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Computational Finance and Financial Engineering
Second R/Rmetrics User and Developer WorkshopJ une 28thJ uly 2nd
2009, Meielisalp, Lake Thune, Switzerland
Workshop Booklet
Welcome
Abstracts
www.rmetrics.org
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List of Sponsors
www.ethz.ch
www.finance.ch
www.revolution-computing.com
Marcel Fehr
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Welcome
Welcome to the Third Meielisalp Workshop on "Computational Finance andFinancial Engineering" together with the Third R/Rmetrics User andDeveloper Workshop. We are very glad that you found the time to come tothe Meielisalp, and for the many of you traveling from Chicago, Bombay,Auckland and various places in Europe, hope that your journey was not tooarduous.
With the Meielisalp Workshop we have no desire to add another workshopto the existing zoo of meetings and conferences. We want to find a new
forum where not only the "older and experienced" speakers present theirresearch and work. Instead we wish to give "younger" people, the students,the chance to attend, to give a presentation and to engage in discussionswith participants from academia and industry. The environment for thisworkshop should be a place a little bit aside from the mainstreamconference of venues, and we are happy to have found this beautiful placehere at the Meielisalp.
About 50 participants are attending the workshop, and the mixture, asplanned, is quite heterogeneous. One third are from academia, and anotherthird from the software and financial industries, including banks. Importantly,
one third of the participants are students, and some of them are presentingtalks about their Masters or PhD thesis work.
Last, but not least, we have to thank our sponsors who made this eventpossible: ETH Zurich, Finance Online Zurich, Revolution Computing NewHaven, and Marcel Fehr. Thanks also to Yohan Chalabi and Andrew Ellis forhelping us in the local organization.
We wish you an interesting workshop with many inspiring and stimulatingdiscussions.
Diethelm Wrtz and David Scott
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Commoditymod
ellinginR
PhilippErb
D
avidLuthi
May29,20
09
Abstract
Stochasticfactormodelswereintrod
ucedinGibsonandSchwartz
(1990)andSchwartz(1997)todescribe
commodity-linkedsecurities.
Wepresentthetwo-factormodelanditsimplementationintheR-
packageschwartz97.Asopposedtoacom
modityterm-structuremodel,
theSchwartz97two-factormodelexplicitlygivesthespotpricedynam-
icstogetherwiththedynamicsoftheconvenienceyield.Ageometric
Brownianmotionisimposedforthespot
priceprocessandanOrnstein-
Uhlenbeckprocessfortheconveniencey
ield,whichentersthedriftof
thespotpriceprocess.
Eventhoughthetwo-factormodelhasthedrawbackoflittleflexi-
bilityconcerningtheforwardcurve,we
believethatitisareasonable
trade-offbetweenstatisticaltractability
andsophistication.
Sincethetwo-factormodeladmitsa
naffinerepresentationitcan
becastinstatespaceform.Inthistalkwewilldescribethefitting
procedurebasedonfuturesdataandth
eKalmanfilteraswellasthe
pricingoffuturesandEuropeanoptionsonfuturesaccordingtoMil-
tersenandSchwartz(1998).Finally,we
presentanempiricalstudyof
themodelsperformance.
1
MultivariateCopulaatwork
MatthiasFischer
D
epartmentofStatisticsandEconometrics
U
niversityofErlangen-Nrnberg,Germany
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OptimizationMeth
odsinFinance
P.
Henaff
BankofAmeric
a,
London
and
ENST-Bretagne,Brest
1June2009
Abstract
QuantitativeFinanceisarichapplicationdomainforoptimization
methods.Oneimmediatelythinksaboutportfoliooptimizationandthe
entireindustrythathasdevelopedfromt
heworkpioneeredbyH.Markowitz.
Inthistalk,
however,
Ifocusonperhapslesserknown,
butnevertheless
powerfulapplicationsofoptimizationmethodstoquantitativefinance.
Theseareasmostlyrelatetothepricingandhedgingofcomplexinstru-
ments.
Illfocusonthreeareas:
thevaluationofrealoptions,specificallythevaluationofindustrial
assetssuchasaNAturalGasstoragefacility.
theuseofstochasticoptimization
forpricingandhedgingfinancial
derivatives,and
theproblemofvaluationilliquidas
sets,usinghistoricalinformation.
Foreachtopic,weattempttoframe
theproblemandshowthecontri-
butionthatoptimizationmethodshave
provided.
Thetalkweavesprac-
ticalexamplesandtheory.
Foreachtop
ic,wepresentreal-lifenumerical
illustrations.
1
Risk-NeutralModelingofEmissionAllowancePrice
s
and
OptionValuation
JuriHinz
NationalUniversityofSingapore
DepartmentofMathematics,
FacultyofScience
The
existence
ofmandatory
emission
trading
schemes
in
Europeand
theUS,andtheincreasedliquidityoftradingon
futurescontr
actsonCO_
2emissionsallowances,lednatu
rally
tothenextstepinthedevelopmentofthesemarkets:these
futurescontractsarenow
usedasunderliersforavib
rant
derivativema
rket.Inthispaper,wegivearigorousanalysis
ofa
simplerisk-n
eutralreduced-form
modelforallowance
futures
prices,demo
nstrateitscalibrationtohistoricaldata,ands
how
howtoprice
Europeancalloptionswrittenonfuturescontracts.
Wealsoelab
orateonanR-implementationofouroptionpricing
technique.
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Introducingforeach
anditerators
BryanW.Le
wis
REvolutionCom
puting
NewHave
n
Thenewforeachanditeratorspackagesintro
ducelistcomprehensionsand
anabstractiteratorclasstotheRlanguage.T
hepackagesalsoprovidean
abstractinterfacetoparallel/distributedprogrammingwithRindependently
ofaspecificparallelimplementation.Wediscussthenewpackagesandtheir
applicationtocommonperformancebottlenecksinreal-worldproblems,
includingparameteroptimizationandothere
xamples.
RecentFinancialCrisis
Alook,perspective&lessons
MahendraMehta
NeuralTechSoft,Mumbai,India
AbstractIntherecentp
ast,theworldhasseenunprecedentedfinancialcr
isisandanear
collapseofworldsmajorfinancialgiants.Manylargeinstitutionshavebeenspendingtimein
IntensiveCareUnit(ICU)
andcouldonlysurvivewiththehelpofoxygen(whichcameinthe
formofmassivebailoutp
ackagefromtherespectiveGovernments).Afew
largeesteemed
investmentbankscouldnotevensurviveinthewakeoffinancialTsunami.
Someofthese
institutionshavebeenope
rationalformanydecadesandhavehadastonishingtrackrecord
andenviousprofitabilityinpast.
Thetalkanalyzessomeo
ftheimportantreasonsoftherecentfinancialcrisisandputsin
perspectivesomeimportantissueswhichwouldconfrontfinancialindustry,currentstructure
ofthefinancialmarkets,
regulation,professionalsandothersassociatedwiththefinancial
industry&services.
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ProblemsandSolutions
forPortfolioRisk
BrianPetersonandPeterCarl
Braverock,Ch
icago
Manycommonriskmeasuresaremostappro
priateforuseonsingleinstruments
orGaussiannormaldistributions.
Thiswork
shop-stylepresentationwillreview
thehistoryofriskmeasuresinfinance,followedbytheevolutionofmethodsfor
determiningriskoninstrumentswithnon-normaldistributions,extendthese
ideastoexaminingportfolioriskmetrics,an
dlookatriskbudgetingforportfolio
optimization.
Portfolioriskmetricsshouldideallybeabletodecomposetheunivariateriskof
aportfolioreturntothecontributionofthatr
iskofeachindividualelementof
theportfolio.
Ideallysuchameasurewouldbeacoherentriskmeasureper
Artzner,wouldbeagoodoutofsamplepredictorofrisk,andwouldbeusefulex
anteinportfoliooptimizationorriskbudgeti
ng.
Wewillexamineportfolio
extensionstoVaRandES/CVaRthathaveth
esepropertiesaswellasrobustness
todataspikesandnon-normality.Itwillalso
reviewpossibleusesofsuch
measuresinportfoliooptimizationandriskb
udgetconstraints.
BrianPetersonandPeterCarl(notatMeielis
alp)aretheauthorsoftheR
packagePerformanceAnalytics.Theyhavev
ariouslyworkedatinvestment
banks,hedgefunds,andproprietarytradingfirms.Theyhaverecentlypublished
(withKrisBoudt,K.U.
Leuven,notatMeielisalp)academicpapersinRISK,t
he
JournalofRisk,ComputationalFinance,and
otherlocations.
PackageDevelopmentinR:
Implem
entingGO-GARCHModels
Dr.BernhardPfaff
GlobalQuantitativeEquity
InvescoAssetManagementDeutschlandGmbH
Inthistutorial
thepackagedevelopmentanddesignof
GO-GARCHm
odelsinRisdemonstrated.Hereby,the
neccessarysteps
involved
from
academic
paperto
designandwo
rkingRcodearebespoken.Thetouched
topicsinclude
ITrequirements,designofclassedand
methods
as
wellas
some
guidances
to
make
the
packageavaila
bletoothers.
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MarkowitzandTwoManagers
KlausRheinb
erger
ResearchCenterProcessand
ProductEngineering
FachhochschuleVorar
lberg,
Austria
WeconsiderthestandardMarkowitzportfoliooptimizationproblem
[1]inthesitu-
ationthattwomanagersoptimizeportfoliosundertherestrictionthattheymayonly
investinsubsetsofthemarket.Forthispurpose,thesetofriskyassetsisseparated
intotwodisjointsubsets.Incasethatarisklessassetisconsidered,itisincluded
inbothsubsets.Thetwomanagersoptimalp
ortfoliosarecombinedaffinelytoone
ormoreso-calledmasterportfolios.Incontrast,theoptimalportfolioofallassets
(optimizedbyonemanager)iscalledtheglobalportfolio.
Ourmainresultsare:
1.Incaseofmaximizationofquadraticutility,themasterportfoliosmaynotreach
theglobalportfolioeveninanuncorrelatedassetseparation,i.e.,whentheco-
variancematrixisblockdiagonalwithrespecttotheseparation.
2.Incaseofuncorrelatedandonlyriskyassetsubsets,everyglobalportfoliocanbe
attainedbyappropriateoptimizationint
heassetsubsetsofthetwomanagers
(usingforexampleexpectedutilitymaximizationorvarianceminimizationat
fixedreturn)andbyappropriatecombinationoftheiroptimalportfoliostoa
masterportfolio.
3.Incaseofuncorrelatedriskyassetsubsetsextendedwitharisklessasset,every
global1-fundcanbeattainedbytwomanagersbyappropriatecombinationof
their1-funds.
Conditionswhentheconversesof2and3aretrue,willbegiveninthepresentation.
Thecomputationsandfiguresofexamplesareg
eneratedusingR[2].
References
[1]DavidG.Luenberger,InvestmentScience,O
xfordUniversityPress,1997
[2]http://www.r-project.org/
1
RealTimeTradinginR
An
exampleusingtheIBrokerspackage
JeffreyA.
Ryan
Chicago,
IllinoisUSA
PresentedatR/Rmetrics2009
June2
8July2Meielisalp,
LakeThune,Switzerland
Whilemanyalgorithmictradingsystemsmakeuseofmultipleprogramm
ing
languages,amoreidealsolutioninvolvesusingasinglelanguagetomanage
the
entireprocess.T
hissinglelanguagemustbeabletohandleoneormoreincom
ing
datafeeds,make
tradedecisionsbasedonthisreal-timedata,andcontinuously
processordersandorder-relatedinformation.
Theadvantagesanddisadvantagestoasingle-languagesolutioninRwillbe
explored,aswellasanoverviewofthetechnicalchallengesanddesignconsider-
ationswithrespecttoasynchronousconnections,single-threadedprocesses,
and
interprettedlang
uages.
Finally,aco
mpletetradingexampleusingtheIBrokers
packagewillbe
covered.Frome
stablishingconnections,torequestinglivedataandexecuting
ordersfromwith
inR,theflexibilityofthelanguagewillbeexploredtohighlight
thepowerofmak
ingRthesingle-languagetoolofchoiceforalgorithmictrad
ing.
1
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