global financial stability report by imf (1)
TRANSCRIPT
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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.