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System shock analysis and complex network effects


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Joint presentation with Michelle Tuveson and Dr Andrew Coburn from Cambridge Risk Center at the Conference Board Global Risk Conference in New York, 8 May 2013. …

Joint presentation with Michelle Tuveson and Dr Andrew Coburn from Cambridge Risk Center at the Conference Board Global Risk Conference in New York, 8 May 2013.

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    • 1. Analytical FrameworksSystem shock analysis and complex network effectsThe 2013 Global Risk ManagementPre-Conference SeminarMichelle Tuveson, Executive Director, Cambridge Centre for Risk StudiesAndrew Coburn, Director External Advisory Board, Centre for Risk StudiesDr Kimmo Soramäki, Founder and CEO, Financial Network Analytics
    • 2. Analytical Frameworks: System shock analysis and complex network effectsSession Outline Michelle TuvesonExecutive Director, Centre for Risk Studies, University of Cambridge– A Framework for Managing Emerging Risks in International Business Systems– Problem statement: emerging risks as a corporate problem, the CambridgeFramework as a structure for approaching the problem Dr Andrew CoburnDirector of External Advisory Board, Centre for Risk Studies, University of Cambridge– Developing Scenarios for Managing Emerging Risks– Methodology: structural modeling of scenarios and their consequences;examples of scenarios for extreme oil prices Dr Kimmo SoramäkiFounder and CEO, Financial Network Analytics– Understanding Shock Effects on Business Systems and Investment Portfolios– Solutions: networks and interactivity, investment portfolios, illustration ofnetwork modeling
    • 3. A Framework for Managing Emerging Risksin International Business SystemsThe 2013 Global Risk Management Pre-Conference SeminarAnalytical Frameworks: System shock analysis and complex network effectsMichelle TuvesonExecutive DirectorCentre for Risk Studies, University of Cambridge
    • 4. Some Recent Events Disrupting International Business4Hurricane Sandy 2012impacted a region that generates 40% of US economy.Flights from many airports disrupted. Eastern sea portclosures disrupted international shipping for weeksArab Spring 2011-12Impacts on many international businesses. Increased fuelprices. 22% of businesses globally reported that the unresthas a negative impact on their businessCredit Crunch 2008US housing price crash in 2007 caused liquidity crisisimpacting all major economies and triggering lengthyrecession , impacting global businessesJapan Tōhoku Tsunami 2011Killed 26,000, destroyed factories andinfrastructure, triggered Fukushima nuclear meltdown.Disrupted supply chains for electronics and other high-techcomponentsSwine Flu Pandemic 2009caused international panic with initial reports of a highvirulence virus, leading to travel and business disruptionfor many weeksThailand Floods 2011Manufacturing regions in Chao Phraya flood plainsinundated disrupting supply chains for internationalbusinesses . Large contingent business interruption claims
    • 5. And the list goes on… Volcanic eruption of Eyjafjallajökull, Iceland, 2010, closed airports across Europe for twoweeks. Business sectors worst hit, included fresh produceproviders, pharmaceuticals, and electronics In 2010 piracy activity around Horn of Africa reached an unprecedented level of 490 actsof piracy, and an estimated $12bn in costs incurred, leading to re-routing, delays, andcost escalation for shipping routes between Europe and Asia Unprecedented multi-national General Strikes were coordinated acrossPortugal, Spain, Italy and Greece in November 2012, leading to impacts on airtravel, telecoms, and many other business sectors 7/7 2005 terrorist attack on London caused the closure of the City’s financialcentre, airports and local travel systems, and impacted international business activity North American Blizzard of 2010 affected most of US with record snowlevels, suspending travel services, international flights and shipping with waves ofsnowfall through Feb and March Deepwater Horizon oil spill in 2010 made large parts of the Gulf of Mexicounnavigable, caused damage to local industries and disrupted international businessconnected to the region SARS outbreak in 2003 disrupted airline passenger traffic for five months, depressingtourism, travel and other business5
    • 6. The Problem Modern corporate businesses are finding that their processes aremore prone to disruption than they expected– Each geo-political event causes surprise This is a result of globalization – corporate systems now reach acrossthe world and are impacted by many more hazards and localizedchanges than ever before Global business systems have been optimized to minimize cost – thisreduces safety margins There is a new operational focus on ‘resiliency’ To understand and measure resilience requires a new framework– The Cambridge Risk Framework Many corporates are espousing new approaches to managing‘emerging risks’– The Cambridge Risk Framework aims to provide tools for this management6
    • 7. Japan Tōhoku CatastropheDisruption to Business Systems7“Sonys production and sales were severely affected by the earthquake and tsunami in Japan in Marchlast year.The twin disasters resulted in supply chain disruptions and a shortage in power supply in Japan, forcingSony to curtail production.Its fortunes were hurt further by floods in Thailand later in the year, which saw its factories in thecountry being affected.”
    • 8. The Cost of Disruption Examples of daily cost impact of a disruption in a company’s supply networkbeing $50-$100 million– Rice and Caniato (2003) Studies of ‘long-run’ equity values of companies following disruption to supplychain show:– Average abnormal stock returns of -40% for firms suffering disruptions– Shareholders lose average of 10% of their stock value at announcement– 14% increase in equity risk in the year following a disruption announcement– Firms do not quickly recover from the negative effects of disruptions– Source: Hendricks & Singhal, 2005 (sample of 827 disruption announcements made during 1989–2000) 2004 Survey of top executives at Global 1000 firms showed supply chain disruptions andassociated operational and financial risks to be single greatest concern– (Green, 2004) Current trends in best practice for managing the risk of international disruption:– Cost management and efficiency improvements– Supply base reduction– Global sourcing– Sourcing from supply clusters– Source: Craighead et al., 2007, The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities8
    • 9. The Current Challenge of Managing ‘Emerging Risk’ Modern businesses face a large number of ‘Emerging Risks’ Many companies maintain an emerging risk committee or havea formal monitoring system in place– Much of this work is ad-hoc ‘Emerging Risks’ also include emerging recognition of long-standing threats Is there a systematic process to assess and evaluate the entirerange of threats? How are these threats best managed? Can we also assess the positive opportunities and upsidepotential that might be presented by new threats? What financial products or techniques could best answer thecorporate demand for de-risking global business?9
    • 10. Catastrophe Modeling Meets Complex Systems The Centre for Risk Studies arises from shared interests by theparticipants in exploring areas of intersection between– Catastrophe modeling and extreme risk analytics– Complex systems and networks failures Advance the scientific understanding of how systems can be mademore resilient to the threat of catastrophic failures10Air Travel Network Global EconomyTo answer questions such as:‘What would be the impact ofa [War in Taiwan] on the [Air Travel Network] and how would this impact the [Global Economy]?Regional Conflict
    • 11. Business Activity as a System of Systems11Air Travel Network Cargo Shipping NetworksCommunications Networks
    • 12. Networks, Attacks, and Residual Modeling A framework for assessing the consequences of an event on a system network12Network ‘Attack’ Residual Describe the topology of the networkas nodes and links Baseline efficiency of the networkquantified through standard metricsof Value Function:• Connectivity• Reference path length• Diameter• Social Welfare Degradation of the network throughlocalized impairment or removal ofnodes and links Attack measured by ‘k-cut’ metrics Post-attack network either static oradaptive• Network may be fragmented after an attack Adaptive response of a network adjuststraffic and relationships May introduce congestion Changes in Value Function aremeasured as a result of the attack
    • 13. Components ofCambridge Risk Framework13Threat ObservatoryNetwork ManagerAnalytics WorkbenchStrategy Forum
    • 14. Cambridge Risk FrameworkThreat Taxonomy14FamineWater Supply FailureRefugeeCrisisWelfare SystemFailureChildPovertyHumanitarianCrisisAidCatMeteoriteSolar StormSatellite SystemFailureOzone LayerCollapseSpaceThreatExternalitySpaceCatOtherNextCatLabour DisputeTrade SanctionsTariffWarNationalizationCartelPressureTradeDisputeTradeCatConventional WarAsymmetric WarNuclearWarCivilWarExternalForceGeopoliticalConflictWarCatTerrorismSeparatismCivilDisorderAssassinationOrganizedCrimePoliticalViolenceHateCatEarthquakeWindstormTsunamiFloodVolcanicEruptionNaturalCatastropheNatCatDroughtFreezeHeatwaveElectricStormTornado &HailClimaticCatastropheWeatherCatSea Level RiseOcean System ChangeAtmospheric SystemChangePollutionEventWildfireEnvironmentalCatastropheEcoCatNuclear MeltdownIndustrial AccidentInfrastructureFailureTechnologicalAccidentCyberCatastropheTechnologicalCatastropheTechCatHuman EpidemicAnimal EpidemicPlantEpidemicZoonosisWaterborneEpidemicDiseaseOutbreakHealthCatAsset BubbleFinancial IrregularityBankRunSovereignDefaultMarketCrashFinancialShockFinCat
    • 15. Profile of each Macro-Threat ClassWe are preparing a monograph on each ofthe key threat categories: State-of-knowledge summary of the science Identify the leading authorities and publicationson the subject Catalogue of historical events Map the geography of threat Define an index of severity (‘magnitude scale’) Assess a first-order magnitude-recurrencefrequency (worldwide) Provide illustrative ‘Stress Test’ scenarios oflarge magnitude events– For e.g. 1-in-100 (or 1-in-1,000) annual probability System impact (vulnerability) knowledge Assessment of uncertainties15
    • 16. Adopting Cambridge Threat Taxonomyas an Industry Standard In September 2013, Munich Re will be co-hosting a workshopto review the CRS Threat Taxonomy v2.0 for use in emergingrisk management processes Attendees include major corporations, model developers andinsurance companies Objective is to produce a version 3.0 for use by Munich Re andothers for use as an industry standard Others are welcome to participate– Invitation to attend the workshop– Or review the proposed standard during consultation stage– Participants should be interested in adopting the standard for theirown use in risk management16
    • 17. Conclusions Many international corporates now recognize theimportance of managing emerging risks in their globalbusiness Managing emerging risks needs a framework for– Understanding the interlinkages in global business systems– Assessing all the different types of threats that might impactthose business systems The framework can be used to develop shock testscenarios for use in risk management17
    • 18. Developing Scenariosfor Managing Emerging RisksThe 2013 Global Risk Management Pre-Conference SeminarAnalytical Frameworks: System shock analysis and complex network effectsDr Andrew CoburnDirector of External Advisory BoardCentre for Risk Studies, University of Cambridge
    • 19. Using Scenarios for Risk Management Many companies use ‘what-if’ scenarios forunderstanding and managing risk Management science is well developed– Use of scenarios in business strategy since 1960s Scenario planning proved to create business value– Companies like Shell place great value in their scenariounit, and attribute it with anticipation of the 1970s oilcrisis, and rapid response to 2008 financial crisis Scenarios– Create management flexibility– Improve resilience to a crisis– Challenge management assumptions about status quo19
    • 20. Seven Key Lessons for Developing Scenarios1. Make it plausible, not probable2. Ensure that the scenarios are disruptive and challenging3. Offer two scenarios for a situation, not one or three4. Make the suite of scenarios equally likely5. Quantify the consequences of the scenario6. Ensure scenarios are ‘coherent’7. Make the scenarios relevant to the management team20
    • 21. Example Scenarios Currently in Development21Cyber Catastrophe RiskMajor compromise of commercial and national infrastructure IT systems bymalicious worm attackGeopolitical Conflict RiskRegional conflict in South China Sea embroiling Western military powers and SEAsian nationsHuman Pandemic RiskVirulent influenza pandemic causes 6 months of workforce absenteeism andsocial and economic disruptionCivil Disorder RiskAusterity-driven riots and strikes across multiple cities in several Eurozonecountries
    • 22. Oil Supply Shock Analysis22Hypothetical Scenario ofa Geopolitical Crisis in Middle East
    • 23. Disclaimer This is a hypothetical scenario developed as astress test for risk management purposes It does not constitute a prediction The Centre for Risk Studies develops hypotheticalscenarios for use in improving business resilienceto shocks These are contingency scenarios used for ‘what-if’studies and do not constitute forecasts of what islikely to happen5/9/2013
    • 24. System Shock ProjectHow might…24A geo-political event …impact the global price ofcrude oil……and how would that affecta typical investment portfolio..?$
    • 25. Oil Price Shock Scenarios25Forcing Oil Price to an Unprecedented LowShale oil bonanza from large reserves in China turns China into anet producer, causing rapid oil price collapse on global marketsForcing Oil Price to an Unprecedented High‘Arab Spring’ regime change in Saudi Arabia deregulates OPEC-Swing oil production and triggers extreme oil price escalation
    • 26. Project Team26Andrew CoburnMichelle TuvesonDanny RalphSimon RuffleGary BowmanLouise PryorKimmo SoramäkiSamantha CookChristian BrownleesWith assistance from:Peace and Collaborative Development NetworkIvan UretaAssociate Prof in International RelationsInvestment FundWill BeverleyHead of Macro Research
    • 27. Sample Investment Portfolio27USEquities11%UKEquities7%EUEquities10%JapaneseEquities6%Asiaex-JapanEquities6%SmallCapEquities6%EMEquities4%GovernmentBonds11%CorporateBonds4%High YieldBonds12%Property8%PrivateEquity4%Gold6%Commodities3%Cash2%
    • 28. Historical Oil Price Shocks28
    • 29. Basic Structure: Price of OilDemand- Transport-Transport excl. cars- Heating/ElectricitySupply-Saudi & Kuwait- OPEC-Non OPECDemand/SupplyEquilibrium
    • 30. Oil Prices Driven by Global GrowthPrices of commodities tend to be:• Log-normal-ish, but• fat-tailed• mean reverting• with sudden jumpsPrices of commodities tend to be:• well-correlated to global economy• cyclical• seasonal
    • 31. Spot Price($/B)Initial SpotPrice ($/B)Price AdjustmentMust be between 0 and 2PriceAdjustmentDelayDelta: PA Now -PA DelayFutures OilPrice ($/B)Initial FuturesOil Price ($/B)DifferenceFutures/SpotFutures/SpotPriceAdjustmentFuture delay($/B)Futures/FuturesDelay PriceAdjustmentMarketSentiment market adjInital MarketSentimentmarket adjoutput<Prod - Cons 1month delay (B/M)>Ideal Production -Consumption (B/M)Ideal D/S - ActualD/S (B/m)Demand/SupplyPrice AdjustmentCommercialInventory Adj<CommercialInventory Flows(B/M)>ExogenouseventSpot Price 1Month Delay($/B)<Strategic InventoryFlows (B/M)>StrategicInventory Adj<Prod - Cons 1month delay (B/M)>ST geopolitics<Exogenousevent><OPEC Supply constraints:Politics/embargos/wars(B/M)>Conversion Delay 1Exo EveGeopolticsST geoModeling of Crude Oil Spot Price
    • 32. Scenario Initiation Two months of initial unrest leads toincreasing levels of violence and anti-government protest in Saudi Arabia Initial dissatisfaction is driven by socialconditions but is rapidly taken up byneo-Arab nationalism and minority ShiaIslamic fundamentalism Suspicion of support to rebels beingprovided by Shia groups in MiddleEast, including Iran and Hezbollah32
    • 33. Seizure of Refineries and Oil Production Mass-movement leads to loss of controlof major oil production facilities asprotestors occupy refineries– Ras Taruna (0.5 m barrels/day)– Yanbu (1m barrels/day)– Multiple others Many thousands of armed protestorsoccupying sites, taking hundreds ofwestern workers as hostages Military stand-off as Saudi and US forcesare unable to retake facilities withoutjeopardizing civilian hostages Sudden loss of production of over 1mbarrels a day (10% of Saudi output) Political chaos as leadership falters33
    • 34. Initial StateOverthrowScenario Escalation Event Tree34Anti-western regimeestablishedUS MilitaryInterventionIran HezbollahResponse RegionalEscalationNone -Forced StandoffSwift restitution ofpro-Western regime InsurgencyIranian state-backedmilitary invasionAnnexation ofregional caliphateLengthy militarycampaignChina backing formilitary actionIsraeli counter-strikesand broader ectionsWestern coalitionforces deployedRussia annexes areasof Islamic influenceOther coincidental or triggered consequences can increase the severity of a scenarioACDEB
    • 35. Conflict Escalation Across ‘the Oil Corridor’ Potential for scenario toescalate into broaderregional conflict ‘Oil Corridor’ contains a thirdof the world’s oil Worst case sees prolongedconflict across entire region
    • 36. Arab Spring TimelinesLibya First protests (15 Feb 2011) UN Recognition (16 Sep 2011) End of violence (23 Oct 2011) 251 days36Egypt First protests (25 Jan 2011) Mubarak resigns (11 Feb 2011) Protests end (30 June 2012) 18 days (523 days of unrest)Tunisia First protests (18 Dec 2010) Regime Change (14 Jan 2011) Protests end (9 Mar 2011) 27 days (82 days of unrest)Yemen First protests (27 Jan 2011) Ceasefires and Transitions End of protests (27 Feb 2012) 397 daysSyria First protests (15 Mar 2011) 736 days (ongoing)
    • 37. Oil Production OPEC produces 40% of theworld’s 80 mbbl/d oil andholds three quarters of theworld’s 1.6 tr bbl reserves Oil consumption is well-correlated to global economy– with cyclical and seasonalpatterns Oil Corridor accounts for athird of all oil production OPEC follows Oil Corridor lead37SaudiArabia, 10Rest ofOPEC, 23Non-OPEC, 450102030405060708090MillionsofbarrelsofoilperdayWorld Oil Productionmillions of barrels a dayTotal 80 mbbl/dTotal WorldSaudi ArabiaOther OPECMiddle Eastern Oil Corridor
    • 38. OPEC Swing Saudi Arabia controls the ‘OPEC-Swing’ OPEC Swing is a pricingregulatory mechanism– releases more reserves as pricerises It damps sudden price rises andconstrains market volatility In this scenario, the OPEC Swingmechanism is effectively disabled It enables prices to follow marketsentiment rather than economicfundamentals38
    • 39. Market Reaction: The Black Bubble Market reactions are severe Negative sentiment feedback andpessimistic commentary results ina ‘black bubble’ Oil prices peak at $500 a barrel for3 days Release of government strategicreserves and political commentaryreduces oil pricing to below $300 Sustained period of high oil prices39
    • 40. Modeled Impact on Oil Price$0$100$200$300$400$500$6001 11 21 31 41 51 61 71 81 91 101OilPriceperbarrelCrisis (Days)Oil Price during Saudi Arabia Crisis ScenarioAttack onRas TanuraAttack onYanbu‘OPEC Swing’failureNote – this is a ‘what-if’ illustration ofpotential extreme price patterns not aprediction or estimation of an actualoutcomeDuration of military action
    • 41. Scenario Durations and Impacts410%5%10%15%20%25%30%35%0 20 40 60 80 100 120 140ABCDEDuration: Months before restoration of normal oil productionImpact:% ofworld’s oilproductionaffectedShortRevolutionSuccessful USInterventionUS fights well-resourced insurgencyIranian invasionRegionalConflagrationDurationImpact
    • 42. Sectors Worst Affected42Code Sector Subcode Industry Groups Correlation with Oil Price Shock10 Energy 1010 Energy High + 315 Materials 1510 Materials High - -32010 Capital Goods Medium - -22020 Commercial & Professional Services Low - -12030 Transportation High - -32510 Automobiles and Components Medium - -22520 Consumer Durables and Apparel Medium - -22530 Consumer Services Medium - -22540 Media Medium - -22550 Retailing Medium - -23010 Food & Staples Retailing High - -33020 Food, Beverage & Tobacco Medium - -23030 Household & Personal Products Medium - -23510 Health Care Equipment & Services Low - -13520 Pharmaceuticals, Biotechnology & Life Sciences Low - -14010 Banks Medium - -24020 Diversified Financials Medium - -24030 Insurance Medium - -24040 Real Estate Medium - -24510 Software & Services Low - -14520 Technology Hardware & Equipment Low - -14530 Semiconductors & Semiconductor Equipment Medium - -250 Telecommunication Services 5010 Telecommunication Services Low - -155 Utilities 5510 Utilities Medium + 235 Health Care40 Financials45 Information Technology20 Industrials25 Consumer Discretionary30 Consumer StaplesFew sectors are not negatively impacted by a severe oil price
    • 43. Understanding the Implications of a High Oil Price Businesses can trace the implications of high oil prices onall their business operation costs and opportunities Sectoral impacts have marginal differences Affects overall macro-economic environment– Transportation of all goods to market cause spirals of costinflation– Severe curtailment of demand through increased pricing– Recessionary forces– Alternative sources of energy become more attractive andeconomically viable A major impact is investment portfolio asset movements43
    • 44. What Other Scenarios Should a Business Consider? As an alternative to contingency planning for a world ofextreme high energy prices, there are scenarios forextreme low prices of energy– The Shale Oil Bonanza These may have opposite implications and contingencyrequirement There are also several scenarios for extreme impacts onbusiness systems and operational continuity that areplausible– Pandemics; cyber-catastrophes; severe weather; environmentalcollapse; Drives emphasis on flexibility of thinking, and resiliencyto cope with unexpected shocks44
    • 45. Conclusions Scenarios are useful tools for business planning tochallenge assumptions about the status quo Can be used as stress tests to a five-year plan and ascontingency plan requirements Scenarios have proved their business value in helpingbusinesses have more agile reactions to unexpectedevents The Cambridge Centre for Risk Studies will be publishingand releasing scenarios for use with models of networkedbusiness systems to fully understand potential effects45
    • 46. Understanding Shock Effects onBusiness Systems and Investment PortfoliosThe 2013 Global Risk Management Pre-Conference SeminarAnalytical Frameworks: System shock analysis and complex network effectsDr Kimmo SoramäkiFounder and CEOFinancial Network Analytics
    • 47. Systemic Risk ≠ systematic riskThe risk that a complex system composed of many interactingparts fails (due to a shock to some of its parts).Domino effects, cascading failures, financial interlinkages, … ->i.e. a process in the financial networkNews articles mentioning “systemic risk”, Source:
    • 48. Network TheoryMain premise of network theory:Structure of links between nodesmattersLarge empirical networks aregenerally very sparseNetwork analysis is not analternative to other analysismethodsNetwork aspect is an unexploreddimension of ANY data48
    • 49. 49For example:Entities:100 banksVariables:Balance sheet itemsTime:Quarterly data since 2011Links:Interbank exposuresInformation on the links allowsus to develop better models forbanks balance sheets in times ofstressNetworks brings us beyond the Data Cube"The Tesseract"
    • 50. Observing vs Inferring Observing links– Exposures, payment flow, trade, co-ownership, joint boardmembership, etc.– Cause of link is known Inferring links– Observing the effects and inferring arelationship e.g. via correlations– Cause of link is unknown– Time series on asset prices, tradevolumes, balance sheet items50
    • 51. Inferring Links from Asset PricesIssues:– Prices vs Returns (arithmetic vs log)– Controlling for Common Factors (PCA)– Correlation (Pearson, rank, ...) vs dependence (partialcorrelations, tail, normal, regimes)– Time period (short vs long)– Significant and Multiple Comparisons -correction-> Goal is to uncover links or relationships that form a network
    • 52. Benefit of Visualization52Mean of x 9Variance of x 11Mean of y ~7.50Variance of y ~4.1Correlation ~0.816Linear regression:y = 3.00 + 0.500xAnscombes Quartet: Constructed in 1973 by Francis Anscombe todemonstrate both the importance of graphing data before analyzing it andthe effect of outliers on statistical properties
    • 53. Visualizing CorrelationsCalculate pairwise correlations for 31ETFs in various geographies and assetclasses(465 correlations)Color code correlations:Problem:We are making many estimates, someof which are likely false positives-1 +12007-20082012-2013
    • 54. 54Example - Distribution of correlation in 30 trialswith random numbers20 pairs 50 pairs100 pairs 200 pairs
    • 55. Significant CorrelationsKeep statistically significant correlationswith 95% confidence levelCarry out Multiple comparison -correction -> Expected error rate <5%Problem:Heatmaps can be misleading due tohuman color perception2012-2013Last month
    • 56. About Color PerceptionA and B are the sameshade of gray
    • 57. About Color PerceptionA and B are the sameshade of gray
    • 58. Correlation NetworkNetwork layout allows for the display ofmultiple dimensions of the same dataset on a single map.
    • 59. Correlation NetworkNodes (circles) represent assetsand links (lines) representcorrelations between thelinked assetsNode size scales with varianceof returns.Thicker links denote strongercorrelations (red=negative, black=positive)
    • 60. Hierarchical structure in financial markets60
    • 61. Minimum Spanning TreeA Spanning Tree of a graph is a subgraph that:1. is a tree and2. connects all the nodes togetherMinimum spanning tree (MST) is a spanning tree with shortest length. Lengthof a tree is the sum of its links.
    • 62. Re-positioning the AssetsWe lay out the assets by theirhierarchical structure using MinimumSpanning Tree of the asset network.Shorter links indicate highercorrelations. Longer links indicatelower correlations.Negative correlations are shown asred links and positive correlations asblack.Absence of links marks that asset isnot significantly correlated withanythingInteractive chart at:
    • 63. Data Reduction for ClarityNode color indicates identifiedcommunity.Missing links (clusters) denoteno significant correlation.Interactive chart at:
    • 64. Extensions Principal Component Analysis and Correlationregimes GARCH -based forecasts Alternative link definitions:Granger causality, partial correlation, taildependence Outlier detection and alert systems Stress testing
    • 65. Partial CorrelationPartial correlation measures the degree of association between two random variables, controllingfor other variablesWe build regression models for daily returns of e.g. Oil and Gold based on all other assets ofinterest and look at the correlation of their model residuals (i.e. what is left unexplained by theother factors) -> Partial correlationModel 1: Regress Gold on all other assets except OilModel 2: Regress Oil on all other assets except GoldGold residuals = vector of differences between observed Gold values and values predicted byModel 1Oil residuals = vector of differences between observed Oil values and values predicted by Model 2Partial correlation between Oil and Gold is the correlation between Oil residuals and Gold residuals65
    • 66. Partial Correlation NetworkNetwork of statistically significantpartial correlations of monthly returnsfor a wide set ETFs during 2007-2013Link width is value of particalcorrelation (range up to 0.85)We can use the partial correlations toundestand linkages within a standardportfolio stress test modelWe organize the network on the basisof distance from the shocked node:
    • 67. The Network for an Oil ShockInteractive chart at:
    • 68. Shocking Multiple Nodes We use multivariate percentiles (based on the multivariate normaldistribution) to simultaneously shock Financials, German Stocks and Gold First we estimate the mean and covariance matrix of these three assetreturns from theobserved data. Then, for the first percentile, we find the shocks x, y, and z such that thejoint probability P(XLF < x AND EWG < y AND GLD < z) = 0.01 and themarginal probabilities are equal, i.e., P(XLF < x) = P(EWG < y) = P(GLD < z) A similar calculation finds the 99th percentile.
    • 69. The Network for Multiple ShocksInteractive chart at:
    • 70. Is it Correct? We develop a model where we use the network structure to estimate manysmall models (some of which are based on estimates) We see how well cascading predictions works by predicting values for a outof sample data set whose values are known. We compare results to a normal linear model Result: Predictions based on partial correlation network are as good forsingle asset shock, and just slightly worse for multiple asset shock-> The partial correlations do open up the model and provide more insights into assetdynamics and interdependencies Caveats: shocks outside normal bounds may not exhibit same behavior. Shocks tocorrelations, volatilities are not covered.
    • 71. Summary Correlation networks can provide visual insights into marketdynamics Partial correlation networks can provide visual insights forportfolios stress testing
    • 72. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki