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2014.05.19 - OECD-ECLAC Workshop_Session 1_Sheri MARKOSE

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2014.05.19 - OECD-ECLAC Workshop_Session 1_Sheri MARKOSE

  1. 1. OECD – ECLAC WORKSHOP New tools and methods for policy making 19 May 2014 Session 1: Multi-Agent Financial Network Models And Global Macro-net Models: New Tools for Macro-Prudential Policy Sheri MARKOSE (scher@essex.ac.uk) Economics Dept. University of Essex, The software used in network modelling was developed by Sheri Markose with Simone Giansante and Ali Rais Shaghaghi
  2. 2. Roadmap 1 : Why MAFNs/Macro-Nets for Macro- prudential policy ? • Two methodological problems of financial contagion and systemic risk : (i)Paradox of Volatility and the pitfalls of market price data based systemic risk measures hence structural bilateral data based networks modeling needed (ii) Non- trivial Negative Externalities problem → the need for holistic visualization • Some Applications: Systemic Risk From Global Financial Derivatives Modelled Using Network Analysis of Contagion and Its Mitigation With Super-Spreader Tax • Global Macro-nets integrated with Real Side Sectoral Flow of Funds to assess extent of economic imbalance from financialization and also from global flows • Some insights from Indian Financial System and Bilateral Data based Network Modeling: Pioneering first full bilateral digital map of financial system (Brazil and Mexico also mandating bilateral data)
  3. 3. Macro-prudential: Systemic Risk Management With Networks • Holistic Visualization to overcome fallacy of composition type errors • ICT data base driven multi-agent financial networks: glorified data visualizations → Andrew Haldane/ Mark Buchanan Star Trek Vision • Causal Connections v Statistical Analysis • Minimum three elements are needed bilateral financial data :Contracts, Counterparties, Maturity Buckets • Integration and automation of financial data bases in a MAFN framework aims to transform the data from a document or record view of the world to an object-centric view (see Balakrishnan et. al. 2010), where multiple facts about the same real-world financial entity are accessed to give a composite visualization of their interactions with other such entities in a scalable way.
  4. 4. Roadmap 2: Systemic Risk Analytics • Stability of Networks and Eigen-Pair Analysis: Markose et. al. (2012) • 3 main questions of macro-prudential regulation : (i)Is financial system more or less stable? (ii)Who contributes to Systemic Risk ? (iii)How to internalize costs of systemic risk of ‘super-spreaders’ using Pigou tax based on eigenvector centrality: Management of moral hazard, Bail in vs Bail out : How to Stabilize system using EIG Algorithm ? • Generalization to multi-layer networks ● Conclusions
  5. 5. Mark ( (i )Market Data based Statistical Models of Systemic Risk : A Case of Steadfast Refusal to Face Facts ( á la Goodhart, 2009) ? Absence of Early Warning Signals Major drawback of market price based systemic risk measures: they suffer from paradox of volatility(Borio and Drehman ,2009) or paradox of financial stability issues first addressed by Hyman Minsky (1982). Market based statistical proxies for systemic risk (eg Segoviano and Goodhart (2009) banking stability index, Contingent Claim Analysis Distance to Distress Index of Castern and Kavounis (2011)) at best contemporaneous with the crisis in markets, at worst they spike after crisis. Laura Kodres et al IMF WP /2013/115 now call market based systemic risk indices Coincident and Near Coincident Systemic Risk Measures: conceded absence of early warning capabilities. As credit growth boosts asset prices, CDS spreads and VIX indices which are inversely related to asset prices are at their lowest precisely before the crash when asset prices peak → Also procyclicality of leverage Adrian and Shin (2010, 2011).
  6. 6. Banking Stability Index (Segoviano, Goodhart 09/04) v Market VIX and V-FTSE Indexes : Sadly market data based indices spike contemporaneously with crisis ; devoid of requisite info for Early Warning System
  7. 7. “Paradox of stability” : Stock Index and Volatility Index Paradox of Volatility (Borio and Drehman(2009); Minsky (1982)) Volatility low during boom and at local minimum before market tanks : hence misled regulators “great moderation”
  8. 8. IMF WP /2013/115 Market Data Based Systemic Risk Measures: Coincident or Near Coincident Devoid of Early Warning Few Weeks at Best Arsov et. al. (2013) design IMF Systemic Financial Stress (SFS, black above) index which records the extreme negative returns at 5 percentile of the (left) tail for the joint distribution of returns of a selected sample of large US and Eurozone FIs (Ibid Figure 4 ) Backtesting of popular systemic risk metrics (Red, above)
  9. 9. (ii) Fallacy of Composition In the Generation of Systemic Risk/Negative Externalities: Holistic Visualization Needed Systemic risk refers to the larger threats to the financial system as a whole that arise from domino effects of the failed entity on others. At the level of the individual user micro-prudential schemes appear plausible but at the macro-level may lead to systemically unsustainable outcomes. Example 1 : Risk sharing in advanced economies uses O-T-C derivatives. Success of risk sharing at a system level depends on who is providing insurance and structural interconnections involved in the provision of guarantees. Only 5% of world OTC derivatives is for hedging purposes Credit Risk Transfer in Basel 2 gave capital reductions from 8% to 1.6% capital charge if banks got CDS guarantees from ‘AAA’ providers
  10. 10. Structure of Global Financial Derivatives Market:Modern Risk Management based on Fragile Topology that Mitigates Social Usefulness (2009,Q4 204 participants): Green(Interest Rate), Blue (Forex), Maroon ( Equity); Red (CDS); Yellow (Commodity); Circle 16 Broker Dealers in all markets (Source Markose IMF W, 2012)
  11. 11. Granular Banks and Non Bank Financial Intermediaries (Dec 2012)- Note that insurance companies (H codes) mutual funds(G codes) and not banks (A-D codes) are net liquidity providers: Fact can be missed in banks only models !
  12. 12. A Financial Intermediary is member of multiple financial markets (multi-layer networks) How to calculate its centrality across the different networks it is present ?Joint Eigen-vector centrality Multi-Layer Network with common nodes in some layers ; m markets Single network
  13. 13. Castren and Racan (ECB 2012 WP) Phenomenal Global Macro-net Model With National Sectoral Flow of Funds To Track Global Financial Contagion! Only Problem- the Castren-Racan Systemic Risk Analytics Fail to have Early Warning Capabilities The circle in the center represents banking systems that are exposed to the cross border liabilities of sectors (household, non bank corporate, public etc) within countries. The latter with sectoral flow of funds are given in the outer circle
  14. 14. Castren-Racan (2012) Loss Multiplier Systemic Risk Measure(blue lines) vs. Markose et al (2012, 2013) Maximum Eigenvalue of Matrix of Net Liabilities Relative to Tier 1 Capital (green line) Castren-Racan loss multiplier (blue lines), unfortunately, peaks well after crisis has started and asset side of FIs is considerably weakened. Markose (2012,2013) direct measure of maximum eigenvalue, (green line)of matrix of liabilities of countries relat Tier1 capital of the exposed national banking systems, will capture growing instabili network relative to distribution of capital buffers well ahead of actual crisis.
  15. 15. Global Macro-net plagued by within country sectoral imbalances and global imbalances only 8% of total lending for real investment 0% 20% 40% 60% 80% 100% 120% 140% 160% 180%Other Financial Institutions Secured Household (banks) Secured Household (BSocs) Unsecured Household LBO targets Commercial Real Estate 'core' PNFC
  16. 16. Network Stability and Systemic Risk Measure: Why Does Network Structure Matter to Stability ? lmax = 𝑁𝐶 s < 1 Formula for network stability • My work influenced by Robert May (1972, 1974) • Stability of a network system based on the maximum eigenvalue lmax of an appropriate dynamical system • May gave a closed form solution for lmax in terms of 3 network parameters , C : Connectivity , number of nodes N and s Std Deviation of Node Strength : lmax = 𝑁𝐶 s All 3 network statistics cannot grow and the network remain stable. Eg a highly asymmetric network with high s such as core periphery, its connectivity has to be very low for it to be stable
  17. 17. From Epidemiology : Failure of i at q+1 determined by the criteria that losses exceed a predetermined buffer ratio, r, of Tier 1 capital  𝑢 iq+1 = (1 - r) uiq + (𝑥 𝑗𝑖−𝑥 𝑖𝑗) 𝐶 𝑖0 + 𝑢𝑗𝑞 1 𝑗 (2) (i)First term i’s own survival probability given by the capital Ciq it has remaining at q relative to initial capital Ci0 , r is common cure rate and (1 - r) is rate of not surviving in the worst case scenario . (ii)The sum of ‘infection rates’= sum of net liabilities of its j failed counterparties relative to its own capital is given by the term (𝑥 𝑗𝑖 −𝑥 𝑖𝑗 ) 𝐶 𝑖0 + 𝑗
  18. 18. Stability of the dynamical network system : Eigen Pair (λmax , v) In matrix algebra dynamics of bank failures given by: Ut +1 = [´ + (1- r)I] Ut = Q Ut (3) I is identity matrix and r is the % buffer  The system stability of (2) will be evaluated on the basis of the power iteration of the matrix Q=[(1-r)I+Θ´]. From (3), Uq takes the form: Uq= Qq U0  Stability Condition lmax(´) < r After q iterations λmax is maximum eigenvalue of Θ
  19. 19. Solvency Contagion and Stability of Matrix Θ’ : Netted impact of i on j relative to j’s capital )2( 0... )( .... )( .0........ )( ...0.... )( .........0.. )( ........ )( 00 0.....0. )()( 0 1 11 1 11 33 3 3223 3 3113 2 2112                                      jt jNNj t NN Nt NiiN t ii Nt NN t tt C xx C xx C xx C xx C xx C xx C xx C xx
  20. 20. Financial network models to date have yielded mixed results : None about propagators of 2007 crisis (C: Core; P Periphery (see Fricke and Lux (2012))
  21. 21. Some Networks: A graphical representation of random graph (left) and small world graph with hubs, Markose et. al. 2004
  22. 22. Contagion when JP Morgan Demises in Clustered CDS Network 2008 Q4 ( Left 4 banks fail in first step and crisis contained) v In Random Graph (Right 22 banks fail !! Over many steps) Innoculate some key players v Innoculate all ( Data Q4 08)
  23. 23. Eigenvector Centrality (EVC) Centrality: a measure of the relative importance of a node within a network Eigenvector centrality Based on the idea that the centrality vi of a node should be proportional to the sum of the centralities of the neighbors l is maximum eigenvalue of Θ A variant is used in the Page Ranking algorithm used by Google The vector v, containing centrality values of all nodes is obtained by solving the eigenvalue equation Θ 𝒗 𝟏 = λmax 𝒗 𝟏. λmax is a real positive number and the eigenvector 𝒗 𝟏 associated with the largest eigenvalue has non-negative components by the Perron-Frobenius theorem (see Meyer (2000)) Right Eigenvector Centrality : Systemic Risk Index Tax using Right EVC Left Eigenvector centrality Leads to vulnerability Index
  24. 24. Mitigation of Systemic Risk Impact of Network Central Banks: How to stabilize ? To date the problem of how to have banks internalize their systemic risk costs to others (and tax payer) from failure has not been adequately solved In particular, penalty for being too interconnected has not been dealt with from direct bilateral network data Tax according to right eigenvector centrality
  25. 25. There are 5 ways in which stability of the financial network can be achieved (i)Constrain the bilateral exposure of financial intermediaries (Ad hoc constraints do not work) Serafin Martinez implemented these in Mexico (ii) Ad hocly increase the threshold rho in (11), (iii) Change the topology of the network (iv) Directly deduct a eigenvector centrality based prefunded buffer in matrix Si # = 𝜃𝑗𝑖 # 𝑗 = 1 𝐶 𝑖 ( (𝑥𝑗𝑖𝑗 − 𝑥𝑖𝑗)+ −  vi 𝐶𝑖) . (i) & (ii) do not price in negative externalities and systemic risk of failure of highly network central nodes. Network topologies emerge endogenously and are hard to manipulate
  26. 26. How to stabilize: Superspreader tax quantified : tax using Eigen Vector Centrality of each bank vi or vi ^2 to reduce max eigenvalue of matrix to 6%
  27. 27. Superspreader tax rate
  28. 28. • Too interconnected to fail addressed only if systemic risk from individual banks can be rectified with a price or tax reflecting the negative externalities of their connectivity • Lessons to be learnt : Disease Transmission in scale free networks (May and Lloyd (1998), Barthelemy et. al : With higher probabilities that a node is connected to highly connected nodes means disease spread follows a hierarchical order. Knowledge of financial interconnectivity essential for targeted interventions • Highly connected nodes become infected first and epidemic dying out fast and often contained in first two tiers • Innoculate a few rather than whole population; Strengthen hub; Reduce variance of node strength in maximum eigenvalue formula Conclusion : Regulators and Systemic Risk Researchers must face up to limits of data mining market price data for early warning signals and instead mandate structural bilateral financial data and digitally map the macro-financial network system
  29. 29. • Changes in eigenvector centrality of FIs can give early warning of instability causing banks • EVC basis of Bail in Escrow fund/Capital Surcharge • These banks with high Eigen vector centrality will, like Northern Rock, be winning bank of the year awards ; however potentially destabilizing from macro-prudential perspective Capital for CCPs to secure system stability can use same calculations • Beware gross aggregation and netting across product classes for which there is no multi-lateral clearing ; more systemic risk and hence more, capital/collateral needed for stabilization • Single network v multi-layer networks Other Concluding Remarks
  30. 30. References: (1) Markose, S.M (2013) "Systemic Risk Analytics: A Data Driven Multi-Agent Financial Network (MAFN) Approach", Journal of Banking Regulation , Vol.14, 3/4, p.285-305, Special Issue on Regulatory Data and Systemic Risk Analytics. (2) Markose, S. (2012) “Systemic Risk From Global Financial Derivatives: A Network Analysis of Contagion and Its Mitigation With Super-Spreader Tax”, November, IMF Working Paper No. 12/282, (3) Markose, S., S. Giansante, and A. Shaghaghi, (2012), “Too Interconnected To Fail Financial Network of U.S. CDS Market: Topological Fragility and Systemic Risk”, Journal of Economic Behavior and Organization, Volume 83, Issue 3, August 2012, P627- 646 http://www.sciencedirect.com/science/article/pii/S0167268112001254 (3)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, Chapter in Simulation in Computational Finance and Economics: Tools and Emerging Applications Editor(s): Alexandrova-Kabadjova B., S. Martinez- Jaramillo, A. L. Garcia-Almanza, E. Tsang, IGI Global, August 2012. http://www.acefinmod.com/CDS1.html

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