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 ? [email protected]The software used in network modelling was developed by Sheri Markose with Simone Giansante and Ali Rais Shaghaghi Economics Department Centre for Computational Finance and Economic Agents
Transcript
Kiel Institute for the World Economy:6th Kiel Institute Summer School on Economic Policy Policy,June 24 – 30, 2012
Professor Sheri MarkoseEconomics Department (University of Essex)Multi-Agent Financial Network Models (MAFN) For Systemic Risk Management: A New Complexity PerspectiveLecture 2: Design of Robust Macro-prudential Policy: Why a new complexity approach and MAFNs [email protected]
The software used in network modelling was developed by Sheri Markose with Simone Giansante and Ali Rais Shaghaghi
Economics DepartmentCentre for Computational Finance and Economic Agents
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 analysisReserve 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 monitor it on an on-going basis for its capacity to generate perverse incentives..Kane (2010), we must avoid “official definitions of systemic risk that have left out the role of government officials in generating it.”
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
In contrast, main draw-back of estimation based equation analyses is that structure changes from strategic behaviour and tracing of causal links are almost impossible to do.
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 CO-EVOLUTIONARY 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.
State Supplied Monetary Base M0(Notes and coins)
M0 Increased by Quantitative Easing
Broad Money ‘Inside Money’ Produced by Credit /Leverage Based on Discounting of private securities SECURITIZAT-ION INCREASES SCOPE OF PRIVATE DEBT SHADOW BANKING SYSTEM
Money Supply with Securitization , Money Demand and Inflation
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
Expands and contracts with business cycle; CRISIS OCCURS WHEN CONVER-TIBILITY FROM INSIDE MONEY TO M0 IS AT STAKE
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 macro-financial 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 of monetary authorities to engineer inflation which had fallen to under 2.5% by 1994 in advanced cashless economies (see, Marimon et. al.,1997).
Table: interest rates and inflation rates for selected EU and G10 countries
Data: International Monetary Fund, International Financial Statistics Yearbook 2000 and 2001.
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 will be that the role of electronic money in curbing inflation has been undervalued.”
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 raceThe 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 Kydland-
Precott 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
avoid unintended perverse consequences.
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 co-evolutionary 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 macro-economics 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. Pre-committing 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
)b,b(f aap
(s))b,b( aa
(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, f a
!, 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 macro-econometric 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
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 GiansanteIn Press Chapter in book Simulation in Computational Finance and Economics: Tools and Emerging
Applications , Edited by Serafin Martinez et. al.
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?
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
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.
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]
2009Q2 2009Q3 2009Q4 2010Q1 2010Q20
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
90,000,000
100,000,000
JPMORGAN CHASE BANK
CITIBANK, N.A.
BANK OF AMERICA, NATIONAL ASSO-CIATION
GOLDMAN SACHS BANK USA
HSBC BANK USA
All Banks
$ th
ousa
nds
Source: FDIC Call Reports
[1] The FDIC Call Report code for this is RCFDG404.
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
Q3 2007 46.6 145.97 604.87 1,362.99 1,498.71 Source : CDS Spreads ABX-HE Index and CDO Bond Spreads from Nomura Fixed Income Research
Let 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 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:
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
Total RMBS Bank Assets FDIC Data Simulated (Leveraged #) Simulated (No Leverage)
$ B
illi
on
s
Figure 12 Using the financial network visualizer Empirically Constructed CDS Network for US Banks and US Non-Banks(Triangle): Empirical Small World initial network (FDIC Call Report Data of 2008 Q4)
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 .
Figure 13 : Instability propagation in Clustered CDS Network
NB: Black nodes denote failed banks with successive concentric circles denoting the q-steps of the knock on effects. Here q is 1. Source: Markose et. al. (2010)
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 2008Also 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 Theory of Actor Innovation”, Physica A: Statistical Mechanics and Its Applications, vol. 344, pp. 41- 49.
