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NABE�Annual�Meeting: October�13�16,�2012Bridging�the�Gap�Between�Finance�and�the�Real�Economy�
Computational�Economics�and�Big�Data:�How�Technology��s���Transforming�Business�Decision�Making
David K.A. MordecaiRisk Economics, Inc.
1 Copyright ©2012 Risk Economics, Inc.
Computational��conomics
• An�emergent algorithmic�paradigm�involving�– distributed�
social– social– adaptive– procedurally�rational�(learning,�search),�and�
inherently Bayesian inference– inherently�Bayesian�inference
• Generative�social�simulation�re:�carrying�capacity�and�complexityResource competition cooperation and CPRs (common pool resources)– Resource�competition,�cooperation,�and�CPRs�(common�pool�resources)
• e.g.�liquidity,�credit�• Sustainability,�fragility,�stability• Neo�institutional�in�nature• Hebbian learning,�search,�and�complexity• Interactions�between�heterogeneous,�autonomous�agents�(Neo�Keynesian?)• Sparsity�vs.�parsimony
2 Copyright ©2012 Risk Economics, Inc.
Seminal�Trends
• Pervasive,�mobile�adaptive�social�computing�and�sensing– Urbanization– Rental/leasing�markets
• Service�flows�vs.�Ownership�(asset�stocks)• Co�location�and�microtransactions
• Fundamental�changes�in�commerce�and�finance– consumer�credit�and�insurance
• Payment�systems�and�ecurrency (e.g.�Bitcoin)
3 Copyright ©2012 Risk Economics, Inc.
Emergent�Institutional�and�Market�Determinants
• Monopolistic�Competition– Supply/Demand�elasticities– Dominant�oligopolies�with�competitive�fringe– Market�contestability
• Microstructure• Auctions�and�(real)�options�“imbedded�everywhere”�(ubiquitous,�pervasive)• Herds,�fads,�fashions�(social�contagion�and�cultural�propagation;�norms)• Curated life�experiences,�events�trajectories• CPRs�and�network�externalities• Lock�in and�path�dependence�(increasing�returns)
– Product�liability�and�warranty�(legal,�insurance)�implicationsE id i l i t l t i l• Epidemiology,�environmental�toxicology
• Evidentiary�(discovery,�causation,�liability�rules,�damages,�etc.)
4 Copyright ©2012 Risk Economics, Inc.
Tools�and�Methodologies
• Data�Science– Scalable�Bayesian�inference�(with�sparsity)
ll l– Large� vs.�small� samples– Spatio�temporal�dynamics– Unstructured�data
• Crowdsourcing,�Machine�Learning,�Agent�Based�Models– Coordination�and�mechanism�design– Experimental�economics– Activity�Recognition– Social�network�analysis�(peer�effects)y (p )– Discrete�choice�and�hedonic�pricing�(e.g.�market�baskets�re:�
preferences,�utilities,�elasticities)
5 Copyright ©2012 Risk Economics, Inc.
Copyright�©�2011�Risk�Economics,�Inc. 12
Urbanization�will�result�in�changing�dynamics�(e.g.�unemployment,�crime)6 Copyright ©2012 Risk Economics, Inc.
Agent�Based�Modelling
�
Realistic�“ground�up”�models�of��real�world�strategic�interactions,�structural�shifts,�and�complex�evolving�dynamics���
7 Copyright ©2012 Risk Economics, Inc.
Demographics,�Sociometrics and�Psychometrics�matter
��
Actively�monitoring�consumer�behavior,�lifestyle�choices,�and�aggregated�preferences�at�the�right�level�of�granularity�can�be�predictive�of�both�short� and�long�tailed�loss�development.
8 Copyright ©2012 Risk Economics, Inc.
Is there an Analogue for Casualty Underwriting?
Source:�Deloitte�Consulting
9 Copyright ©2012 Risk Economics, Inc.
Case�in�Point:�Correspondences�Between�Business�Conditions,�Unemployment,�and�Insurance�claims
��
2,000
2,500
3,000
3,500
4,000
4,500
5,000
5,500
6,000
6,500
7,000
200
300
400
500
600
700
800
900
1000
10/00
04/01
10/01
04/02
10/02
04/03
10/03
04/04
10/04
04/05
10/05
04/06
09/06
03/07
09/07
03/08
09/08
03/09
09/09
03/10
09/10
Graph�1:�Initial�Claims�vs�Continuing�Claims
RecessionsInitial�ClaimsInitial�Claims�4Wk�MAContinuing�ClaimsCont�Claims�4Wk�MA
250
300
350
400
450
500
550
600
650
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100
104
Graph�2:�Initial�Claims��(000s)
Current Expansion 2001 Expansion1991 Expansion 1982 Expansion1975 Expansion
2,000
2,500
3,000
3,500
4,000
4,500
5,000
5,500
6,000
6,500
7,000
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100
104
Graph�3:�Continuing�Claims��(000s)
For�example,�there�are�currently�unexploited�techniques�for�analyzing�statistical�relationships�between�changing�unemployment�insurance�claims�relative�to�workers�compensation�and�disability�claims.�
Source:�NBER,�ETA�and�Risk�EconomicsTM calculations10 Copyright ©2012 Risk Economics, Inc.
Disability�versus�Earnings��
��
Relative�earnings�of�permanent�partial�disability�claimants�as�a�proportion�of�earnings�of�comparison�workers�in�five�states
SOURCE:�Robert T.�Reville,�Leslie I.�Boden,�Jeffrey E.�Biddle,�and�Christopher�Mardesich,�An�Evaluation�of�New�Mexico�Workers'�Compensation�Permanent�Partial�Disability�and�Return�to�Work,�MR�1414�ICJ�(Santa�Monica,�CA:�RAND�Institute�for�Civil�Justice,�2001)
What�this�graph�does�not�tell�you�is�that�disability�claims�increase�during�periods�of�high�unemployment,�i.e.�poor�business�conditions.�In�other�words,�disability�and�workers�compensation�may�be�serving�as�a�substitutes�or�supplements�to�unemployment�insurance.
Question:�what�specific�industry�and�occupational�characteristics�may�be�predictive?
11 Copyright ©2012 Risk Economics, Inc.
Socio�Informatics:�Network�Analysis,�Data�Mining�and�Text�Mining
� 31
These�analytic�methods�are�applicable�to�many�risks:�Energy�and�power�and�natural�resource�markets;�Social�systems�and�integrated�modeling�of�interactions�between�natural�and�social�processes;�national�security�topics,�e.g.�distributed�adaptive�network�control�and�terrorist�networks;�supply�chain�dynamics;�biological�systems,�including�pandemics;�industrial�and�macroeconomic�structures�(trade�and�capital�flows);�other�geopolitical,�socioeconomic,�legislative,�regulatory,�commercial,�financial�market�and�policy�issues.
Source:�CI�Chicago
12 Copyright ©2012 Risk Economics, Inc.
Text�Mining�has�Forensic�Implications�for�Tort,�Litigation,�and�Claims�Settlement
13 Copyright ©2012 Risk Economics, Inc.
`
The Federal Reserve Bank of New York, November, 2009
Short-Term Funding Short- to Long-Term Cash
MMDAs
Households, Businesses, Governments CDs
Equity Funding Long-Term Investments
Equity Equity
`
Equity
Short-Term Debt InstrumentsRegulated Money Market Unregulated Money Market
Agency MBS Agency Discount Notes Intermediaries Intermediaries
*Conforming mortgages RRs
Other*
Liquidity Puts A1 *ARS, MMMFs
A1 CP
AAA ABCP
AA-BBB BDP
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A1 *ARS, MMMFs, as well as A1
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AA-BBB RRs RRs Short-Term Savings
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CP
ABCP
BDP
RRs
Other*
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Equity Repo Equity Supers CNs* ABCP BDP
LTD Haircuts BDP RRs
Households Equity O/C High-Yield CLOs RRs Ultra-Short Bond Funds Other* Households and Nonprofits
(LBO Loans) Other* A1 *MMMFs
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2nd Lien Equity RRs
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an Banks
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MMIFF
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Governments RRs (Sovereign Wealth Funds)
Equity O/C Subprime ABS Other* *Done by custodian banks
(2nd lien, subprime, HELOCs) Trading Book *MMMFs, MTNs, term ABS and on an agent basis.
A1 TOBs and VRDOs
AAA
AA-BBB
Equity
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CMBS and High-Yield CLOs A1 in exchange for investibe FX
(LBO Loans) CP Long-Term Savings
A1 ABCP
AAA BDP Fixed Income Portfolios*
AA-BBB RRs
Equity Equity Other* *Done by real money accounts
*MMMFs, MTNs, term ABS and on a principal basis.
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*Bank, Shadow Bank
and Corporate Debt
Consumer ABS
(Card, Auto, Student Loan)
CP A1
AAA Cash
AA-BBB MTNs
Equity LTD Equity Portfolios*
Haircuts Equity
Equity O/C Other ABS
(Floorplan, Equipment, Fleets)
A1 AAA *Mutual Funds, ETFs, Separate Accounts
AAA AA-BBB *Bank and Corporate Debt
AA-BBB MTNs
Equity CNs*
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ABCP (Loans to SMEs) Cash
`
LTD AA-BBB Equity
Equity Equity Equity
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debt and equity tranches
Public Equity
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MTNs
LTD
Structured Credit Equity
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*Hedge Funds and Private Equity
(only credit exposures)
TSLF/Maiden Lane LLC
3/11/08 and 3/16/08, respectively
Portfolio Protection*
EquityHouseholds, Businesses, Governments
Households, Businesses, Governments Equity and the Rest of the World (RoW)
Equity
AA-BBBEquity Equity
Source: Shadow Banking (Pozsar, Adrian, Ashcraft, Boesky (2010))
Debt
Tri-Party Repo System
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prime
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Commercial Bank*
Step (7): Wholesale Funding
(Shadow Bank "Depositors")
ABS Reserves ABCP
Equity
Counterparty Hedges
Synthetic Credit Liabilities
Counterparty Hedges
3/11/08 and 3/24/2008, respectively 11/10/2008
*Funded by UST's $50 billion
No
Explicit
Fees
GSEs, DoE, SBA Federal Government
Agency Debt PurchasesAgency MBS Purchases
Funded Synthetic CDOs*
Consumers
Finance Company*
Fannie and Freddie*
Credit Hedge Funds
Conceptualized, designed and created by Zoltan Pozsar ([email protected])
Debt
Insu-
rance
TLGP
Warehouse Hedges
Counterparty Hedges
Protec-
tion Sold
Insurance
Guarantees
Step (4): ABS Warehousing
Finance Company*
The "Synthetic" Shadow Banking System
Broker-Dealers*
Wh
ole
sale
Fu
nd
ing
(Ter
m D
ebt
Fun
din
g)
Long-Term Synthetic Liabilities Broker-dealer CVA desks, ABS pipeline hedges,
Warehouse Hedges
Single-Name and Index
CDS Indices
Single-Name and Index
Assets Bond(s)
Sovereign CDS
Proprietary trading desks, credit hedge funds, etc.
Term
Savings
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AA-BBB
Synthetic Exposures*
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Bets
Term
Savings
Hedgers*
(Credit-Linked Notes)
Deposits
Bank Equity
Real Money Accounts*
Equity Portfolios
Money Market "Portfolios"
Loans
Th
e T
rad
itio
nal
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kin
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yst
em
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rigin
ate-
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old
-to
-Mat
uri
ty-a
nd
-Fun
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ith
-Dep
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ts")
unfunded liabilities, etc.
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CDOs
Unfunded
Protection
Referencing ABS and single-name CDS *Referencing single-name CDS indices, etc. *Inability to meet
Treasurys
indices, etc.
CDS
AAA AAAAAA
[…]
Equity
Loan(s)
Structured Credit and Loan
negative basis traders, real money accounts, etc.
Speculators*
Unfunded
Protec-
tionProtec-
tion Sold
*Market Makers
Funded
Protec-
tion
"Naked"
Positions
Funded
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n*Referencing corporate loan indices, etc.
*Hedging Motives
Insured
Assets
*Speculative Motives
Warehouse Hedges
Loans,
ABS
and
CDOs
Unfunded Synthetic CDOs*
Protec-
tion
Bought
Ultimate Creditors
Bond(s) CDS
Unfunded
Protec-
tionProtec-
tion Sold
[…]
Warehouse Hedges
Pri
vate
Ris
k R
ep
osi
tori
es
Th
e "
Syn
theti
c"
Sh
ad
ow
Ban
kin
g S
yst
em
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ivat
ives
-Bas
ed R
isk R
epo
sito
ries
)
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Funded
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Ultimate Borrowers Corporate CDS CDPCs CPDOs* Short-Term Synthetic Liabilities
Warehouse Hedges Warehouse and
Counterparty Hedges
AAALoans,
ABS
and
CDOs
9/19/2008 10/21/2008
Reserves
ABCP
10/7/2008 11/25/2008 10/11/2008 12/12/2007 12/12/2007
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used
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vate
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dit
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nsf
orm
ati
on
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l R
isk A
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on
)
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ion
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k C
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ital
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vate
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k R
ep
osi
tori
es
*Unaffiliated with originators!
Credit Insurance
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Cre
dit
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ges
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Credit
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Insurance Premia
Credit
InsuranceClient
Funds
Cre
dit
"R
efe
ren
ces"
Mortgage Insurers* Monoline Insurers*
*Unaffiliated with originators! *Unaffiliated with originators!
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eral
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po
nse
s
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had
ow
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k B
acks
tops)
Equity
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Senior
Credit Insurance Credit Insurance
[…]
MTNs
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and Life and P&C Insurance Companies
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(A4)
*Public and Private Pension Funds,
Debt
Tranches
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Structured Credit Portfolios*
Term
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d Credit
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Client
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Pension
Liabilities
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A1
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e "
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tern
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ad
ow
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kin
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ecia
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: O
rigin
ate-
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d M
od
el |
DB
Ds:
Ori
gin
ate-
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istr
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te M
od
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-Bal
ance
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eet
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Structure
d Credit
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t S
pecia
list
s' C
red
it I
nte
rmed
iati
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Pro
cess
Standalone Finance Company Credit Hedge Fund
Loans
Loans
*Finance company affiliate
[…]
Term
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ole
sale
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nd
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ium
-Ter
m F
un
din
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-Bal
ance
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eet
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High-Grade CDOs
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ments
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ole
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rop
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ad
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rnal"
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-Bal
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-Bal
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"Equity"
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SharesBDPs
BDPs
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AssetsLoans
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$1 NAV
Shares
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Shares$1 NAV
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eet
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Shares
MunisDiscount
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-Bal
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Sh
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Ultimate Borrowers CMOs Direct Money Market Investors Ultimate Creditors
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Agency
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nse
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acks
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red
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nte
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iati
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(Time-Tranched Agency MBS)
Federal Government
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MBS
The "Cash" Shadow Banking System Deposit Insurance
(FDIC)
Liability Insurance
(Federal Government)
Credit Insurance
(GSEs, DoE, SBA)
Liability Insurance
(Federal Government)
Liability Insurance
(EU Government)
11/25/2008
Agency
MBSReserves
Deposit Insurance
(FDIC)
MMMF
Insurance
Agency Debt Purchases
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bli
c R
isk
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osi
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bli
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red
it T
ran
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(Tai
l R
isk A
bso
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)
European Sovereign and
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11/25/2008
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Dollar
Deposits
Eq
uit
y F
un
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Discount Window
Loan
Collateral
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DebtReserves
Term
Savings
*Asset Managers, Insurance Companies and
Bank
Equity
LGIPs
Loans
Exchange Stabilization Fund
$1 NAV
Shares
Insurance
Premia
Pu
bli
c R
isk
Rep
osi
tori
es
(Tai
l R
isk A
bso
rpti
on
)
Corporate Treasurers
A1
CP
Households, Businesses
Checking
Account
and the Rest of the World
Equity
Asset Managers*
Bank Treasurers
Bank
Equity
$1 NAV
Shares
Savings:
Excess
Cash
Savings:
Excess
Cash
The Traditional Banking System
CDs
Ultimate CreditorsDepositors
Deposits
Step (1): Loan Origination
TBA
Market
Step (3): ABS Issuance
Short-
Term
Savings
Implicit
Insurance
9/18/2008
MMMF Guarantee
Loans
Ultimate Borrowers
Reserves
11/25/2008
No
Explicit
Fees
Implicit
Insurance
Deb
t F
un
din
g
(Sh
ort
an
d L
on
g-T
erm
Dep
osi
ts
Implicit
Insurance
Assets
Borrowers
Loans
Asset Manager*AAA
Loans
Checking
Accounts
Wh
ole
sale
Fu
nd
ing
(Sh
ort
-Ter
m F
un
din
g)
AA-BBB Arbitrage Conduit
No
Explicit
Fees
Implicit
Insurance
Step (5): ABS CDO Issuance
Loans*
(ABS Warehouse Conduits)
Structuring
and
Syndication
Single-Seller Conduit
(Warehouse and Term)
Loans ABCP
(Warehouse and Term)
Loans
Loans
Brokered
DepositsAAA
Loans
Discount
Notes
Step (2): Loan Warehousing
Agency
Debt
Equity
Structuring
and
Syndication
Loans
Subprime, RMBS, CMBS
[…]
Mezz
ABS
Hybrid Conduits
*FHC affiliated
Short-
Term
Munis
Off
-Bal
ance
Sh
eet
Off
-Bal
ance
Sh
eet
SIV
*Capital Notes
Maiden Lane II LLCMMIFFAMLF
Trading Books
Catalogue:,
Shadow Bank LiabilitiesStep (6): ABS "Intermediation"
Trading Books
Repo Conduits
ABCP
CDOs
Loans
Bonds
Assets
"Prime" Homeowners
*DBD affiliate
Loans
Single-Seller Conduit
Multi-Seller Conduit* Broker-Dealer*
Multi-Seller Conduit*
The Shadow Banking System
Loans
Bonds
("Rent-a-Conduit")
Broker Sweep Accounts
Single-Seller Conduit
ABS
and
CDO
Equity
Loans
ABS
Loans
ABS
Equity
Hybrid Conduits
ABCP
CP
14 Copyright ©2012 Risk Economics, Inc.
15 Copyright ©2012 Risk Economics, Inc.
Cross-Border Financial Surveillance: A Network Perspective (IMF Working Paper 105 , April 2010 ) by Marco Espinosa-Vega and Juan Sole
16 Copyright ©2012 Risk Economics, Inc.
A network analysis of global banking:1978-2009 (April 2011) by Camelia Minoiu and Javier A. Reyes
Does “God” Play With Fuzzy Dice?A stochastic game is a dynamic game played by one or more players with probabilistic transitions, such that at each stage
the probability distribution for that new random state depends on the previous state and the actions chosen by the players
Representation of a finite-state machine (example): an FSM that determines whether a binary number has an odd or even number of0’s. FSM can be non-deterministic (i.e. stochastic). Also state machines can be infinite (i.e. continuous)
on the previous state and the actions chosen by the players
Probabilistic parameters of a Hidden Markov model What if certain parameters (example of the simplest dynamic Bayesian framework):x states, y possible observations;a state transition probabilities, b output probabilities
f p(e.g. transition states) are fuzzy, time-varying, etc.?
Model underlying the Kalman filter. Squares represent matrices. Ellipses represent multivariate normal distributions (with the mean and covariance matrix enclosed). Unenclosed values are vectors. In the simpleUnenclosed values are vectors. In the simplecase, the various matrices are constant with time, and thus the subscripts are dropped, but the Kalman filter allows any of them to change each time step 0
17 Copyright ©2012 Risk Economics, Inc.
A stochastic game is a dynamic game played by one or more players with probabilistic transitions, such that at each stage
g y g p y yp y p g
the probability distribution for that new random state dependsp y f pthe previous state and the actions chosen by the playersthe previous state and the actions chosen by the players
Bayesian Graphical Models
A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional d d i i di t d li h (DAG)dependencies via a directed acyclic graph (DAG).
Bayesian networks that model sequences of variables are called dynamic Bayesian networks. Generalized Bayesian networks that can represent and solve decision problems under uncertainty by combining probabilistic inference and utility maximization are called decision networks,
l d fl d Effi irelevance diagrams, or influence diagrams. Efficientalgorithms exist that perform inference and learning in Bayesian networks.
A Markov random field (“MRF”), Markov network or undirected graphical model: a graphical model in which a set of Markov random variables are described by an undirected graph and is similar to a Bayesian network in its representation of dependencies An MRF canvariables are described by an undirected graph, and is similar to a Bayesian network in its representation of dependencies. An MRF canrepresent certain dependencies that a Bayesian network cannot (such as cyclic dependencies), but cannot represent certain dependenciesthat a Bayesian network can (e.g. induced dependencies). The classic MRF is the Ising model a statistical model of ferromagnetism
A log-linear model is a Markov random field with feature functions fk such that the full-joint distribution can be written as shown with partition function z, and where Xis the set of possible assignments of values to all the network's random variables
18 Copyright ©2012 Risk Economics, Inc.
A Markov random field (“MRF”), Markov network or undirected graphical model:
Generalized Bayesiany y ynetworks that can represent and solve decision problemsp punder uncertainty by combining probabilistic inferencey y g pand utility maximization are called decision networks,y
l d fl drelevance diagramsdd , or infln uence diagramsdd
A Bayesian network
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