, which is a
natural starting point and the focus of much theoretical work in risk management and corporate finance. The empirical evidence indicates that derivatives
are only one of the many ways used to manage risk, however. We therefore
proceed and explore less conventional ways of managing risk: operational
hedging, investing in flexibility and ensuring access to liquidity and to new funds.
We conclude in Sect. 2.5.
The central role that we assign to Knightian uncertainty in presenting the whys and hows of risk management is a bit unconventional but
hopefully the reader will agree that it provides a useful prism. A booklength treatment following the same approach is found in Friberg (2015):
Managing Risk and Uncertainty: A Strategic Approach and the present survey can in many ways be seen as a summary of this area as presented in
the book.1

2.2 Why Should a Firm Manage Risks?
We commence with the question of why a firm should manage risk. While
similar arguments can be made for the case of uncertainty, they are more
transparently illustrated for the case of risk, and risk that can be captured
by a probability distribution is also the case that the academic literature has
focused on. We therefore take this as a starting point.