Economics Department
Centre for Computational Finance and Economic Agents
Kiel Institute for the World Economy:6th Kiel Institute
Summer School on Economic Policy Policy,June 24 –
30, 2012
Professor Sheri Markose
Economics Department (University of Essex)
Multi-Agent Financial Network Models (MAFN) For
Systemic Risk Management: A New Complexity
Perspective
Lecture 2: Design of Robust Macro-prudential Policy:
Why a new complexity approach and MAFNs ?
scher@essex.ac.uk
The software used in network modelling was developed by
Sheri Markose with Simone Giansante and Ali Rais
Shaghaghi
Recap : Flavour of ICT (Information and Communication
Technology) based data driven MAFN models
• Mark Buchanan (Aug 2010,Nature) : “A screen maps the world's
largest financial players — banks, governments and hedge funds —
as well as the web of loans, …and other legal claims that link them.
High-powered computers using these enormous volumes of data run
through scenarios that flush out unexpected risks. ..This morning
they trigger an alarm....
• Flashing orange alerts on the screen show a cluster of US-based
hedge funds has unknowingly taken large positions in similar assets.
If one of the funds should have to sell assets to raise cash, the
computers warn, its action could drive down the assets' value and
force others to start selling their own holdings in a self-amplifying
downward spiral. Many funds ..bankrupt within 30 minutes,
…threatening the entire financial system. ..
• Financial authorities step in to orchestrate a controlled elimination of
the dangerous tangle.”
Unfortunately, such enabling technologies of advanced ICT
economies, have yet to be harnessed for economic
analysis and systemic risk monitoring
IBM MIDAS project (Balakrishnan et. al. 2010) and the EC grant FP6 -034270-2
project of Markose and Giansante (see ACEfinmod.com) are software technologies
being developed for (US centric) large scale firm level financial database driven
models for systemic risk analysis
Reserve Bank of India April 2011- April 2014 Project automating bilateral financial
data with MAFN visualization and monitoring (Consultant Markose/Giansante)
Agent based computational economics or ACE (acronym coined by Leigh Tesfatsion,
Tesfatsion and Judd, 2006 ) is based on object oriented programming that can
produce agents that are both inanimate (eg. repositories of data bases) as well as
behavioural agents capable of varying degrees of computational intelligence. These
range from fixed rules to fully adaptive agents representing real world entities (such as
banks or consumers) in artificial environments which can be replicas of, for instance,
the financial system.
New computational and simulation based methodologies to track bank balance sheet
and off balance sheet activity of financial intermediaries in response to changes in
regulatory policy and also due to competitive co-evolutionary pressures to grow
market share.
Challenges not just economists/regulators behind
technology curve:Deep foundational issues on reflexivity,
strategic innovation missing in mainstream policy design
Extant macro- economic or monetary models for policy show absence of: endemic
arms race of strategic gaming by regulatees which includes innovation, race to the
bottom and maladaptive compliance when regulation yields perverse incentives.
Econometric models cannot handle structural interconnections and interactions
between economic units. Paradigm shift and skills gap involved in implementing and
utilizing such large scale data base driven computational simulators to analyse
financial networks stability and to conduct ‘what if’ analysis will be highlighted.
Axelrod (2003) system failure in networks arises from :“coevolution is not anticipated”.
We need understanding on why policy can be rendered ineffective and the need for
policy makers to co-evolve with regulatees requires addressing foundational issues on
strategic behaviour. For this we need to go beyond traditional game theory and
espouse a complexity perspective (markets as complex adaptive systems by Markose
(2005)).
Obvious need to reverse lax attitude toward design of robust regulatory policy
framework and the need to stress testing policy both prior to implementation and to
Regulatory Arbitrage and Peverse Incentives of
Policy : Eichengreen (2010, also Hellwig, 2010) concluded “fundamentally, the (2007) crisis is the result of flawed regulations and perverse
incentives in financial markets”.
Emphasizing how individually rational activity of financial institutions aimed at
expanding their loan market share will undermine system stability, Jones
(2000), from the Division of Research and Statistics of the Board of
Governors of the Federal Reserve System:
“absent measures to reduce incentives or opportunities for regulatory capital
arbitrage, over time such developments could undermine the usefulness of
formal capital requirements as prudential policy tools”.
Jones noted regulatory capital arbitrage has attracted scant academic
attention, or for that matter as a key aspect of regulatory design, and appears
to think that this is due to a lack of sufficient time series data which impedes
econometric analysis of regulatory capital arbitrage.
If econometric models are not up to the task of modelling regulatory capital
arbitrage due to limited data points, are there no other tools to test bed
regulatory systems?
Advantages of ACE Models v. Macro-Econometric
Models
In ACE models, the artificial environments can depict real time orientation,
institutional rules, and also complex interactions.
•For simulation framework to be useful for assessment of policy, financial firm
level responses must be modelled with prevalent market conditions and with
automated access to balance sheet and off- balance sheet data to anchor the
financial decisions. Financial interconnections are empirically determined by data
bases can give structural snap shots of the situation without needing large time
series that statistical and econometric models need.
•Further, the interactions of agents produce system wide dynamics that are not
restricted to pre-specified equations which have to be estimated using past data
in econometric or time series approaches. In ACE model, each agent follows
explicit rules or strategies under specified market conditions and a ‘probe’
monitors causal internal workings and also aggregates outcomes. I will demo
that testing for perverse outcomes if agents followed the policy due to incentives
is relatively easy in ACE models: simply program them to implement the
conditions of the policy and check quarter after quarter if simulated balance sheet
data corresponds to actual data
Blundell-Wignall and Atkinson (2008) quite rightly state: “understanding
causality is a precondition for correct policy making”
Above authors say this in their attempt to assess the impact of the Basel II incentives
for capital reduction by banks and the CDS negative basis carry trade for the critical
build up in 2006-7 of RMBS and CDS on US banks’ balance and off balance sheets
that brought the US financial system to the brink of collapse.
While they bring a wealth of evidence on regulatory incentives for the acceleration of
RMBS assets on banks’ balance sheets, they do not attempt to develop a
methodological framework to study causality.
Extant statistical and econometric models fail to identify the threats to stability from
such incentives for capital arbitrage among financial firms that lead to topological
fragility of the CDS based risk sharing institutions.
Lecture 2:Roadmap
2007 financial crisis result of deep
doctrinal errors : Policy framework devoid of institutions,
policy incentives, defaults and most financial data bases
• Part I: (i) “Steadfast refusal to face facts” (Goodhart,
2009 Economica) (i.a) Gary Gorton COEVOLUTIONARY TRANSFORMATION OF BANKING
WHILE REMAINING A FRACTIONAL SYSTEM for
which central banks responsible and tax payer is
liable: Since Henry Thornton private securitized
forms of leverage will suffer convertibility problems
to regulated monetary base
• (i.b)New Inflationary transmission mechanism in CPI
index needed with ICT based payment substitutes
for M0 in advanced cashless economies: The
inflation conundrum
Lecture 2:Roadmap contd Complexity Perspective:
Reflexivity and Lucas Critique
• Part II: Inadequate modelling tools for policy
design with co-evolving uber intelligent private
sector : How did central bank policy get reduced
to a simple rule for inflation targetting to the
exclusion of all else?
• Part III Demo : How a multi-agent financial
network (MAFN) model is well suited to monitor
bank activity and to stress test policy for
perverse incentives on an ongoing basis.
Part I : Challenges to Robust Policy Modelling “Steadfast
refusal to face facts” (Goodhart, 2009 Economica)
• Transformations in the financial and
monetary landscape : Not acknowledged
and modelled in fine grained detail
• Gorton and Metrich (2009) outdated models of money
and banking prevented central banks and supervisory
bodies from seeing the $12 trillion procyclically sensitive
collateralized securities in the repo and shadow banking
system as being part of the fractional system of private
credit creation which will suffer convertibility problems
vis-à-vis central bank regulated funds and reserves for
which the tax payer remains liable.
Money Supply with Securitization , Money Demand and Inflation
State Supplied
Monetary Base
M0(Notes and coins)
M0 Increased by
Quantitative Easing
Expands
and
contracts
with
business
cycle;
CRISIS
OCCURS
WHEN
CONVERTIBILITY
FROM
INSIDE
MONEY
TO M0 IS
AT
STAKE
Broad Money
‘Inside Money’
Produced by
Credit /Leverage
Based on
Discounting of
private securities
SECURITIZAT
-ION
INCREASES
SCOPE OF
PRIVATE
DEBT
SHADOW
BANKING
SYSTEM
M0 Demand for
Transactions/Expenditure
Shrinking in advanced
cashless economies
Dramatic fall in Inflation
of Consumer Price Index
Anglo-Saxon
Financial
systems prone
to asset and
house price
inflation as
M0 no longer
channelled to
transactions
demand and
CPI inflation;
Otherwise we
would have a
Weimar
Republic
scenario with
all the
quantitative
easing
Cash Hoarding
Added (5) Regulatory Incentives from
Credit Risk Transfer & Risk Weighting
Which Marries Balance Sheet Items with
Offbalance sheet Derivatives Assets
& Derivatives Liabilities
Source : Gorton and Metrick (2009)
Low inflation in advanced cashless
economies: major conundrum marks the
2007 financial crisis
•Why the threats from private sector leverage
or loose monetary conditions
engineered by authorities were obscured and continue to be so.
Following the legacy of double digit inflation, from the 1980’s onwards macro and
monetary stability policy in advanced economies, was aimed at inflation control.
Concerted efforts, especially in the UK and the ECB to restrict the central bank by
statute, to focus entirely on inflation control and in that a fixed rule in the UK,
has led many to conclude that despite interest rate policy that was unabashedly
loose in the US and UK, the so-called ‘great moderation’ of low inflation can be
attributed to good ‘helmsmanship’.
The absence of inflationary overheating was equated with the absence of macrofinancial instability. Few have noted that ICT innovations in payments technology
having reduced the use of state supplied money or M0 in retail transactions
(Humphreys et. al. 1996, Markose and Loke, 2003) may have curtailed the capacity
Table: interest rates and inflation rates for selected EU and G10 countries
SOURCE: Sheri Markose and Yiing Jia Loke (2002)
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Average
Jap.
Dep.rate
Infl.rate
3.56
2.98
4.14
3.32
3.35
1.76
2.14
1.22
1.7
0.70
0.9
-0.10
0.3
0.10
0.3
1.70
0.27
0.69
0.12
-0.29
1.68
1.21
USA
Dep.rate
Infl.rate
8.1
5.28
5.7
4.32
3.52
3.02
3.02
2.93
4.2
2.64
5.84
2.77
5.3
2.90
5.62
2.33
5.47
1.61
5.33
2.15
5.21
3.00
Fin.
Dep.rate
Infl.rate
7.5
6.15
7.5
4.12
7.5
2.57
4.75
2.19
3.27
1.02
3.19
1.01
2.35
0.60
2
0.40
1.88
2.18
1.80
1.16
4.17
2.14
Fra.
Dep.rate
Infl.rate
4.5
3.34
4.5
3.24
4.5
2.38
4.5
2.01
4.56
1.76
4.5
1.73
3.67
2.00
3.5
1.18
3.21
0.68
2.69
0.58
4.01
1.89
Ger.
Dep.rate
Infl.rate
7.07
2.63
7.62
1.63
8.01
5.05
6.27
4.48
4.47
2.30
3.85
2.25
2.83
1.40
2.69
1.87
2.88
0.97
2.43
0.58
4.81
2.32
Italy
Dep.rate
Infl.rate
6.8
6.53
6.64
6.26
7.11
5.05
7.79
4.46
6.21
4.05
6.45
5.26
6.49
4.00
4.83
2.02
3.16
1.98
1.61
1.66
5.71
4.13
Port.
Dep.rate
Infl.rate
13.99
13.48
14.8
11.32
14.59
8.89
11.06
6.77
8.37
4.92
8.38
4.17
6.32
3.10
4.56
2.13
3.37
2.85
2.40
2.31
8.78
5.99
Spain
Dep.rate
Infl.rate
10.65
6.58
10.47
6.05
10.43
5.83
9.63
4.59
6.7
4.71
7.68
4.71
6.12
3.60
3.96
1.93
2.92
1.80
2.43
2.33
7.10
4.21
UK
Dep.rate
Infl.rate
Dep.rate
Infl. rate
12.54
9.44
8.20
5.94
10.28
5.91
7.50
5.00
7.46
3.68
6.89
3.58
3.97
1.61
5.56
3.09
3.66
2.44
4.69
2.40
4.11
3.41
4.79
2.44
3.05
2.40
3.57
1.92
3.63
3.22
3.13
1.72
4.48
3.41
2.97
1.46
3.88
1.56
2.62
1.36
5.71
3.71
Average
Data: International Monetary Fund, International Financial Statistics Yearbook 2000
Average inflation in G10 countries fell to
2.5% by 1994 :Predates inflation targets
• Few have noted that ICT innovations in payments
technology having reduced the use of state supplied
money or M0 in retail transactions (Humphreys et. al.
1996, Markose and Loke, 2003) may have curtailed the
capacity of monetary authorities to engineer inflation
which had fallen to under 2.5% by 1994 in advanced
cashless economies (see, Marimon et. al.,1997).
• In the 1970’s Hayek said that a permanent brake on
state engineered inflation requires that we substitute
away from state supplied money in transactions. How
this came to pass with e-money was not anticipated by
Hayek.
Reasons of fall in inflation in G10 countries (Cheap
goods from China vs
• Marimon et. al (1997) admirably stated the following key
issues : “Most developed countries have experienced
a drastic reduction of inflation rates in the last
quarter of this century, from the double digit
numbers of the mid seventies to the very low – say,
below 2.5% , numbers at the end of the nineties. High
inflation episodes seem to be problems of the past,
as if society had become immune to the disease.
This success in curbing inflation has been usually
attributed to a better monetary policy management
to achieve price stability. But, maybe the right
incentives have been created by the widespread
development and use of cash substitutes. Who
deserves most credit? An implication of the paper
Flat CPI inflation output trade off: Economy
Imbalanced by Greenspan Put like
The lack of inflationary overheating has encouraged the
prolonged use of the so called Greenspan put has
structurally unbalanced, especially, the US and UK
economies to the point where disintermediation from real
investment has meant that the GDP growth becomes
correlated with the growth of the financial sector and asset
bubbles (see, Khatiwada,2009 and Stockhammer, 2004).
Hence, in the US and UK macro-stability in the reduced
form policy model has relied on maintaining asset
bubbles via the so called Greenspan put rather than
correct the real side economic imbalances, contributing
to ever growing global systemic risk.
Part II: How did things go so badly wrong ? Game theory
for Central Bankers : Why did they get it so wrong ?
Sine qua non of a complex adaptive system is novelty and
surprises produced in a Red Queen type arms race
The party that does not coevolve will lose the game
Markose (2005, sections 3,4 )
How did macro-policy design become fixated on
precommitting to simple fixed rules ? A fatal and flawed
combination of Lucas ‘Surprise’ Inflation and KydlandPrecott of buying reputation by fixity and a full repudiation
of the need to adapt and co-volve with a vigorously
evolving monetary and financial system
Dominant view macro-stability lay in a fixed inflation rule
forestalled scientific advances in study of stability of the
economic system as a highly interconnected co-evolving
one in which policy rules have to be carefully designed to
Why Policy Design is Not Geared to
Competitive Co-Evolution ? Traditional Game
Theory is deficient
Though popular with academic economists, the Theil-Tinbergen theory of
policy design based on the so called Linear- Quadratic- Gaussian model of
optimal control where the policy maker’s targets are only buffeted by
random noise rather than by regulatees who game the system, is of
little practical use for policy design and implementation.
The Lucas thesis on policy design which implied that policy analysis must not
be conducted as if it is a game against nature effectively overturned the
traditional Theil-Tinbergen approach to policy analysis as it was traditionally
done. How was this message not taken on ?
Note, surprises or novelty production of objects not previously there is
not permissible in extant game theory and hence the role of coevolutionary arms race between regulator and regulatee is not part of
the mainstream macro-economic policy setting framework
The notion of a surprise strategy in the macroeconomics literature appears in the so called Lucas
surprise supply function often defined as follows:
y = y* + b( p- pe ) + e . This says that output, y, will not increase beyond the natural
rate, y*, unless there is ‘surprise’ inflation, (p - pe) which is the prediction error from
expected inflation, p e.
The idea here is that the private sector contravenes the effects of anticipated
inflation, viz. the neutrality result.
Hence, it is intuitively asserted that authorities who seek to expand output
beyond the natural rate need to use surprise inflation. As surprise inflation
sounds like a ‘bad’ thing to do – the objective of mainstream monetary
policy became one of pre-committing authorities to a fixed rule for inflation
control.
Bolstered by the so called Kydland –Prescott credibility literature. Precommitting central bank authorities to a fixed rule was said to work as an
inflation anchor and bolster credibility. First, they preccommited with
exchange rate pegs and famously brought down
A longstanding misunderstanding by macro and monetary
economists of the notion of a ‘surprise’ policy strategy in
the Lucas thesis on policy design
Led to the dominant view that good monetary policy is one where authorities
are engaged in a pre-commitment strategy of fulfilling a fixed quantitative rule
(see, Markose, 2005 Sections 3 and 4) rather than set up a macro-prudential
framework that will enable them to co-evolve with regulatees and produce
countervailing measures to keep regulatory arbitrage in check.
In 1987, Ken Binmore in Modelling Rational Players had pointed out how
extant game theory cannot incorporate rule breaking contrarian structures and
novelty production. The only indeterminism is randomization between two
known actions as the action set is fixed and given.
Binmore (1987) had indicated that any strategist who upholds deterministic
strategies as being optimal must answer the question “what of the Liar ?” or
what of the agent who is contrarian and can negate or falsify a rule, few if any
recognized the Lucas postulates are analogues of the formal conditions in
mathematical logic of complex adaptive systems , Markose (2004,a).
Who Do you Surprise ?In Math Logic your surprise the Liar , the agent
who will negate you if you if he can predict your actions
In a simple two person oppositional game involving for example private sector and government authorities (or
parasite-host relationship, indexed by p and a) , the generic statement of the Liar or contrarian strategy is the
following :
The first line states that output q will be negated to q~ by the contrarian fp¬ strategy if and only if the policy with
code ba is applied and output q is produced in state s. If not, as noted in the second line above, the Liar does
nothing. The fixed point of fp¬ is denoted as (ba¬, ba¬), is not computable as in
 f  ( b  ,b  ) (s) ( b  , b  )
p
a
a
a
a
(s). For if it is, the two sides of the equation will produce contradictory outputs. Remarkably, two place
encoding (ba¬, ba¬) (analogous to Gödel substitution function) says that p knows that a knows that p is the Liar.
From here on total computable strategy functions starting with that of the authorities, fa!, can only map into a set
such as ( - A)and will mark an arms race in surprises. If this is not feasible, preannounced policy rule a has to
be abandoned or the Liar eliminated to avoid policy failure.
Despite demise of currency pegs from precommitment to transparent
rules: Nothing was learnt
The £-Sterling tethered to the European Exchange Rate currency peg brought down by
George Soros in 1992 who has openly claimed that insights from mathematical logic
relating to the Cretan Liar have been served as an inchoate, though powerful, guide to
his successful career as a currency speculator (see, Soros ,1995, p. 69, p.213),
The development of quantitative integrative modelling tools in a strategic setting for
macro-economic policy or financial product design as a replacement of macroeconometric models were all but abandoned.
After the serial collapse of currency pegs globally with every one of them defended at
large cost to tax payers, Stiglitz (1999, 2000) critiqued the tendency for macro-policy
makers to prescribe ‘optimal’ policy rules with little concern of their strategic
implementation in circumstances that must realistically prevail.
Eichengreen(1999) went on to break ranks with the IMF credo and with hindsight called
into question what was considered de rigueur on the basis of the very large and
influential literature on the conduct of monetary policy which advocated precommitment
to a transparent formalistic institution such as the currency peg.
Part III : MAFN Monitoring of Perverse Incentives
from Policy
Based on US FDIC bank data, available to the regulator at the time, we
investigate how a CDS negative carry trade combined with incentives provided
by Basel II and its precursor in the US, the Joint Agencies Rule 66 Federal
Regulation No. 56914 which became effective on January 1, 2002, on synthetic
securitization and credit risk transfer (CRT), led to the unsustainable trends and
systemic risk
How did US banks accumulate so much toxic RMBS as well as CDS on their
balance sheets?
Multi-Agent Financial Network (MAFN) Model of US Collateralized Debt Obligations (CDO):
Regulatory Capital Arbitrage, Negative CDS Carry Trade and Systemic Risk Analysis
Sheri M. Markose, Bewaji Oluwasegun and Simone Giansante
In Press Chapter in book Simulation in Computational Finance and Economics: Tools and Emerging
Applications , Edited by Serafin Martinez et. al.
Figure : Partially Funded Hybrid Synthetic CDO RMBS Structure ($5 bn)
Sponsoring Bank
Retains on balance
sheet $5bn
of Structured RMBS
CDO;
Off balance sheet
CDS protection
purchase on funded
mezzanine tranche
$4.55bn unfunded
super senior AAA
tranche (91%)
Investors of Credit Linked Notes (CLNs)
$300m senior CLN
tranche
$400m funded
mezzanine tranche
(8%)
$50m first loss
(1%)
Periodic payment of CDS
premia
$100m Junior CLN
tranche
SPV / CDO
Trust
Credit Event: Notional less
recovery rate
Proceeds from CLN sale
High Grade US Treasury
(collateral)
In a synthetic CDO, the tranches are held as assets on bank balance sheets and
the bank buys CDS protection for the mezzanine tranche. The premia on the CDS
is paid to a SPV which also raises funds by issuing credit linked notes. These
funds are invested in low risk treasury bonds which when liquidated should cover
full notional value of the mezzanine tranche should the bank sustain losses on it.
Hence, there is zero risk weight on this fully funded tranche.
Source: Adapted from OCC Bulletin 99-43a: Capital Interpretations Synthetic Collateralized Loan Obligations[1]
CLN : Credit linked note.
In what follows we will assume a 20% risk weight for the senior tranche and a zero risk weight for the funded mezzanine tranche.
•[1] The related document is Capital Interpretations Synthetic Collaterized Loan Obligations at
•http://www.occ.treas.gov/news-issuances/bulletins/1999/bulletin-1999-43.html
As seen from Figure 3, even in 2009 Q2 a substantial sum of £88.56 bn of CDS protection was purchased
for regulatory capital purposes by reporting FDIC US banks. Bulk of it by JP Morgan
Figure 3: Purchased CDS protection that is recognized as a guarantee for regulatory capital purposes by the Top 5 Banks
and All Reporting Banks 2009Q2 to 2010Q2 [1]
100,000,000
90,000,000
80,000,000
JPMORGAN CHASE
BANK
70,000,000
CITIBANK, N.A.
$ thousands
60,000,000
BANK OF AMERICA,
NATIONAL
ASSOCIATION
50,000,000
GOLDMAN SACHS
BANK USA
40,000,000
30,000,000
HSBC BANK USA
20,000,000
All Banks
10,000,000
0
2009Q2
Source: FDIC Call Reports
[1]
The FDIC Call Report code for this is RCFDG404.
2009Q3
2009Q4
2010Q1
2010Q2
Only 33-38 FDIC FIs involved in both CDS and RMBS:Agent based
model takes the savings in equity capital by taking on CDS cover on
balance sheet RMBS and compares it to the cost of buying CDS cover
(Table below shows the tranches for which CDS arb is lucrative )
Table 1: CDS Basis Estimated As CDS Spreads Minus CDO Bond Spreads
AAA
AA
A
BBB
BBB-
Q1 2006
-15.8
-37.9
-73.52
-230.05
-177.84
Q2 2006
-14.46
-33.31
-86.15
-156.86
-74.82
Q3 2006
-17.19
-38.47
-94.29
-84.81
-113.08
Q4 2006
-24.79
-31.16
-91.02
-86.22
-101.96
Q1 2007
-23.21
-44.52
36.42
419.21
736.22
Q2 2007
-15.92
-73.92
307.47
1,326.93
2,075.43
1,498.71
1,362.99
604.87
145.97
46.6
Q3 2007
Source : CDS Spreads ABX-HE Index and CDO Bond Spreads from Nomura Fixed Income Research
Let eand i ,respectively, denote the 8% regulatory capital requirement and the regulatory risk
weight on the CDO tranche i without the credit risk mitigant (CRM) and i
CRM
is the risk weight
commensurate with credit risk mitigant. The latter is assumed to be issued by an AAA rated company
in the form of CDS cover. The savings in risk capital given by eii
CRM
) in basis points must in
principle exceed the net cost of purchasing CDS and acquiring the CDO tranche. For this the CDS
spread λit at time t has to be contrasted with the CDO bond spread given as the yield minus the risk
free rate (yit - rt). This condition for the profitability of CDS based capital reduction for structured
RMBS reference assets is as follows:
eii
CRM
) > (λit – (yit - rt))
(1)
λit: CDS spread at time t given as the annual cost of protection over a N-year period is defined as λit =
[(100-CDS price at time t of ith ABX-HE subindex )/ N*100] + fixed premium
i : Risk weight on ith CDO tranche without credit risk mitigant
i CRM : Risk weight on ith CDO tranche with credit risk mitigant
yit: CDO bond yield to maturity on the ith tranche at time t
rt: Risk free rate at time t
Capital saved with CDS based CRT from an AAA guarantor on the senior RMBS tranche is 240 basis
points (which is 400 basis points less 160 basis points) and on the mezzanine tranche with zero risk
weight, all of the 400 basis points is saved. In a period of zero to negative yields on bonds and
high interest rates, the regulatory capital arbitrage via CDS activity may be naturally curtailed
as the CDS basis becomes positive. Indeed, equation (1) suggests that the regulatory capital
saving incentives are so strong that only in the case of the CDS basis exceeding over 400
basis points will this activity be curtailed.
Figure 10: Actual (FDIC Data) and Simulated Results for Banks’ Holdings of RMBS Assets (2006Q1- 2007Q3)
Note the unleveraged RMBS graph appears as shadow along the $630 bn mark . All the RMBS accumulated
from 2006Q3 – 2007 Q1 lost all of its value by 2007 Q3
750
740
730
720
710
700
$ Billions
690
680
670
660
650
640
630
620
610
600
2006Q1
2006Q2
2006Q3
Total RMBS Bank Assets FDIC Data
2006Q4
2007Q1
Simulated (Leveraged #)
2007Q2
2007Q3
Simulated (No Leverage)
Figure
the financial
network
visualizerCDS
Empirically
Figure 12
13 :Using
Instability
propagation
in Clustered
NetworkConstructed CDS Network for US Banks and US Non-Banks(Triangle):
Empirical Small World initial network (FDIC Call Report Data of 2008 Q4)
NB: Black nodes denote failed banks with
successive concentric circles denoting the qsteps of the knock on effects. Here q is 1.
Source: Markose et. al. (2010)
Source: Markose et. al. (2010)
Using the Tier 1 capital reported in by these banks in the FDIC call reports, we find that default of any of the top 5 banks based on their
bilaterally netted fair value CDS obligations will result typically in the failure of those banks that are highly interconnected amongst themselves.
The contagion stops at this point with it being confined to the top hierarchy (as shown in Figure 13) but in the spirit of being too interconnected to
fail, the top banks (black nodes in Figure 13) are brought down when any other member of this group collapses. Clearly, the implicit socialized
losses of capital from bank failure with such a topological concentration of counterparties with high CDS market share is very large as top banks
also account for some 43% of Tier 1 capital ($430 bn) of the 26 banks in the sample .
Concluding Remarks
• Policy Design and Systemic Risk from Complexity and Networks
Perspective: Socio-economic system failures arise from a disparity between
the pursuit of local interest and those needed for overall stability of the
system.
Fallacies of composition arise due to a lack of visualization tools that can give
a holistic picture of the system not just in terms of disparate individuals or in a
simplistic aggregation of them to one agent (the preferred method of extant
macro-economics)
A networks and complexity perspective gives a unified picture of system
stability in terms of well known monetary and financial tools to do with capital,
reserves, collateral, margins and find these get eroded by the pursuit of
regulatory arbitrage or perverse incentives from policy.
As competitive co-evolution in the form of strategic innovative behaviour
between firms, and firms as regulatees and the regulator should make this the
centre piece of robust design frameworks.
Vigilance is mandatory by the regulator to detect regulatory arbitrage or for
perverse incentives from policy.
Network structures of complex systems due to local efficiency drives will
typically display supercriticality often manifest in a small world networks
structure with tiered central hubs which are too interconnected to fail.
•Threats from Inflation Misconceived: In advanced cashless economies, the
structural changes in monetary aggregates have meant that that the payments
component of money has shrunk drastically leaving ‘inside’ private credit
creation to dominate
This appears to imply that monetary policy influencing interest rates and bank
reserves can only contract or expand private credit creation (subject to Keynesian
liquidity trap conditions when banks hoard money rather than lend it) and generate
asset prices bubbles with little or no impact on inflation on the CPI index. As
Marimon et. al. (1997) have pointed out, central banking elites in developed
countries appear to have a vested interest in not finding out to what extent changes
in payment habits which have substituted away from cash have curbed inflation.
The imbalance in economy in due to decades long disintermediation from real
investment and from the fuelling of assets bubbles requires nothing short of a
Marshall Plan and direct reconstruction.
In BRICs, the large cash based payments systems still harbour old style inflationary
pressures well captured by crude quantity theory of model which revolves around
transactions demand for money and the proportionality between inflation rate, cash
in circulation and growth in monetary base. Take cash out of circulation for
transactions and speed up
•Checks and Balances on Negative
Externalities from Leverage:
Monetary and financial infrastructure is a public good which is ultimately
underpinned by fiat money backed by tax revenues of a government. The
need to control negative externalities that arise from over use/supply by
individual entities whether they are private or governmental agencies is the
only legitimate economic argument against self-regulation.
I have designed a Pigou Tax for financial intermediaries in terms of their
eigenvector centrality so that they internalize their costs of failure
At the heart of the negative externalities problem is the need to ‘cap’ the
production of an economic activity. The design of institutional constraints on
fiat money and private debt based fractional systems are not different from
managing environmental negative externalities where the balance between
growth and sustainability has to be designed artificially or evolved by trial and
error. Extant pricing models for credit risk are unable to price in the ‘clean up’
costs entailed in systemic risk leading to chronic underpricing.
•Credit Default Swaps as Credit Risk Mitigant in
Fractional Systems of Credit Creation:
The role of credit default swaps to substitute bank capital in the Basel II and III
framework adds to the instability of fractional systems of credit generation. The
role of capital is to mitigate the leverage impact while the use of credit derivatives
will have an endemic tendency, fully justifiable at an individual level as a hedge,
to multiply it in a pyramid of derivatives on derivatives. I recommend that the
Basel II and III provision for capital reduction on bank assets from the use
of CDS guarantees cover should be discontinued.
Banks should be left free to seek unfunded CDS cover for bank assets without
the incentive of capital reduction and leverage. Indeed, this may enhance price
discovery role of the CDS market
Perverse Incentives from Risk Weighting of Capital to be Monitored : It
Encourages Race to the bottom and unless it is made countercyclical it
is a key reason for why banks became insolvent (eg. Northern Rock
see Blundell-Wignall and Atkinson (2008) )
The Increasing Leverage Produced by Risk-Weighted Assets
Source: International Monetary Fund Global Financial Stability Report, April 2008
Also in Acharya and Richardson ( 2010).
• New Governance Structures for Financial Innovations and
Regulatory Change:
Consumer protection and public health is well served from workings of food and drugs agencies where
innovations are rigorously vetted for their capacity to harm. It is increasingly accepted that a similar agency
to authorize financial innovations before commercial use will well serve public interest.
The same accountability should be installed for the rule making bodies in banking and finance. Poor rules
made with no cognizance of their systemic risk consequences can wreck financial superstructures faster than
any terrorist malfeasance. I can only repeat here what Martin Hellwig (2010) has said about the lack of
accountability of the Basel Committee on Banking Supervision: “It adheres to the tradition of discussing the
rules of capital regulation among the bureaucratic cognoscenti, in some interactions with the industry,
without ever providing any theoretical or empirical analysis of the effects that the measures under
consideration are deemed to have – and without heeding outsiders who demand that such analysis should be
just as much a precondition for the implementation of regulatory rules as for the introduction of new
pharmaceutical drugs in the market”.
BRICs which are en route to similar stages of financial and monetary development will be better served if
more accountability is brought into the global rule making body for banking and finance.
In conclusion , I submit that managing financial
systemic risk is not simply adding an extra term to
Taylor type rule for financial sector overheating !
References
Binmore, K. (1987), “Modelling Rational Players: Part 1”, Journal of
Economics and Philosophy, vol. 3, pp. 179-214. Cutland, N.,1980,
Computability: An introduction to recursive function theory (CUP).
May, R.M. (1972) “Will a Large Complex system be Stable?” Nature
238, 413-414.
Marimon, R. Nicolini, J.P, Teles, P. (1997) “Electronic money: The end
of inflation ?”, Discussion Paper 122, Institute for Empirical
Macroeconomics, Federal Reserve Bank of Minneapolis.
http://www.acefinmod.com/CDS1.html
Sheri M. Markose Multi-Agent Financial Network Analyses For Systemic Risk
Management Post 2007 Financial Crisis: A New Complexity Perspective For G10 and
BRICs , Speech given at Reserve Bank of India , draft at above website
Sheri M. Markose , Bewaji Oluwasegun and Simone Giansante (2012, forthcoming)
“Multi-Agent Financial Network (MAFN) Model of US Collateralized Debt Obligations
(CDO): Regulatory Capital Arbitrage, Negative CDS Carry Trade and Systemic Risk
Analysis” Chapter in book: Simulation in Computational Finance and Economics:
Tools and Emerging Applications Alexandrova-Kabadjova B., S. Martinez-Jaramillo,
A. L. Garcia-Almanza, E. Tsang, Publisher IGI Global
Markose, S. and A. Alentorn (2011) “The Generalized Extreme Value Distribution,
Implied Tail Index, and Option Pricing”, Journal of Derivatives, Spring 2011, Vol. 18,
No. 3: pp. 35–60.
Markose, S.M (2005) “Computability and Evolutionary Complexity : Markets as
Complex Adaptive Systems (CAS)”, Economic Journal ,vol. 115, pp.F159-F192.
Markose, S. M, (2004), “Novelty in Complex Adaptive Systems (CAS): A Computational