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GLOBAL FINANCIAL STABILITY

REPORT BY IMF-SEPT11

Chapter 3-Towards Operationalizing Macro prudential policies: When to act?

January, 2012

Symbiosis Institute of Telecom Management

Pune

Assignment by: SYNDICATE VIII

MBA(TM-I) Systems and Finance

Date: 02nd

January, 2012

PRN Number Name

11020541004 Akshay Gupta

11020541008 Ankita Agarwal11020541012 Ashwini Nagotia

11020541022 Wayne David Rao

11020541052 Ritesh Francis Barneto

11020541053 Rohini Kawade

11020541065 Arun Koshy Thomas


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Summary

Macroprudential analysis isa method which looksat thehealth of theunderlying financial

institutionsinthesystem andperformsstresstestsandscenarioanalysistohelpdeterminethe

systems sensitivity to economic shocks. Risk can build up in the financial system over a

period of time and manifest suddenly during a crisis. Two kinds of indicators have been

discussed-slow moving macroeconomic indicatorsand fast moving financial indicators that

predictthatacrisisisimminent.

OperationalizingMacroprudentialpolicieshastwodimensions:-

y Investigationoftheusefulnessoftechniquestoidentifyindicatorsforbuildupofrisky How policyinstrumentscan beappliedto mitigatethe buildupofsystemicrisk

Creditgrowthhas beenusedfordetectingriskbuildup. Rapidcreditgrowthisassociated with

a high probability of a crisis. Therefore, macroprudential instruments should be used

whenever credit boomspose a threat to financial stability. The structural modelused here,

uses variables to extract information on sources of shocks so as to mimic different

economies. Threetypesofshocksareconsideredinthis model.

y Assetprice bubble (~12 consecutivequarters)o Can be viewed as apersistent gap betweenpriceof certainassets and their

fundamentallevel.

o During the bubble, credit risk builds up and materialises when it bursts.

y Loweringofbanklendingstandards (~8 consecutivequarters)o Reflectover-optimisticassumptionsaboutcreditrisk.o Done by bankstoincreaseshareinacompetitive market.

y Anticipatedimprovementsineconomysfundamentalso Gainsexpectedfrom future FDIinflows.o Theexpected movementexpandstheeconomysproductionfrontier.o Actualimprovement witnessedafter12 months.

In reality, all three shocks most often occur together. Behaviourof four indicators for the

threeshocks:


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Findings:

y Credit-to-GDPratioincreasesinallcases.Althoughitsignalsundesirablespeculativepaths,itcanalsoindicateahealthycycle (figure1)

y Thedirectionofchangeofvariablesissimilar.y Thetrade-balancecurveshowsthatitdeterioratessuddenlyforproductivityshockand

laxlendingandissmootherfortheasset bubble.

y The bank capital adequacy ratiodips steeplydue toshocks of asset bubbleand laxlending.

y Structural elementsof the economydonot make muchadifferenceascompared tofinancialsector.

Leading indicators of financial sector distress

y Leading Indicators: Early recognitionof the risk buildup allows financial sector tocreateliquidity buffersandaccumulatecapitaltoavertthecrisis,andisdonethroughslow moving,financial balancesheetaggregates.

y Near-coincident indicators: Ability to predict the incidence of a period of highfinancialstressthatprovidesufficientleadtimeforpolicymakerstoact.

A supplemental setof indicators waschosen which moves together with credit aggregates,

capitalinflows,assetpricesandrealeffectiveexchangerates.


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Extracting information from credit aggregates to forecast financial crises

y Eventstudyy Noisetosignalratioy Receiveroperatingcharacteristic1. Event studyy Financialstress identifiedonacountry-by-country basisat the fifthpercentileupper

tailofthe FSI.

y Distress episodesare identified; month for initial FSIrealization is termed to be thesignal monthfordistress.

y It also considers measures of house prices, foreign liabilities, and exchange ratedynamics.

2.

Noise-to-signal ratioy Usesvariablesandpredict beyondthreshold

values.

y Workingofsignalling methodologyThe predictive value of crisis signal is

assessed according to whether itpredicts a

crisis.

Afailuretosignalacrisisthathappens isa

Type 1 Error [C/ (A+C), from fig.] and a

false signal not followed by a crisis is a

Type 2 Error[B/ (B+D),from fig.]

y Two types of errors are compared by means ofNSR.

y High NSR => too many false signals andmissingactualcrises.

y Low NSR => predictionofmanycrises withoutmuchoffalsesignals.

3. Receiver Operating Characteristic

y Determinesdiscriminatorypowerofsignallingvariables


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y A ROC curveisplotted withshareofobservationsofthesignallingvariableonx-axisandfractionofcrisescapturedony-axis.

y Predictivepowerofsignallingvariable = area between ROC curveand 45 line.y Avariablelyingonthe 45linedoesnthaveanypredictiveability (areaof0.5).y Here,theareais 0.57, whichpredicts ~ 34% ofallcrises.

Results:

y Countries with fixedexchange rateshavehigher creditgrowth than average justifyingthatanyshockpropagates morestronglyinfixed/managedexchangerateregimes.(fig F)

y Change in houseprices increased to a tune of 10-12% nearly 2 years before thecrisis.(figH)

y Foreignliabilities & external borrowingsoftheprivatesectoralsoincreased.

These results indicates that though credit growth is apotentially good leading indicator,

variableslikeassetprices,realeffectiveexchangerates,etc.needto beconsideredalongside.

Panel data regressions

Aregressionconductedtoestimatetherelationship betweencredit-to-GDPgapandchangein

credit-to-GDPratiohadastatisticallysignificanteffectoncrisisprobabilities.

Near coincident indicators of imminent crisis

Theseareusedtosignalimminentstressandcrisis. The most widelyusedoneis:

y Conditionalvalue-at-riskvaries withtheLIBOR-OISspreadandtheyieldcurve isahighfrequency measure.

The CoVaRis theValueat Riskofthefinancialsystem conditionaloninstitutions beingunderdistress.

An institutionscontribution tosystemic risk is thedifference between the CoVaR for tail-risk episodes

andthe CoVaratthe medianstate. Thetime-varying CoVaRisestimated byquantileregressionsofthe

returnsofthefinancialsystem onthereturnsofaninstitutionandotherstatevariables


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Risk Materialization:

y Robustnessofagroupofnear-coincidentindicatorsofsystemicfinancialstresscan bedeterminedviavariouseconometrictechniques.

y Anindicatorcombininginformationfrom theyieldcurveandtheLIBOR-OISspreadwasassumedto workbestfortheUnitedStates.However,theindicatorsdidnotpoint

outrisinginterconnectednessofLehman Brothers beforetheirrespectivefailure.

Inthiscaseusingthecurrentcrisisastestinggroundforhighfrequencytwonew indicators

wereintroduced.

1. Systemicfinancialstressand2. asubsetofSFSobservationsisextremeSFSy ForU.S, The set of high-frequencynear-coincident indicators is then tested against

boththeSFSatareasonablehorizon withreasonablelikelihoodandthishelpspredict

changesintherealeconomy.

y 10 near-coincidentindicatorsofsystemicriskarethenranked bytheaveragescoresfrom 0 (worst)to 1 (best)onthethreetests.

y Providinganearlyturning


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y Basedonthescores, CoVartakesintoaccounttwoadditionaltime-varyingvariablesin the methodology: (i) a yield curve and (ii) the LIBOR-OIS spreadis the best

overallperforming near coincident indicator (second figure). The JPoD is able toforecastextremesystemicstressevents wellanddoesless wellinforecasting.

y The macroprudentialpolicies and indicatorsneed to go hand inhand to reduce thefinancialsectorsexposuretosystemicrisk.

y Aparallel analysis of a fixed exchange rate economy and flexible exchange rateeconomy shows thatproperly designed time-varying capital requirements for banks

canhelp mitigatefinancialcyclesforeconomies withdifferentexchangerateregimes.

y Countercyclical capital buffers work to reduce risks of financial and economicdisruptions.

y Knowledge of the type of shock is relevant to avoid the costly imposition ofmacroprudentialtools whentheyarenot warranted.

y Accurate identification of sources of shocks is essential and differences in thefinancialstructureoftheeconomychangethe magnitudeoftheeffectsofshocks but

nottheirdirection.

y Among slow-moving indicators, credit aggregates are useful but need to becomplemented byotherindicators.

y Theregressionresultsindicatethat Creditandliquidityrelated measuresaregenerallyeffectiveinreducingprocyclicality.

STRUCTURAL MODEL:

The behaviorofindividualagentsintheDynamicStochasticGeneral Equilibrium (DSGE)is

derived from explicit optimizingproblems, while aggregate outcomes arises as a result of


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general equilibrium conditions assumed toprevail at all times. The novel feature of this

modelistheendogenousfeedbackloop betweenarealeconomyandafinancialsector.Letus

lookatthesectors which mimicthis model.

REAL SECTOR

This sector mimicsa standard small open-economyDSGE model with sufficient shortand

medium term imperfectionstogeneraterealistic businesscycledynamics.

CHARACTERISTICS:

y Asingleproductionfunctionsforbothlocalandinternational markets.y Exports are assembled by the combination of local value added with re-exports in

fixedproportions.

y The model wasstructuredto workfordifferenttypesofopeneconomiesy Householdsactasconsumers,investorsandsupplylabor.

BANKS

y Banksassetrelateddecisionsandliabilityrelateddecisions.y Monetarypolicy was characterized by a simple inflation. Bank Capital is therefore

subjectto microprudentialcapitalrequirements.

PARAMETERIZING THE MODEL

y Four basic groups ofparameters viz., steady state, transitory,policy and financialwerearrivedatafteranalyzingemerging marketaroundthe world.

y The steady state parameters were calibrated with various long run structuralindicators.

y The modelpresentationsshould be considered moreas thinking devices rather thanempiricallyaccuratepredictions.

PREDICTING PROBABILITY OF BANKING CRISIS

Theprofitpanel data model with country fixed effects gives the relationship between the

probabilities of a banking crisis asa functionof as vectorof system risk indicators and is

mathematicallygivenas:

Pr(yI,t=1|xi,t-h) = ( I+xI,t-h)


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WherePrdenotesa binary bankingcrisisvariable

xI,t-hisarow vectorofindicatorvariables

I denotesthefixedeffectforcountry

isthecumulativedistributionfunctionofastandardnormaldistribution

isacolumnvectorofunknownparametersto beestimated

Basedonthis, Bankingcrisisissystemiciftwoconditionsviz.,significantsignsofdistressin

the banking system and significant bankingpolicy interventions in response to significant

lossesinthe bankingsystem arepresent.

y Theuseofprofitframeworkimpliesthatthe marginaleffectisnonlinearanddependsonthevalueofiandtheleveloftheindicatorvariables.

y Thefixedeffectsdenotethetimeinvariantcharacteristicsthataffectcrisisprobabilityinacountry. Countries withfixedeffects 80

thpercentileofallfixedeffectsaretermed

highriskandlowerthan 20th

percentilearetermedlow risk.

y For a high risk country,onepercentagepoint change increase in the CtG gap willincreasetheprobabilityofthesystemic bankingcrisis.

y The CtG ratiohasa significant impacton the crisisprobability at all three forecasthorizons.

FINDING ROBUST SET OF NEAR-COINCIDENTAL INDICATORS

The 10 indicators used were the yield curve, time varying CoVar,Distance to default of

banks, diebold-Yilmaz, Volatility Index, LIBOR-OIS spread, Systemic Liquidity Risk

IndicatorRolling CoVar, Jpodandthe CreditSuisse FearBarometer.

FORECASTING SYSTEMIC STRESS

y Normalsystemicstress wastestedusingtheGranularCausalitytestsdoneon weeklydateand the tests basedonrunning linearregressions withall four lags in thesame

regression.

y The forecasting of extreme stress is done based on McFadden test for the logicregressions.


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EARLY TURNING POINTS:

y Mostsystemicriskindicatorsrarely move beforethecrisis.y Indicatorsstart movingonlyas wegetnearertothesystemicevent.y The QuandtAndrews breakpoint testgives thepossible breakpoint date for eachof

theindicatorsforeachtest.

CONCLUSION:

y Thecentral bankshouldplayanimportantrolein macroprudentialpolicy.y Complexandfragmentedregulatoryandsupervisorystructuresareunlikelyto be

Conducivetoeffective mitigationofriskstothesystem asa whole.y Systemicriskpreventionandcrisis managementaredifferentpolicyfunctionsthat

should besupported byseparatearrangements.y At leastone institution involved in theanalysisofsystemic riskshould beprovided

accesstoallavailabledataandinformation.

y Theinstitutionalarrangementsshouldpromote willingnesstoactandreducetheriskofdelayedpolicyaction.

y The macroprudential mandate should be assigned to a single institution that can beheldaccountableformeetingitsobjectives.

y Mandatesandaccountability mechanismsshouldguardagainstoverlyrestrictiveMacroprudentialpolicy.

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