Session 6: Legal Entity Identifier

‘Big Data’ Models for Systemic Risk Management :Transformational Impact of

GLEI System ? Discussant : Sheri Markose (University of Essex)

and Senior Consultant on Digital Mapping of Indian Financial System at the Financial Stability Unit of the Reserve Bank of India (2011- present)

Conference on Data Standards, Information and Financial Stability

April 11th – 12th, 2014 Loughborough University, East Midlands, UK

School of Business and Economics, Room BE 0.53

Facts Common on the LEI System to Viccy Lemieux et al, VL for short and Milne and Chan

(MC) • Who is a Legal Entity ? • Who is in charge ? • What are the allegedly official objectives for LEI

(FSB, 2012)? • What data is collected under LEI Records ? • What are the challenges with implementing LEI ?

(VL and MC)

Who is LE ? (FSB 2012 paper) Very wide scope for LE beyond typical regulatory boundaries

• Term ‘legal entity’ refers to a legal person or structure organised under the laws of

any jurisdiction. • Legal entities include, but are not limited to, unique parties that are legally

responsible for the performance of financial transactions or have the legal right in their jurisdiction to enter independently into legal contracts, regardless of whether they are incorporated or constituted in some other way (eg trust, partnership, contractual, etc).

• It excludes natural persons, but includes governmental organizations; and supranationals, defined as governmental or non-governmental entities established by international law or treaty or incorporated at an international level.

• Examples of eligible legal entities include: all financial intermediaries; banks and finance companies; all entities that issue equity, debt or other securities for other capital structures; all entities listed on an exchange;

• All entities that trade stock or debt; investment vehicles, including mutual funds, pension funds and alternative investment vehicles including umbrella funds as well as funds under an umbrella structure, hedge funds, private equities, etc;

• All entities under the purview of a financial regulator and their affiliates, subsidiaries and holding companies; and counterparties to financial transactions.

• The definition above corresponds to the ISO standard 17442:2012 for a Legal Entity Identifier.

Who needs a LEI ? If your organisation is subject to the market regulations either in the United States of America (Dodd-Frank Act) or in the European

Union (EMIR), your organisation must register for its LEI. • LEI is already required in the SWAPS reporting rules put

in place by the CFTC under Dodd-Frank Title VII. • The technical standards for similar trade reporting for

OTC derivatives under the EU EMIR legislation also will require an LEI, once this is available (or an interim identifier if no LEI is yet available).

• Other markets which are putting in place derivatives reporting will also require LEIs over time, and it is expected that all types of financial transactions undertaken by a legal person will over time be required to have LEIs.

Who is in Charge of LEIs ? $200 for registration

• Two Official LEI Registration Authorities : Operated by Depository Trust and Clearing Cooperation and Swift (CICI)in US and WM Datenservice in Germany

• Pre LEI Commodity Futures Trading Commission (CFTC) identification system taken over by DTCC/Swift and 80,000 LEIs given to CFTC participants; now 220,000

• Local Operating Units (LOUs) register or issue LEIs ; example in India LOU is CCIL

• 20 digit code : 1st four allocated to individual LOUs, next 2 left as zeros; next 12 is entity specific and last two digits according to ISO 17442 standard

What Information Is Collected For LEI ? While this is not quite complete, here's the basic scope of each record for each legal entity that is getting created: (i) Legal entity name (ii) Address of headquarters (iii) Address of where legal formation took place (iv) Business registration identification (v)Name of business as registered (vi)Date of first assignment of legal entity ID code (vii)Date of last update of the LEI (viii) Date of expiration of the LEI “Looks like maybe a thousand characters of information, tops. So let's quintuple that. Make it 5,000 characters of information per recrod. Then, let's take the size of the universe -- long term -- that the SIFMA proposal of last year said is involved here. We're talking about 1 million legal entities, roughly. 5,000 characters per record. 1 million records. That, in computer terms, is 5 gigabytes of information, in toto. (See, "IDs on Overdrive") Not “Big Data”. Overengineering – get rid of LOUs and direct registration at website of DTCC or SWIFT http://www.securitiestechnologymonitor.com/blogs/uptick-lei-project-not-big-data-30762-1.html Tom Steinert-Threlkeld Voices, June 15, 2012

What is the principal activity of the firm?

• Worried to find that this info on LEI

system has been left out till future consideration Industrial classification –What line of business does LE do ? Is it a bank, mutual fund, insurance company etc ?

• The US LEIs collected to date leaves this out !

FSB : Official Objectives of Global LEI A Global Legal Entity Identifier for Financial Markets Paper (2012)

A valuable ‘building block’ to contribute to and facilitate many financial stability objectives, including: •Improved risk management in firms (Milne and Chan : private incentives); • Better assessment of micro and macroprudential risks; • Facilitation of orderly resolution (recall Lehman with 7000

subsidiaries); • Containing market abuse and curbing financial fraud and

money laundering ; • It would reduce operational risks within firms by mitigating

need for tailored systems to reconcile the identification of entities and to support aggregation of risk positions and financial data

• It would also facilitate straight through processing (STP)

Official Statements (FSB) of Why We Need LEIs ? Contd Linking LEIs With Transactions Data ISIN

by ISIN • Helps identify systemic risk by aggregating related groups of

counterparties in financial transactions. • Every legal entity is identified in a way that can be matched to

a counterparty … and rolled up so that it is clear what the overall exposure of the company that ultimately controls the business is.

• Key: Being able to connect the dots between parties, products and, inside complex organizations, between all the subsidiaries and affiliates to be able to detect contagion before it spreads across the global financial system.

• It’s not enough to aggregate the identifier codes; data associated with them has to be aggregated as well. It has to be tagged, using the eXtensible Markup Language, so that the data flowing through the financial system is understandable

Applying Visual Analytics to Global LEI SystemTo Enhance Financial Transparency (Viccy Lemieux et al, VL

for short) • Poor data and analytics identified as miscreants that led to

the fact that we sleep walked into 2007 financial crisis • Talks about full transparency of LEI Data • Crowd Sourcing of financial stability • VL talks about Visual Analytics as the science of analytical

reasoning facilitated by interactive visual interfaces (Thomas and Cook, 2005)

• Time series financial data – is market price data – large quantities of it (hyper high frequency )and play ground of financial econometrics

• I will argue we are moving away from statistical correlations or co-movements for inferences to understanding structures and causal connections

• Bilateral financial liabilities and exposures that are usually netted out in standard macro-economics

GLEI: Will It Deliver ? Chan and Milne • Concerned about the wedge between perceived private gains/benefits

from LEI (from interviewees) and the public good/social benefits from LEI • C&M place the direct operational cost savings to financial froms be in the

region of $10 bn and not at $100bn • Total costs of data management to be $35 bn so a $10 bn saving places it

at about 1/3 of total costs • Legal ownership hierarchies needed in orderly resolution – this will be

added to LEI system

• V. interesting analogy made with global GSI supply chain identification for product, location and shipment identification. Individual firm needs this (why ?) to trade with a legitimate counterparty who will not participate with a non GSI party

• Likewise, counterparty if legal will not deal with a non LEI in many financial transactions venues if parties want the rule of law to apply viz. legal enforcement of contracts

Challenges :Mapping LEIs On to Legacy Systems

• Entity data has been collected in multiple data stores: client masters, security masters, CRM systems, Finance and Risk systems, often duplicated across regions and/or divisions.

• The LEI will have to be mapped to a widely varied and inconsistent set of internal and vendor identifiers, residing in multiple containers.

• Adapting to this new standard will require changes to those ancient, poorly supported, difficult to alter legacy systems that industry players are hesitant to touch due to fear of breaking some archaic process or undocumented data flow.

• Also within Regulatory Institutions, BIS Consolidated Cross Border Banking Data etc

Challenges contd. • Confidentiality and privacy restrictions governing some potentially important reference data in some countries, particularly regarding information on corporate relationships and ownership structures. • Such data are important to both the global regulatory community and

private firms as they enable aggregation of information within complex financial groups which is essential to the analysis of exposures and to the development of improved systemic risk measures.

• Governance system design must, therefore, take account of the legal constraints on the storage and transmission of such data, for example, by ensuring that the data are stored in particular jurisdictions and that there are rigorous controls on access.

VL Data Visuals of CICI Utility Records

• From the records of registered entities : cities where registered reveals tax avoidance loopholes, example Delaware comes up as the city where a majority of US firms are registered

• Viccy - Why would we need to predict future patterns of registration ? For purposes of tracking money laundering but not for general purposes of systemic risk

• Typically large numbers of subsidiaries of a company need not be a measure of connectivity for purposes of systemic risk

• Merging of CICI Utility, German WMDatenservice and pre-LEI registration datasets

Macro-prudential: Systemic Risk Management

• Holistic Visualization to overcome fallacy of composition type errors

• Causal Connections v Statistical Analysis • Minimum three elements are needed bilateral

financial data :Contracts, Counterparties, Maturity Buckets

Data Driven Multi-Agent Financial Networks (MAFN)

• Integration and automation of financial data bases in a MAFN framework, therefore, 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.

• See the IBM MIDAS project reports (Balakrishnan et. al., 2010, Hernandez et. al., 2010) on software technologies being developed for large scale firm level bilateral financial database driven models for systemic risk analysis.

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

“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”

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)

(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

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)

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 !

Multi-layer Architecture :Vital to access line of business of financial intermediary

• Reserve Bank of India (also in Mexico, Brazil) has mandated bilateral financial data on a quarterly basis from over 1200 financial intermediaries encoded into groupings from A- J, ranging from banks to the different non-bank financial intermediaries such as mutual funds, insurance companies etc.

• Modelling strategy is to first proceed in a modular fashion enlarging the financial network with agents being cumulatively being included from the A-J groupings with the proviso that new financial products and markets are to be added on in due course with regulators being beady eyed about proteanism.

• When fully completed, there will be a digital map of each FI’s activities with all others both in the non-electronically cleared and in the electronically cleared markets such as the Indian repo and CBLO markets that will be incorporated along with the RTGS payment and settlement system.

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

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

Global Macro-net plagued by within country sectoral imbalances and global imbalances

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%Other Financial InstitutionsSecured Household (banks)Secured Household (BSocs)Unsecured HouseholdLBO targetsCommercial Real Estate'core' PNFC

BIS 2010 Q4 Global Macronet Between 22 Country Financial Flows

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.

Stability of network system not to exceed a certain threshold Markose Eigen-pair Stability metric (IMF WP 2012, 2013)

Is the financial network more or less stable over time ? Who is causing the instability ? How to have latter ‘pay ‘ for this ?

• Now what will this threshold be ? Regulatory capital threshold – we denote by

• How to determine if network system is stable wrt to this threshold ?

• Following Robert May (1972, 1974) stability of the network system is to be determined by the maximum eigen value of the appropriate dynamical system

• The net liabilities (Xij – Xji )/ Capital of j, j being the exposed bank

• If maximum eigen value of above matrix greater than the loss threshold – cause of concern

• System is unstable and it a case of when not if…

Network Stability and Systemic Risk Measure: Why Does Network Structure Matter to Stability ? λmax = 𝑁𝑁 σ < 1

Formula for network stability

• My work influenced by Robert May (1972, 1974) • Stability of a network system based on the maximum

eigenvalue λmax of an appropriate dynamical system • May gave a closed form solution for λmax in terms of

3 network parameters , C : Connectivity , number of nodes N and σ Std Deviation of Node Strength :

• λmax = 𝑁𝑁 σ All 3 network statistics cannot grow and the network remain stable. Eg a highly asymmetric network with high σ such as core periphery, its connectivity has to be very low for it to be stable

Solvency Contagion and Stability of Matrix Θ’ : Netted impact of i on j relative to j’s capital

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From Epidemiology : Failure of i at q+1 determined by the criteria that losses exceed a predetermined buffer ratio, ρ, of

Tier 1 capital

• 𝑢 iq+1 = (1 - ρ) uiq + ∑ (𝑥𝑗𝑗−𝑥𝑗𝑗)𝐶𝑗𝑖

+𝑢𝑗𝑗1𝑗 (2)

(i)First term i’s own survival probability given by the capital Ciq it has remaining at q relative to initial capital Ci0 , ρ is common cure rate and (1 - ρ) 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 ∑ (𝑥𝑗𝑗−𝑥𝑗𝑗

)𝐶𝑗𝑖

+𝑗

Some Networks: A graphical representation of random graph (left) and small world graph with hubs, Markose et. al. 2004

Stability of the dynamical network system : Eigen Pair (λmax , v)

In matrix algebra dynamics of bank failures given by: Ut +1 = [Θ´ + (1- ρ)I] Ut = Q Ut (3) I is identity matrix and ρ is the % buffer • The system stability of (2) will be evaluated on

the basis of the power iteration of the matrix Q=[(1-ρ)I+Θ´]. From (3), Uq takes the form: Uq= Qq U0

• Stability Condition λmax(Θ´) < ρ After q iterations

λmax is maximum eigenvalue of Θ

Conclusions : For Macro-pru LEIS must give type of financial Intermediary for multi-sectoral mappings

• However, bilateral data between counterparties has to be mandated and modelled with design of metrics with capacity of early warning signals

• Market price data devoid of early warning capabilities

• Reserve Bank of India has developed financial network based systemic risk mapping and metrics and put it through its paces

• Visualisation and beautiful pics alone not enough • We need network related stability analytics