HowGPUsEnableXVAPricingandRiskCalculationsforRiskAggregation

JamesMesney |PrincipalSolutionsEngineer|jmesney@kinetica.com

SettingtheScene

WhatisXVA?

3

X-Value Adjustment (XVA) refers to “Valuation Adjustments” in relation to derivative instruments held by banks.

The “X” in XVA means “C”forcredit,“D”fordebt,“F”forfunding,“K”forcapital…

“Doing”XVAisRiskModelling.

It’sallaboutcomputingpotentialRISK,nowandinthefuture.

OBJECTIVE:INSULATETHEBANKFROMRISKWHEREVERPOSSIBLEANDALLOCATETHERIGHTCAPITAL

WhyisXVANeeded?

4

Pre 2007 - Trades cleared at fair valuations • Costs for capital and collateral were irrelevant to

banks’ investment decisions

• Increasingly complex investment portfolios emerge of variable value

ElevatedCapitalisation+CollateralisedTrades=EXPENSE&REDUCEDPROFITABILITY!

HowcanbanksoperateefficientlyAND playbytherules?

ANSWER=LOTSOFDATA+CLEVERFORECASTINGMODELS,LOTS OFCOMPUTE

4

Post 2007 – MAJOR REFORM!! Counterparty Credit Risk and Basel III Accord

XVAChallenges

• ToComputeRisk• Continuallyandcomprehensivelymeasuretradingactivity,currencymovements

• Consistentlyandtimely– attradingspeed…Batchprocessingisnolongersatisfactory

• 100,000sormillionsoftradesperday• 10,000sofcounterparties… in20+currencies!

• Calculations– ComplexandTimeCritical• ComputeintensiveworkloadsarewellsuitedfortheGPU

• REALTIMEadjustmentcalculationsareverycomputationallyintensive

• MonteCarloSimulation

55

LifeAfterMoore’sLaw40YearsofMicroprocessorTrendData

1980 1990 2000 2010 2020

102

103

104

105

106

107

Single-threaded perf

1.5X per year

1.1X per yearTransistors(thousands)

Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp

SpecINT

Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp

1980 1990 2000 2010 2020

102

103

104

105

106

107

Single-threaded perf

1.5X per year

1.1X per year

GPU-Computing perf1.5X per year 1000X

By 2025

TheRiseofGPUComputing

SpecINT

TheCPUBottleneck

8

Withlimitedprocessinggainsonthehorizon,CPUsarefurtherandfurtherbehindthegrowthindata

8

GPUAccelerationOvercomesProcessingBottleneck

4,000+coresperdeviceversus~16coresper

typicalCPU

Highperformancecomputingtrendtousing

GPU’stosolvemassiveprocessingchallenges GPUaccelerationbrings

highperformancecomputetocommodityhardware

Parallelprocessingisidealforscanningentiredataset&bruteforce compute

9

DeployingXVA

XVAArchitecture

11

ETL/STREAMPROCESSING

ONDEMANDSCALEOUT+

Server1

SQL

NativeAPIs

PARALLELINGEST Export

CustomConnectors

In-DatabaseProcessing

SQLBIDMach

MLLibs

BIDASHBOARDS

BI/VISUALISATION

CUSTOMAPPS

KINETICA‘REVEAL’

BATCH&STR

EAMINGDA

TA

PythonJavaC++CUDA

Server'n'

LiveTradingdataCounterparties

OptionsCurrencies

FuturesMarket

ForeignExchangeBloomberg

Reuters

+DOWNSTREAMAPPS

XVAModel

Kinetica’sXVADeploymentatMajorMultinationalBank

12

Largefinancialinstitutionmovescounterpartyriskanalysisfromovernighttoreal-time.

• DatacollectedandfedtoXVAlibrarywhichcomputesriskmetricsforeachtrade

• XVAoutputsstoredinKineticadatabase

• Flexiblereal-timemonitoringbytraders,auditorsandmanagement

• Dataretainedforhistoricanalyses,machine-learning…

FinancialServices:AnIndustryInTransition

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Threeformidableforces—aweakglobaleconomy,digitization,andregulation—threatento

significantlylowerprofitsbyasmuchas$90Bfortheglobalbankingindustryoverthenextthreeyears.

Source:McKinsey&Co,PwC

FinancialServicesenterprisesmustreinventtheirbusinessbytransformingthecore–

Resilience,Reorientation,Renewal

Kinetica for:Resilience– ManageRevenue,Costs,Capital,andRisksReorient– Customer-centricity,Digitization,OpenBank

Renewal- NewMarkets,Products,Customers

Kinetica:ADistributed,In-Memory,GPUDatabase

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GPU-accelerateddatabaseoperations

Naturallanguageprocessingbasedfull-textsearch

NativeGISandIP-addressobject

support

Realtimedatahandlerstoingeststructuredand

unstructureddata

Deepintegrationwithopensourceandcommercialframeworksandapplications:TensorFlow,Hadoop,Spark,NiFi,Storm,Kafka,Tableau,

KibanaandCaravel

Predictablescaleoutfordataingestionand

querying

Notypicaltuning,indexing,andtweaking

Distributedvisualizationpipelinebuiltin

KineticaEnablingBroadEnterpriseSolutions

RETAIL/CPGOmni-Channel

CustomerExperienceSupplyChainOptimization

TargetedMarketing

UTILITIESSmartMeters

SmartGridOptimizationInfrastructureModernization

CROSSINDUSTRYReal-TimeAnalyticsConvergeAI&BI

Location-BasedAnalyticsIoT Analytics

FINANCIALSERVICESRiskModelingFinancialCrimesCompliance

CustomerExperience

HEALTHCAREDrugDevelopmentPrecisionMedicine

Patient360

MEDIA/ENTERTAINMENTSentimentAnalytics

RecommendationEnginesAdTargeting

COMMUNICATIONSCustomerChurn

NetworkOptimizationContentTargeting

TRAVELPriceOptimization

CustomerExperienceEquipmentMaintenance

16

Kinetica:UniqueStrengths&Capabilities

16

SuperchargeBI

TakingadvantageoftheparallelnatureoftheGPUKineticadeliverslow-latency,highperformanceanalyticsonlargeandsteamingdatasets

Simultaneouslyingest,explore,analyze,andvisualizedatawithinmillisecondstomakecriticaldecisions.

User-definedfunctions(UDFs)allowfordistributedcustomcompute

directlyfromwithinthedatabase.

Easiertoworkwithlargegeospatialdatasets.

Fast,DistributedDatabaseEngine

InDatabaseAnalytics

NativeGeospatial&VisualizationPipeline

JamesMesney |PrincipalSolutionsEngineer|jmesney@kinetica.com

Thank You!ComegetyourKineticat-shirtandcopyofthenewO’ReillybookatboothG.01!