ࡱ> FHEO bjbj>> .&TeTeMMMMMaaaa$a!<tL;;;;;;;$=K@x;M;MM;^MM;;N6$:`o7V&v80;;0!<8t@ @`$:@M$:h;;j!<@ :  MERGEFIELD "Course" MATH 542  MERGEFIELD "Title" Stochastic Processes  Course Description from Bulletin: This is an introductory course in stochastic processes. Its purpose is to introduce students into a range of stochastic processes, which are used as modeling tools in diverse fields of applications, especially in the business applications. The course introduces the most fundamental ideas in the area of modeling and analysis of real World phenomena in terms of stochastic processes. The course covers different classes of Markov processes (e.g. discrete and continuous-time Markov chains, Brownian motion and diffusion processes) as well as discrete and continuous-time martingales. It also presents some aspects of stochastic calculus with emphasis on the application to financial modeling and financial engineering. Credit may not be granted for MATH 481 and Math 542. (3-0-3) Enrollment: Elective for AM and other majors Textbook(s): Gregory F. Lawler, Introduction to Stochastic Processes, Chapman & Hall; Thomas Mikosch, Elementary Stochastic Calculus with Finance in View, World Scientific Other required material: None Prerequisites: MATH 332 or 333 or equivalent; MATH 475 Objectives: Students will learn about some useful classes of stochastic processes. Specifically, students will learn about Markov processes and martingales. Students will learn the essential tools required to analyze Markov processes in both discrete time and continuous time. Specifically, students will learn about transition probabilities and generators. Students will learn some elements of the asymptotic analysis of Markov processes. The main example of a continuous time Markov chain with discrete state space discussed in this class will be Poisson process. The main example of a continuous time Markov process with uncountable state space will be Brownian motion. Students will understand basic properties of martingales, supermartngales and submartingales. Students will understand relationship between Markov processes and martingales. Students will learn the basics of stochastic analysis. Specifically, students will learn about stochastic integration, Ito formula, stochastic differential equations and about Girsanov theorem. Students will work on projects that will provide a basis for some topics in the follow-up courses MATH 543, MATH 582 and MATH 586. MERGEFIELD "M_6"  MERGEFIELD "M_7"  MERGEFIELD "M_8"  Lecture schedule: Two 75 minute lectures per week Course Outline: Hours Discrete-time Markov chains 9 Motivation and construction First step analysis and Chapman-Kolmogorov equations Long-range behavior and invariant probability Classification of states Return times [first return times, mean return times] Discrete-time martingales 12 Filtrations and conditional expectations Definitions and examples Stopping times, Markov times, optional sampling theorem and optional stopping theorem Uniform integrability and UI martingales Martingale convergence theorem Doob-Meyer decomposition The quadratic variation process Continuous-time Markov chains 3 Poisson process Birth and Death process Brownian motion process 6 Definition and basic properties Markov property Functionals of Brownian motion Brownian motion with a drift and geometric Brownian motion Continuous-time Markov processes and martingales 6 Definition and examples Diffusion process Elements of stochastic analysis 9 Stochastic integration Ito formula and (Stochastic) Integration by parts formula Stochastic differential equations, diffusion processes, Ito processes Girsanov transformation Assessment: Quizzes/Tests 45-50% Graduate Project 0-10% Final Exam 45-50% Syllabus prepared by: Tomasz Bielecki and Fred Hickernell Date: 07/09/20  #$89NOPQrs 1 \ ] A B C v x  ƾʱwogc\c h=hdSh - h - h - 5h,h,5hwAhH5 hwAhwAh?h# j hwAhbhH/ h hbh,h,hH5hs~h hbmHnHujh-?UhRjhRUhHhH/ mHnHuhRhoOmHnHujhb*7UhWrpjhWrpU"PQ  [ \ z { R nW & F gdxgdHL]Lgdbgd - 0^`0gdS 0^`0gd,$a$gdb      H J P Q [ \ t u y { KL^`rt޽鹵骣jhsUhshxhWrp h 5\ hSh  hwAhwAhS hHhHhwAhh hbh'h'6 hb6hbhb6h' hbhbhHhShH5hSh 5 h - h - /R!:jz & Fgd\U8^8gd!X & Fgd' & Fgd!X & Fgd' & FgdI]gd & F gdsQS !"9:67ghyz89;YZikͽɽɽ뽶 h\Uh\Uh\Uh5ChMh1Qh!X h!Xh!Xh h 5\h$h:nh'h$h h h 5Hz:Zj3EmC gda $ a$gd gd  & Fgd' & Fgd' & Fgd\U & Fgd\U23DEdelm(*;<ACͽŹͽɹh(_%h# jhRhoh[hh5h2qhzdgh?h8hhh h}nch 5 hR5 h^h^h'h1Qh^h5C h\Uh\Uh\U5,1h/ =!"#$% w666666666vvvvvvvvv666666>6666666666666666666666666666666666666666666666666hH6666666666666666666666666666666666666666666666666666666666666666662 0@P`p2( 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p8XV~ 0@ 0@ 0@ 0@ 0@ 0@ 0@ 0@ 0@ 0@ 0@ 0@ 0@ 0@6666_HmH nH sH tH @`@ NormalCJ_HaJmH sH tH ::  Heading 1$@&5\DA D Default Paragraph FontZiZ  Table Normal :V 4 l4a _H(k (No List `$` Envelope Address!@ &+D/^@ ^J6>@6 Title$a$ 5CJ\PK![Content_Types].xmlN0EH-J@%ǎǢ|ș$زULTB l,3;rØJB+$G]7O٭VGRU1a$N% ʣꂣKЛjVkUDRKQj/dR*SxMPsʧJ5$4vq^WCʽ D{>̳`3REB=꽻Ut Qy@֐\.X7<:+& 0h @>nƭBVqu ѡ{5kP?O&Cנ Aw0kPo۵(h[5($=CVs]mY2zw`nKDC]j%KXK 'P@$I=Y%C%gx'$!V(ekڤք'Qt!x7xbJ7 o߼W_y|nʒ;Fido/_1z/L?>o_;9:33`=—S,FĔ觑@)R8elmEv|!ո/,Ә%qh|'1:`ij.̳u'k CZ^WcK0'E8S߱sˮdΙ`K}A"NșM1I/AeހQתGF@A~eh-QR9C 5 ~d"9 0exp<^!͸~J7䒜t L䈝c\)Ic8E&]Sf~@Aw?'r3Ȱ&2@7k}̬naWJ}N1XGVh`L%Z`=`VKb*X=z%"sI<&n| .qc:?7/N<Z*`]u-]e|aѸ¾|mH{m3CԚ .ÕnAr)[;-ݑ$$`:Ʊ>NVl%kv:Ns _OuCX=mO4m's߸d|0n;pt2e}:zOrgI( 'B='8\L`"Ǚ 4F+8JI$rՑVLvVxNN";fVYx-,JfV<+k>hP!aLfh:HHX WQXt,:JU{,Z BpB)sֻڙӇiE4(=U\.O. +x"aMB[F7x"ytѫиK-zz>F>75eo5C9Z%c7ܼ%6M2ˊ 9B" N "1(IzZ~>Yr]H+9pd\4n(Kg\V$=]B,lוDA=eX)Ly5ot e㈮bW3gp : j$/g*QjZTa!e9#i5*j5ö fE`514g{7vnO(^ ,j~V9;kvv"adV݊oTAn7jah+y^@ARhW.GMuO "/e5[s󿬅`Z'WfPt~f}kA'0z|>ܙ|Uw{@՘tAm'`4T֠2j ۣhvWwA9 ZNU+Awvhv36V`^PK! ѐ'theme/theme/_rels/themeManager.xml.relsM 0wooӺ&݈Э5 6?$Q ,.aic21h:qm@RN;d`o7gK(M&$R(.1r'JЊT8V"AȻHu}|$b{P8g/]QAsم(#L[PK-![Content_Types].xmlPK-!֧6 0_rels/.relsPK-!kytheme/theme/themeManager.xmlPK-!g theme/theme/theme1.xmlPK-! ѐ' theme/theme/_rels/themeManager.xml.relsPK] &  z #8NK ^ _ r s ;;; ; ; 8@0(  B S  ?      . 7 8<3333333QC  :  Y Z i j ** **| n}rR~<*YxD<ȕt|^^H %TZ=pD!J@{(p9+" Nj.:h9ısPXUPbN+U 9nk0j=^`.^`.88^8`.^`. ^`OJQJo( ^`OJQJo( 88^8`OJQJo( ^`OJQJo(hh^h`. hh^h`OJQJo(808^8`0o(.^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.h^`.h ^`hH.h pLp^p`LhH.h @ @ ^@ `hH.h ^`hH.h L^`LhH.h ^`hH.h ^`hH.h PLP^P`LhH.^`o(.^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.^`o(.dd^d`.4L4^4`L.  ^ `.  ^ `.L^`L.tt^t`.DD^D`.L^`L.88^8`o(. ^`hH.  L ^ `LhH.   ^ `hH. xx^x`hH. HLH^H`LhH. ^`hH. ^`hH. L^`LhH.h^`o(.h^`o(.h$ $ ^$ `o(. @ @ ^@ `hH. ^`hH. L^`LhH. ^`hH. ^`hH. PLP^P`LhH.^`o(.^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.^`o(.dd^d`.4L4^4`L.  ^ `.  ^ `.L^`L.tt^t`.DD^D`.L^`L.^`o(. ^`hH. pLp^p`LhH. @ @ ^@ `hH. ^`hH. L^`LhH. ^`hH. ^`hH. PLP^P`LhH.^`o(.h^`o(. pLp^p`LhH. @ @ ^@ `hH. ^`hH. L^`LhH. ^`hH. ^`hH. PLP^P`LhH.^`o(.^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.HNj.=nk!h9@{(9+sPXU+U~}|J                          $?v        `                          x                 TSR:W &Rc UEF# - H/ q"(_%?(*,`71s3b*7-?7?3AwAETM|QdSeVX!X'\c]I]s!]zdg# j:noWrph%rjsiwx{^bb[.M) `s,@j2q5CoO-1Q$as~g:k3 8=g'iBMS$ H?\U@  @UnknownG.[x Times New Roman5Symbol3. .[x ArialC.,.{$ Calibri Light7..{$ CalibriA$BCambria Math"qhLL:a a 4\3QP}nc2!xx4 SyllabusGladys I CollinsTomasz Bielecki`                Oh+'0 , L X d p| SyllabusGladys I Collins Normal.dotmTomasz Bielecki3Microsoft Office Word@F#@W Z@B7V@ޯf7Va  ՜.+,0 hp|  IIT  Syllabus Title  !"#$%&'()*+,-./012346789:;<>?@ABCDGRoot Entry F2o7VI1Table#AWordDocument.&SummaryInformation(5DocumentSummaryInformation8=CompObjr  F Microsoft Word 97-2003 Document MSWordDocWord.Document.89q