stochastic models lecture 1 markov chainsnchenweb/mfe5110/lecture/lecture_1.pdffinancial rationale:...
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STOCHASTIC MODELSLECTURE 1
MARKOV CHAINSNanChen
MScPrograminFinancialEngineeringTheChineseUniversityofHongKong
(ShenZhen)Sept.6,2016
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Outline1. Introduction2. Chapman-Kolmogrov
Equations3. TheGambler’sruinProblem
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1.1INTRODUCTION
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WhatisaStochasticProcess?
• Astochasticprocessisacollectionofrandomvariablesthatareindexedbytime.Usually,wedenoteitby– or–
• Examples:– DailyaveragetemperatureonCUHK-SZcampus– Real-timestockpriceofGoogle
(X1,X2,!,Xn,!)(Xt, t ≥ 0).
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MotivationofMarkovChains
• Stochasticprocessesarewidelyusedtocharacterizethetemporalrelationshipbetweenrandomvariables.
• Thesimplestmodelshouldbethatareindependentofeachother.
• But,theabovemodelmaynotbeabletoprovideareasonableapproximationtofinancialmarkets.
Xn
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WhatisaMarkovChain?
• Letbeastochasticprocessthattakesonafinite/countablenumberofpossiblestates.WecallitbyaMarkovchain,iftheconditionaldistributionofdependsonthepastobservationsonlythrough.Namely,
forall
(Xn,n = 0,1, 2,!)
Xn+1
(X1,X2,!,Xn )Xn
P(Xn+1 = j | X1 = i1,X2 = i2,!,Xn = in ) = P(Xn+1 = j | Xn = in )
n.
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TheMarkovian Property
• ItcanbeshownthatthedefinitionofMarkovchainsisequivalenttostatingthat
• Inwords,giventhecurrentstateoftheprocess,itsfutureandhistoricalmovementsareindependent.
P(Xn+1 = j,X1 = i1,X2 = i2,!,Xn−1 = in−1 | Xn = in )= P(Xn+1 = j | Xn = in )P(X1 = i1,X2 = i2,!,Xn−1 = in−1 | Xn = in )
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FinancialRationale:EfficientMarketHypothesis• TheMarkovian propertyturnsouttobehighlyrelevanttofinancialmodelinginlightofoneofthemostprofoundtheoryinthehistoryofmodernfinance --- efficientmarkethypothesis.
• Itstates–Marketinformation,suchastheinformationreflectedinthepastrecordortheinformationpublishedinfinancialpress,mustbeabsorbedandreflectedquicklyinthestockprice.
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MoreaboutEMH:aThoughtExperiment• Letusstartwiththefollowingthoughtexperiment:AssumethatProf.ChenhadinventedanformulawhichwecouldusetopredictthemovementsofGooglestockpriceveryaccurately.Whatwouldhappenifthisformulawasdisclosedtothepublic?
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MoreaboutEMH:aThoughtExperiment• SupposethatitpredictedthatGoogle’sstockpricewouldrisedramaticallyinthreedaystoUS$700fromUS$650.– Thepredictionwouldinduceagreatwaveofimmediatebuyorders.
– HugedemandsonGoogle’sstockswouldpushitspricetojumpto$700immediately.
– Theformulafails!• AtruestoryofEdwardThorpandtheBlack-Scholesformula
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ImplicationofEfficientMarketHypothesis• OneimplicationofEMHisthatgiventhecurrentstockprice,knowingitshistorywillhelpverylittleinpredictingitsfuture.
• Therefore,weshoulduseMarkovprocessestomodelthedynamicoffinancialvariables.
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TransitionMatrix
• Inthislecture,weonlyconsidertime-homogenousMarkovchains;thatis,thetransitionprobabilitiesareindependentoftime
• DenoteWethencanusethefollowingmatrixtocharacterizetheprocess.
P(Xn+1 = j | Xn = in )n.
pij := P(Xn+1 = j | Xn = i).
P :=
p11 p12 ! p1np21 p22 ! p2n! ! ! !pn1 pn2 ! pnn
!
"
#####
$
%
&&&&&
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TransitionMatrix
• ThetransitionmatrixofaMarkovchainmustbeastochasticmatrix:––
pij ≥ 0.
pijj=1
n
∑ =1.
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ExampleI:ForecastingtheWeather
• SupposethatthechanceofraintomorrowinShenzhendependsonpreviousweatherconditionsonlythroughwhetherornotitisrainingtoday.
• Assumethatifitrainstoday,thenitwillraintomorrowwithprobability70%;andifitdoesnotraintoday,thenitwillraintomorrowwithprob.50%.
• HowcanweuseaMarkovchaintomodelit?
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ExampleII:1-dimensionalRandomWalk• AMarkovchainwhosestatespaceisgivenbytheintegersissaidtobearandomwalkif,forsomenumber
• Wesaytherandomwalkissymmetricif;asymmetricif
0 < p <1,0,±1,±2,...
Pi,i+1 = p =1−Pi,i−1, i = 0,±1,±2,...
p =1/ 2 p ≠1/ 2.
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1.2CHAPMAN-KOLMOGOROV EQUATIONS
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TheChapman-KolmogorovEquations
• TheCKequationsprovideamethodforcomputingthe-steptransitionprobabilitiesofaMarkovchain.
•
orinamatrixform,
n
Pijn+m = P Xn+m = j | X0 = i( ) = Pik
nPkjm
k∑
Pn+m = PnPm.
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ExampleIII:RainProbability
• ReconsiderthesituationinExampleI.Giventhatitisrainingtoday,whatistheprobabilitythatitwillrainfourdaysfromtoday?
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ExampleIV:UrnandBalls
• Anurnalwayscontains2balls.Ballcolorsareredandblue.Ateachstageaballisrandomlychosenandthenreplacedbyanewball,whichwithprob.80%isthesamecolor,andwithprob.20%istheoppositecolor.
• Ifinitiallybothballsarered,findtheprobabilitythatthefifthballselectedisred.
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1.3THE GAMBLER’S RUINPROBLEM
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TheGambler’sRuinProblem
• Consideragamblerwhoateachplayofthegamehasprobabilityofwinningonedollarandprobabilityoflosingonedollar.Assuming thatsuccessiveplaysofthegameareindependent,whatistheprobabilitythat,startingwithdollars,thegamblerfortunewillwindollarsbeforeheruins(i.e.,hisfortunereaches0)?
p
q =1− p
iN
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MarkovDescriptionoftheModel
• Ifweletdenotetheplayer’sfortuneattime,thentheprocessisaMarkovchainwithtransitionprobabilities–– ,
• TheMarkovchainhasthreeclasses:
{Xn,n =1,2,...}
Xn
n
P00 = PNN =1Pi,i+1 = p =1−Pi,i−1 i =1,2,...,N −1.
{0},{1, 2,...,N −1},{N}
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Solution
• Letbetheprobabilitythat,startingwithdollars,thegamblerfortunewilleventuallyreach.
• Byconditioningontheoutcomeoftheinitialplay
,and
Pi = pPi+1 + qPi−1
Pi i
N
i =1,2,...,N −1.
P0 = 0, PN =1.
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Solution(Continued)
• Hence,weobtainfromtheprecedingslidethat––
– ……–
P2 −P1 =qp(P1 −P0 ) =
qpP1;
P3 −P2 =qp(P2 −P1) =
qp
"
#$
%
&'
2
P1;
PN −PN−1 =qp(PN−1 −PN−2 ) =
qp
"
#$
%
&'
N−1
P1.
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Solution(Continued)
• Addingalltheequalitiesup,weobtain–
–
P1 =1/ N,
1− (q / p)1− (q / p)N
,
"
#$
%$
p =1/ 2;
p ≠1/ 2.
Pi =i / N,
1− (q / p)i
1− (q / p)N,
"
#$
%$
p =1/ 2;
p ≠1/ 2.
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Solution(Continued)
• Notethat,as
• Thus,if,thereisapositiveprobabilitythatthegambler’sfortunewillincreaseindefinitely;whileif,thegamblerwill,withprobability1,goruinagainstaninfinitelyrichadversary(say,acasino).
N→+∞,
Pi →0,
1− qp
#
$%
&
'(
i
,
)
*++
,++
p ≤1/ 2;
p >1/ 2.
p >1/ 2p ≤1/ 2
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HomeworkAssignments
• ReadRossChapter4.1,4.2,and4.5.1.• AnswerQuestions:– Exercises5,6(Page261,Ross)– Exercises13,14(Page262,Ross)– Exercises56,57(Page270,Ross)
• DueonSept.13,Wed.