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Stochastic Calculus via Regularizations PDF

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Bocconi & Springer Series 11 Mathematics, Statistics, Finance and Economics Francesco Russo Pierre Vallois Stochastic Calculus via Regularizations Bocconi & Springer Series Mathematics, Statistics, Finance and Economics Volume 11 Editors-in-Chief Lorenzo Peccati, Dipartimento di Scienze delle Decisioni, Università Bocconi, Milan,Italy SandroSalsa,DepartmentofMathematics,PolitecnicodiMilano,Milan,Italy SeriesEditors Beatrice Acciaio, Department of Mathematics, Eidgenössische Technische HochschuleZürich,Zürich,Switzerland CarloA.Favero,DipartimentodiFinanza,UniversitàBocconi,Milan,Italy Eckhard Platen, Department of Mathematical Sciences, University of Technology Sydney,Sydney,NSW,Australia WolfgangJ.Runggaldier,DipartimentodiMatematicaPuraeApplicata,Università degliStudidiPadova,Padova,Italy Erik Schlögl, School of Mathematical and Physical Sciences, University of TechnologySydney,CityCampus,NSW,Australia TheBocconi&SpringerSeriesaimstopublishresearchmonographsandadvanced textbookscoveringa widevarietyoftopicsin thefieldsofmathematics,statistics, finance,economicsandfinancialeconomics.Concerningtextbooks,thefocusisto provideaneducationalcoreatatypicalMaster’sdegreelevel,publishingbooksand alsoofferingextramaterialthatcanbeusedbyteachers,studentsandresearchers. TheseriesisbornincooperationwithBocconiUniversityPress,thepublishing house of the famous academy,the first Italian universityto granta degreein eco- nomics, and which today enjoys internationalrecognitionin business, economics, andlaw. The series is managed by an international scientific Editorial Board. Each memberoftheBoardisatoplevelresearcherinhisfield,well-knownatalocaland globalscale. Some of the Board Editorsare also Springerauthorsand/orBocconi high level representatives. They all have in common a unique passion for higher, specificeducation,andforbooks. VolumesoftheseriesareindexedinWebofScience-ThomsonReuters. ManuscriptsshouldbesubmittedelectronicallytoSpringer’smathematicsedito- rialdepartment:[email protected] THESERIESISINDEXEDINSCOPUS Francesco Russo (cid:129) Pierre Vallois Stochastic Calculus via Regularizations FrancescoRusso PierreVallois ENSTAParis FacultédesSciencesetTechnologies InstitutPolytechniquedeParis InsitutÉlieCartan,UniversitédeLorraine UnitédeMathématiquesAppliquées Vandœuvre-lès-Nancy,France Palaiseau,France ISSN2039-1471 ISSN2039-148X (electronic) Bocconi&SpringerSeries ISBN978-3-031-09445-3 ISBN978-3-031-09446-0 (eBook) https://doi.org/10.1007/978-3-031-09446-0 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerland AG2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewhole orpart ofthematerial isconcerned, specifically therights oftranslation, reprinting, reuse ofillustrations, recitation, broadcasting, reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland To Cristinaand totheDarkLadies Maraand Michela.To thememoryofmyparents(FR) To Marianne,Laure, Isabelle,andNicolas andtothememoryofmyparents (PV) Preface In physics, classical analysis plays a central role. For instance in Newtonian mechanics, thermodynamics,and electricity, many phenomenaare well explained by deterministic models involving either ordinary differential equations or partial differentialequations. Inthedeterministicworld,theoretically,thefutureevolutionofasystemdepends on initial conditions and on some parameters driving its own dynamics; never- theless, in practice, their values are often measured with uncertainty. Moreover, the theory of chaos explains that small variations in the initial data can generate big fluctuations later on. In addition, deterministic models used for representing complex phenomena currently require a huge number of equations which are either difficultor impossible to solve within a reasonabletime. In factcomplexity and uncertainty can be integrated directly via a probabilistic model. Mention can be made of statistical mechanics, networks (Internet, gene interaction), financial markets,andpercolation.Inprobabilitytheory,arandomquantitywhichfluctuates as a function depending on time is modeled by a stochastic process. On a given probability space ((cid:2),F,P), a process X = (X (ω),t ∈ [0,T],ω ∈ (cid:2)) is t a “measurable” collection of random variables which are defined on the same underlying space and take their values in some space E. A process depends on time t and on the random realization ω. Fixing ω, t (cid:3)→ X (ω) is a real function, t oftencalled pathor trajectory of the process.In the sequel,the processX will be denotedeither(X )or(X(t)),omittingthevariableω.Forthesakeofsimplicity,in t thisintroduction,weessentiallyonlydealwithE =R. In deterministicanalysis,differentiationandintegrationplayan importantrole. Consider two functions f,x : (cid:2)R →(cid:3) R which are a(cid:2)ssum(cid:3)ed to be differentiable. Thus, the derivative of t (cid:3)→ f x(t) at time t is f(cid:5) x(t) x(cid:5)(t). This property is equivalentto (cid:4) (cid:4) (cid:2) (cid:3) (cid:2) (cid:3) t (cid:2) (cid:3) t (cid:2) (cid:3) f x(t) =f x(0) + f(cid:5) x(s) x(cid:5)(s)ds = f(cid:5) x(s) dx(s),t ∈[0,T], (1) 0 0 wherethelatterintegralisintendedinthesenseofLebesgue-Stieltjes. vii viii Preface (cid:2) (cid:3) Now, let (X(t),t ∈ [0,T] be a real valued stochastic process. Fixing ω, the path t (cid:3)→ X(t,ω) is generally continuous but is neither differentiable nor with boundedvariation as it happensin particular when X is a Brow(cid:2)nian(cid:3)motion. Therefore, even if f is C1, (1) cannot be applied since t (cid:3)→ f X(t) is not differentiable. An important field in probability theory is the so called stochastic calculuswhichcombinesprobabilityandcalculus,andallowsinparticular (cid:4) t (cid:2) (cid:3) (cid:5) 1. givingasensetointegrals(intime)ofthetype f X(s) dX(s), 0 2. developing a us(cid:2)eful (cid:3)and efficient (stochastic) calculus, i.e., a formula for differentiatingf X(t) as(1), foralargeclassofnon-differentiableproc(cid:2)essesXandfu(cid:3)nctionsf. Infact,foragivenintegratorprocess X ,t ∈[0,T] ,moregenerally,onegoal t (cid:4) T of stochastic integration is to define the stochastic integral Y(s)dX(s) for a (cid:2) (cid:3) 0 large class of integrands Y ,t ∈ [0,T] also defined on the same probability t space. Obviously, if for almost all ω, the path t (cid:3)→ X (ω) were differentiable (cid:4) t T and |Y (ω)X˙ (ω)|dt < ∞, the above-mentioned stochastic integral could be t t 0 defined as the Lebesgue integral in time for almost all ω. As mentioned earlier, mostofthesignificantprocessesinterveninginclassicalstochasticmodelsarenot (cid:4) T differentiable,sothatthepreviousschemedoesnotpermitdefining Y(s)dX(s). 0 ClassicalstochasticintegrationisanicecombinationofLebesgueintegrationand martingaletheory.Themostfamous(stochastic)integralistheItôintegralwhichis definedforinstancewhentheintegratoristheBrownianmotionX =W.However, modern (Itô’s) stochastic integration can be developed in a more general setting, whichreferstothecasewhenX isasemimartingalewithrespecttoanunderlying filtration(Ft)t≥0,i.e.,thesumofa(local)(Ft)-martingaleMandan(Ft)-“adapted” (i.e.,non-anticipative)boundedvariationprocessV.Here,weessentiallydealwith continuous semimartingales, i.e., when M and V are continuous processes. If Y and X are both semimartingales, an(cid:5)other celebrated integral is the so called Fisk- Stratonovich integral, denoted by tY ◦ dX which coincides with the sum of (cid:5) 0 Itô’sintegral tYdXandhalfthe(oblique)bracket(cid:9)Y,X(cid:10)betweenthemartingale 0 components of Y and X. When X = Y, one also denotes (cid:9)X(cid:10) := (cid:9)Y,X(cid:10). Itô’s (and Fisk-Stratonovich’s) integral only allows non-anticipating integrands Y with respecttothesemimartingaleintegratorX.Aprocess(Y )issaidtobeadaptedif, t for any t, the random variable Y is F -measurable. In many situations (Y ) must t t t be progressivelymeasurable,which is a more stringentnotion,see Definition 2.9. Timeevolutionplaysacrucialroleindynamicalphysicalsystemsandmostofthe interesting (random)quantities Y are adapted to the informationcarried by X. At timet,thisinformationisconcentratedintheσ-fieldF ,whichisgeneratedbythe t collectionofrandomvariables(X ,0≤u≤t).Instochasticmodelsforfinance,at u eachtimet, theinvestorknowstheassets(stocks,bonds,interestrates)price(X ) t Preface ix inagivenmarket.Thus,theportfoliocompositionY ofth(cid:2)ein(cid:3)vestorattimet,must be non-a(cid:2)ntic(cid:3)ipatingfor the increasingfamily of σ-fields Ft . If X is a Brownian motion, F iscalledaBrownianfiltration. t Givena progressivelymeasu(cid:4)rableintegrandprocess(Yt), andas(cid:4)emimartingale t t X = M +V, the Itô integral Y dX is defined as the sum of Y dM and s s s s (cid:4) 0 0 t Y dV . The formerone makesmajor use of the martingalepropertyof M and s s 0 thesecondintegralisdefinedforanyωastheclassicalLebesgueintegralunderthe (cid:4) T assumption |Y |d(cid:12)V(cid:12) < ∞,where(cid:12)V(cid:12)standsforthetotalvariationprocess s s 0 ofV.Inordertogiveasensetothestochasticintegralwithrespecttothemartingale M, let us start with a bounded and piecewise constant process of the type Y = t N(cid:6)−1 Y−110(t)+ Yi1]ti,ti+1](t),t ∈ [0,T], where Yi is Fti-measurable, Y−1 is F0- i=0 measurableand0 = t < ··· < t < ··· < t = T is a subdivisionof[0,T]. It 0 i N seemsreasonabletoset (cid:4) t YsdMs :=Y−1M0+Y0(Mt1 −Mt0)+···+Yi(Mt −Mti), (2) 0 foranyt in[ti,ti+1].We denotebyE theclassofsuchelementaryprocessesY.It can be proved, see(cid:7)S(cid:5)ect. 5.1, that the ma(cid:8)p I, which to any Y ∈ E associates the stochastic integral tY dM ,t ∈[0,T] is linear, takes its values in the set of 0 s s continuouslocalmartingales(seeDefinition2.21),wherethatspaceofcontinuous stochastic processes is equipped with the topologyof the uniformconvergencein probability. Moreover, the map I can be prolonged by continuity to the space of (cid:4) T progressively measurable processes Y such that Y2d < M > < ∞ almost s s 0 surely.IntheparticularcaseofBrownianmotionM =W,wehave<W > =t for t anyt ≥0. The class of progressively measurable processes (resp. semimartingale(cid:4)s) is the · rightsettingforintegrands(resp.integrators).IndeedthemapY ∈ E (cid:3)→ YdX 0 is continuous if and only if X is a semimartingale, according to the celebrated Bichteler–Dellacherie theorem, see Section III.7 in [273], or Section IV-2.16 in [276]orSectionVIII.4in[78]. Asalreadypointedout,classicalobjectsofrealanalysisareordinarydifferential equations, which can be expressed in the differential form or equivalently in the integral form. Their stochastic counterpartare the so called stochastic differential equations (SDEs). They appear in an integral formulation, since there is no naturalpath-wise differentiationforstochastic integratorslike semimartingales,in particularBrownianmotion. x Preface ThemostcelebratedSDEsarethosedrivenbyaclassicalBrownianmotionW = (W ),whosecoefficientsa,b : [0,T]×R → RareBorelfunctions,andaninitial t valueξ whichisarandomvariableindependentof(W ),i.e., t (cid:4) (cid:4) t t X =ξ + a(s,X )dW + b(s,X )ds, t ∈[0,T], (3) t s s s 0 0 (cid:4) t where a(s,X )dW isanItôintegral,whichisa(local)martingale.Inparticular s s 0 a(cid:4)solution is a semimartingale whose value at 0 is ξ, its local(cid:4)martingale part is t t a(s,X )dW and its bounded variation componentequals b(s,X )ds. For s s s 0 0 them,itisoftenconvenienttoadopttheimproperdifferentialformulation (cid:9) dX = a(t,X )dW +b(t,X )dt t t t t (4) X = ξ. 0 ThesolutionsofSDEsof thetype(4), arecalled diffusionprocessesordiffusions. Thefamilyofdiffusionsprocessesisanimportantsubclassoftheoneofsemimartin- gales. In physics, the motion of a microscopic particle in a medium with velocity b(t,x)attimet andpositionx,subjectedtonoiseperturbation,withintensitya,is oftendescribedbyadiffusion.InChaps.12and13wedeveloptheclassicaltheory of SDEs and mainly study existence and uniqueness. The SDE (4) admits multi- dimensionalandinfinitedimensionalformulations,see[73]. Notethatifthediffusioncoefficientavanishesthen(4)isanordinarydifferential equation, for which the differential calculus is the main device. In order to study stochastic differential equations, one needs the aforementioned Itô’s stochastic calculus,supportingItôstochasticintegration.Thecentralfeatureofthatpowerful calculusisthesocalledItô’sformula,whichisinfactachangeofvariableformula. Thatinstrumentconstitutesthestochasticextensionofthefundamentaltheoremof integralcalculusorchainrulepropertystatedin(1).Letusassumethatf :R→R isofclassC2 andXisasemimartingale.Then(1)canbegeneralizedinto (cid:4) (cid:4) t 1 t f(X )=f(X )+ f(cid:5)(X )dX + f(cid:5)(cid:5)(X )d <X> , 0≤t ≤T. (5) t 0 s s s s 2 0 0 IfXisadifferentiableprocess,then<X >=0sinceitsmartingalepartisconstant. IntheStratonovichformulation(5)canbealsowrittenas (cid:4) t 1 f(X )=f(X )+ f(cid:5)(X )dX + (cid:9)f(cid:5)(X),X(cid:10) , 0≤t ≤T. (6) t 0 s s t 2 0

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