SAS® - A Point of View on Market Risk VaR

8/6/2019 SAS - A Point of View on Market Risk VaR

1/12

WHITE PAPER

SAS: A P V

Mar Rs VaR


2/12

SAS: A Point o View on MARket RiSk VAR

Table o Contents

Revaluing growing numbers o increasingly complex fnancial instruments ...2

Choosing between numerous approachesor modeling risk actor evolution ..2

Accessing, integrating, cleaning, and maintaining market and position data ..3

Meeting internal and external reporting requirements .....................................6

Deciding at which aggregation levels to set and monitor VaR-based limits ....7

Determining optimal actions, hedges and integration with enterprise risk

management initiatives ....................................................................................7

Summary ..........................................................................................................9


3/12


An industry best practice or estimating the market risk o trading operations

involves projecting prot-and-loss distributions o portolios o nancial

instruments over short time horizons and then summarizing that inormation into

single numbers, such as value at risk (VaR) and expected shortall.

Easy to understand and conceptually straightorward, VaR has long been an

industry standard or estimating market risk. The means by which it is calculated

and used in practice to manage risk, however, present a number o modeling,

data management and reporting challenges. This paper addresses ways in which

SAS can help clients overcome these challenges to better measure and manage

their market risk.

SAS oers a comprehensive platorm or: automating the collection and

preparation o market data; modeling risk actor evolution and instrument

valuation to create prot/loss distributions; and accessing results at their most

granular levels rom interaces that are already amiliar to business users and

quantitative resources.

By eliminating time-consuming manual and redundant data management tasks,

market risk analysts have more time to spend on more productive tasks, such

as exploring strategies or controlling and managing market risk. By providing a

range o modeling approaches that vary in their level o sophistication, market

risk analysts can uncover sensitivities o market risk estimates to model selection

and parameter uncertainty. By comparing the results and time constraints o

dierent modeling approaches, analysts can decide upon the most appropriate

approaches or meeting their internal and external market risk estimation

requirements. Finally, by providing accessibility to results through numerousinteraces, such as a Web browser and Microsot Excel, all levels o business and

quantitative users can explore (at any level o detail) the prot/loss distributions

rom which market risk estimates are derived.

Challenges in measuring market risk with VaR include:

Revaluinggrowingnumbersofincreasinglycomplexnancialinstruments.

Choosingbetweennumerousapproachesformodelingriskfactorevolution.

Overcomingperformanceproblemsduetogrowingtradingvolumesand

instrument complexity to meet existing and uture time constraints.

Accessing,integrating,cleaningandmaintainingmarketdataand

position data.

Meetinginternalandexternalreportingrequirements.

1


4/12


Challenges in managing market risk with VaR include:

DecidingatwhichaggregationlevelstosetandmonitorVaR-basedlimits.

Determiningoptimalactions,hedgesandintegrationwithenterpriserisk

management initiatives.

Revaluing growing numbers o increasinglycomplex fnancial instruments

Derivativesandothercomplexinstruments,suchasstructuredproducts,oftenhave

contingent, path-dependent cash fows. Many such instruments do not have closed-

orm analytical pricing unctions, so numerical techniques (such as lattice building

and Monte Carlo simulation) are employed to value them. Perormance concerns

regarding ull valuation approaches oten lead to the use o analytical approximations

or estimating price changes o complex instruments.

In SAS, internal or third-party pricing models, prepayment models, term structure

models, deault models, credit spread models and deal waterall libraries are all

integrated within one environment so that users have a choice o using ull valuation

or approximations approaches or revaluing all types o instruments in a market risk

simulation.

Choosing between numerous approachesor modeling risk actor evolution

In selecting an appropriate risk actor evolution model, the strengths and weaknesses

o various approaches must be weighed. Tradeos between accuracy and eciency,

internal resource and system constraints, and internal and external reporting

requirements must also be considered. Like any model, a risk actor evolution

model cannot be expected to ully emulate all o the complexities o how risk actors

are likely to move individually and in relation to one another. SAS oers users the

fexibility to pursue a number o dierent approaches or modeling the evolution o

risk actors, including delta normal, historical simulation, and variance-covariance

and model-based Monte Carlo simulation (see Figure 1). Users can simultaneously

run multiple market risk analyses in SAS, each one using a dierent risk actor

evolution approach, and then compare the resulting prot-and-loss distributions andVaR numbers to gain insights into model sensitivity in calculating VaR.

2


5/12


Non-simulation based DeltaNormal

Simulation based Historical Simulation

Variance-Covariance Monte Carlo

Simulation

Model-Based Monte Carlo Simulation

Predefned or ad hoc risk

actor changes

Stress Testing

Scenario Analysis

Scenario Simulation

Figure 1. Risk actor evolution models.

Overcomingperformanceproblemsduetogrowingtradingvolumesandinstrument

complexity to meet existing and uture time constraints

The quicker a rm can reanalyze and assess its risks, the quicker it can take actions

to mitigate those risks. Product commoditization has compelled dealers to set

aggressive growth targets or exotic and plain-vanilla derivatives trading volumes.

Increasing numbers o increasingly complex nancial instruments create valuation

challenges in achieving real-time or near-real-time intraday market risk analysis.

SAS meets real-time or near-real-time internal market risk requirements through

its: out-o-the-box, grid-based, distributed and parallel processing capabilities or

complex instruments; linear scaling to handle large trading volumes; and fexibility

or users to dene and value new instruments by calling internal or external pricing

unctions written in C or C++.

Accessing, integrating, cleaning, andmaintaining market and position data

SAS is uniquely positioned to address a number o market risk challenges

surrounding market and position data. For market data, SAS data integration tools

enable automation o many market data eeds as well as internal data eeds. For

position data, the SAS data model and data integration tools signicantly reduce the

eorts involved in conguring and maintaining a securities master.

Whether working with an existing securities master or one built in SAS, data

integration tools in SAS allow users to build metadata-driven visual process fows

that automate the process o accessing, cleaning and merging detailed position data

(see Figure 2).

3


6/12


The notion o metadata (inormation about data) in SAS is broad, comprehensive

andcompatiblewithindustrystandards.TheSASopenmetadatarepository(OMR),

builtontopoftheOMGsCommonWarehouseMetamodel,containsapproximately

165 metadata types and associations between these types much more than the

basic technical metadata about data relationships and denitions. This broader

denition o metadata means not only are physical descriptions o the tables/columns

(etc.) contained in the metadata repository, but additional inormation detailing

anapplicationsuseofdata,calledapplicationmetadata,isalsostoredinthe

metadata repository.

TheSASOMRleveragesthisbroaderdenitionofmetadatatocontractanend-to-

endmetadataobjectlineage,suchthatanyapplicationusedtocreateanalyticor

reporting output and the data that eeds that process (as well as the process that

created and manipulated the data used as input to those application processes),

can be tracked rom an end-to-end perspective thereby enabling transparency and

auditability on top o a secured environment.

Giventhatriskmanagementanalyticprocessesareinherentlycomplex,theSAS

OMRsbroadsupportofalltypesofmetadataallowsarbitrarilycomplexprocessesto

remain ully transparent, and allows users and managers to track the lineage o any

output or input into those processes ultimately reducing the operational risk aspect

that is associated with any complex risk analytic and reporting process.

Via metadata-driven data integration process fows, position data originating

rom internal source systems and third-party vendors (such as Bloomberg) can

be processed in SAS and subsequently stored in a single data target the SAS

data model.

As a centralized and reliable data store, the SAS data model can be used to

update an existing securities master, it can become a securities master itsel and

itcanprovideasingleversionofthetruthforsourcesystemdata;therefore,it

can be shared with other solutions and risk analyses beyond market risk, such

asforcounterpartycreditriskanalysesofOTCderivativesfromamarketrisk

trading portolio.

4


7/12


5

Figure 2. Metadata-driven visual process fows: point-and-click, drag-and-drop

interaces or automating data management.


8/12


DatastoredintheSASdatamodelincludesnotonlyinstrumentattributes,butalso

derived data that is calculated during the course o market risk analyses such as

implied volatilities that are backed out o option prices as well as credit spreads,

zerospreadsandOAS.Thisderiveddatacantheninturnbeusedinsubsequentriskactor projections in market risk or counterparty credit risk analyses, as well as or

FAS157andNAVpurposes.

Market-impliedassumptionsthatcouldbebackedoutofaninstrumentscurrent

mark-to-market price could be stored as part o the historical record or that

instrument in the data model. This automated ability to take derived data rom

one market risk analysis and then write it back to a data model or uture use in

subsequent market and counterparty credit risk analyses creates greater consistency

between these two dierent risk analyses an important consideration or enterprise

risk management and economic capital initiatives.

Meeting internal and external reporting requirements

SAS provides an inrastructure or automating the production and distribution o daily

valuation and risk reports. SAS provides Web portals and customizable dashboards

or users to access published reports. Users can run dynamic, parameter-driven risk

analyses remotely rom these and other interaces, such as Microsot Excel, and have

the results returned within the originating interace in real time.

Since SAS can retain all o the intermediate results generated in a market risk

simulation, including individual risk actor changes and instrument revaluations, re-

aggregation along any dimension can be perormed on the fy at the reporting level.Users can lter by instrument type, trader, desk, business unit, or individual or groups

ofriskfactorstogeneratenewprot/lossdistributionsandVaR.Greaterinsight

into risk at dierent aggregation levels means more inormed decisions regarding

corrective actions that might be needed or managing market risk.

External reporting requirements o regulators, rating agencies and investors as well

as those or internal purposes such as trading limits management, enterprise risk

management and economic capital initiatives all dier. The strategic implications o

risk analyses or internal risk budgeting and capital allocation implies a need or more

accurate risk calculations, and may require more rigorous valuation and risk actor

modeling approaches than those used or meeting external reporting requirements.

SAS provides a single platorm where multiple approaches can be simultaneously

implemented and reported upon all rom the same consistent data integration,

analytical and reporting platorm.

6


9/12


7

For trading operations subject to Market Risk Amendment regulatory requirements,

specic risk and incremental deault risk present challenges or market risk

measurement. In SAS, credit spreads can be modeled via transition matrices or as

unctions o macroeconomic risk actors, and credit models can be incorporated intosimulation analyses, including structural, reduced orm, and hybrid models o deault

and migration or long time horizons.

Deciding at which aggregation levelsto set and monitor VaR-based limits

Measuring market risk by creating realistic prot/loss distributions and deriving VaR

and expected shortall is important, but these measures alone do not specically

address the management o market risk. Aside rom regulatory capital requirements

and other external reporting needs, market risk is measured so that it can be

managed versus internal trading limits and or economic capital purposes.

VaR-based trading limits are one o the original uses o VaR, but it is not obvious at

what aggregation level they should be applied. Should limits be set and monitored at

the business unit, desk or trader level? The implications o where you set limits are

important in determining what actions should be taken to prevent limits rom being

exceeded.

In SAS, rms can simultaneously monitor market risk at the business unit, desk or

trader level, or at any user-dened level o aggregation or multilevel, limit-setting

schemes. An enterprise view o market risk across the entire rm will highlight

diversication benets across trader positions.

Determining optimal actions, hedges andintegration with enterprise risk management initiatives

When business units, desks or traders approach maximum VaR limits, it is not

immediately obvious which positions should be unwound (or what overlays and

hedges to put in place and at what level) without inadvertently exposing the portolio

to more risk. Forcing traders to prematurely unwind inventory positions intended or

client sales can be avoided by traders themselves adding to their hedges, or by risk

management overlaying hedges across desks or traders. What should those hedges

be, and at what level should they be applied? How do you allocate the costs o

overlay hedges across desks and traders? To answer these questions and nd the

optimal course o action, your risk system must oer the fexibility to quantitatively

explore the implications o dierent actions.


10/12

8


SAS oers numerous methods or exploring prot/loss distributions to identiy

hedging needs at dierent portolio aggregations: (1) sensitivity analysis via

second-order Taylor series approximations provides deltas and gammas o all

portolio aggregations, so you can look at approximate changes in prot/loss given

multiple, simultaneous changes in risk actors; (2) prot/loss curves or changes in

portolio value given changes in a single risk actor, based on ull revaluation o the

instruments in the portolio; (3) prot/loss suraces or changes in portolio value

given changes in a pair o risk actors, also based on ull revaluation (see Figure 3);

and(4)sandboxfunctionality,whereanalystscanrerunsimulationsofportfolios

that include hypothetical hedges in them.

Figure 3. Examples o simulation-based market risk output.

Beyond hedges or maintaining targeted VaR-based limits, rms may wish to lookmore closely at the implications o various strategies upon the entire P/L distribution,

and not just extreme values such as VaR. Instead o buying protection against just

extreme moves, it may make sense to also hedge against more probable, less

extreme (but still worrisome) market moves. By comparing and drilling down into

hypothetical prot/loss distributions in SAS, rms can uncover hidden concentrations

o exposure to particular market risk actors (as well as discover what types o

plausible market moves create unacceptable losses) and use that knowledge to

devise and implement better hedging strategies.

Intraday revaluations when signicant new positions and market changes occur,

pretrade limit testing and possible actions to accommodate new trades all present

special challenges that SAS can address in its market risk system.

Finally, SAS provides an ideal data management, analytical and reporting

environment or integrating its market risk analysis with longer-horizon enterprise risk

management and economic capital initiatives, where perormance o trading desks is

considered simultaneously with that o other divisions o the rm.

Sensitivityanalysis(viasecond-orderTaylorseriesexpansionofaportfolios

valuation unction).

P/Lcurves.

P/Lsurfaces.

VaR.

Expectedshortfall.

P/Ldistribution.

AllgranularintermediateresultsthatwentintothecreationofP/Ldistribution.

User-denedaggregationsforalloftheabove.


11/12


9

Summary

SAS provides fexibility, extensibility, scalability, accessibility and productivity gains in

estimating market risk:

Flexibilitytoimplementawidevarietyofapproachesforriskfactormodelingand

instrument valuation.

Extensibilitytodeneandvalueanytypeofassetatanylevelofcomplexity

internally, without having to rely on sotware updates or outside consultants.

Scalabilitytohandleextremelylargenumbersofportfoliopositions,including

complex instruments, via grid-based, parallel or distributed computing providing

desired market risk estimates within required time rames.

Accessibilitytorunriskanalyses,queryanddrilldowntohighlygranular

position-level results via user-preerred interaces, such as a Web browser andMicrosot Excel.

Productivitygainsforbothbusinessandquantitativeusers,whowillspendless

time preparing data and more time managing market risk.

As rms seek to gain a deeper understanding o the drivers that aect their

business and the interrelationships between them, they are increasingly turning to

more sophisticated quantitative modeling techniques. Market risk management is

no exception. SAS provides gold-standard econometric modeling capabilities or

quantitative users, as well as access to the results o model-driven analyses or

business inormation consumers.

Automating data integration using SAS lets you eliminate redundant, manual eorts

and better leverage the unique capabilities o your mathematically and statistically

abstract quantitative resources and your pragmatic, results-oriented business users.

In SAS, technical and quantitative resources can publish parameter-driven risk

analyses or business users. These dynamic analyses, which also include automated

data integration processes in SAS, can be run by business users rom the interaces

thattheyremostcomfortablewith(suchasMicrosoftExcel).Freeingbusiness

users rom abstract models and time-consuming data access and preparation

tasks means that risk analysts spend less time managing data, and more time

managing risk.

With SAS, the fexibility to implement many dierent modeling approaches or risk

actor evolution and revaluation means that rms can pick and choose the appropriate

methodology or a particular need. Whether or meeting regulatory, rating agency

or investor disclosure requirements, or the internal needs o managing the market

risk o trading operations and enterprise risk management, rms leverage SAS to

complement their existing inrastructure. Using SAS, rms can pick and choose rom

a wide variety o best-o-breed components that snap together better than those o

any other technology vendor. SAS provides an extensive analytical, data integration

and reporting environment that is being used by rms, in whole or part, to build highly

customized, fexible and extensible risk systems that will not only meet their current risk

measurement and management requirements, but their uture ones as well.


12/12

SAS Institute Inc. World Headquarters +1 919 677 8000 To contact your local SAS ofce, please visit:.sas.cm/fcs

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration.

Other brand and product names are trademarks of their respective companies. Copyright 2009, SAS Institute Inc. All rights reserved. 103887_530399.0309

Documents

SAS® - A Point of View on Market Risk VaR