r语言股票下载 ar模型 描述统计 附代码数据Word下载.docx
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r语言股票下载 ar模型 描述统计 附代码数据Word下载.docx
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#MicrosoftsymbolinGooglefinanceisMSFT
#d.DownloadMicrosoftstockpricedatafromJan3,2010toJan20,2016inRusinggetSymbolscommandin
#Quantmod,checkifdataisreadproperlyusingheadandtailcommand5
getSymbols('
MSFT'
from="
2010-01-03"
to="
2016-01-20"
)
##Asof0.4-0,'
getSymbols'
usesenv=parent.frame()and
##auto.assign=TRUEbydefault.
##Thisbehaviorwillbephasedoutin0.5-0whenthecallwill
##defaulttouseauto.assign=FALSE.getOption("
getSymbols.env"
)and
##getOptions("
getSymbols.auto.assign"
)arenowcheckedforalternatedefaults
##Thismessageisshownoncepersessionandmaybedisabledbysetting
##options("
getSymbols.warning4.0"
=FALSE).See?
getSymbolsformoredetails.
##[1]"
MSFT"
head(MSFT)
##MSFT.OpenMSFT.HighMSFT.LowMSFT.CloseMSFT.Volume
##2010-01-0430.6231.1030.5930.9538409100
##2010-01-0530.8531.1030.6430.9649749600
##2010-01-0630.8831.0830.5230.7758182400
##2010-01-0730.6330.7030.1930.4550559700
##2010-01-0830.2830.8830.2430.6651197400
##2010-01-1130.7130.7630.1230.2768754700
##MSFT.Adjusted
##2010-01-0425.71042
##2010-01-0525.71872
##2010-01-0625.56089
##2010-01-0725.29506
##2010-01-0825.46951
##2010-01-1125.14553
tail(MSFT)
##MSFT.OpenMSFT.HighMSFT.LowMSFT.CloseMSFT.Volume
##2016-01-1252.7653.1052.0652.7836095500
##2016-01-1353.8054.0751.3051.6466883600
##2016-01-1452.0053.4251.5753.1152381900
##2016-01-1551.3151.9750.3450.9970739100
##2016-01-1951.4851.6850.0650.5643564500
##2016-01-2049.9851.3849.1050.7963273000
##2016-01-1251.37038
##2016-01-1350.26083
##2016-01-1451.69157
##2016-01-1549.62819
##2016-01-1949.20968
##2016-01-2049.43353
#e.Plotgraphforstockprice5
plot(MSFT)
##Warninginplot.xts(MSFT):
onlytheunivariateserieswillbeplotted
#f.CalculatelogreturnsforAdjustedSeriesandPlotsimpletimeseriesgraphforreturns5
n<
-length(MSFT[,4]);
lrest<
-log(as.numeric(MSFT[,4])[-1]/as.numeric(MSFT[,4])[-n])
#g.Plotreturnsdistributiongraph5
hist(lrest,breaks=50,col="
green"
freq=F)
lines(density(lrest),col="
red"
)
#h.Whatdidyoulearnaboutthedatainstep(g)5
#直方图来看,数据为左偏分布
#i.CalculateBasicStatisticsforReturnSeriesandreport5
summary(lrest)
##Min.1stQu.MedianMean3rdQu.Max.
##-0.1210000-0.00766500.00000000.00032570.00817500.0994100
#j.Lookatthebasicstatsandcommentonthevaluesforskewnessandkurtosis5
#
library(fBasics)
timeDate
timeSeries
timeSeries'
##Thefollowingobjectismasked_by_'
.GlobalEnv'
##MSFT
##Thefollowingobjectismaskedfrom'
##time<
-
##RmetricsPackagefBasics
##AnalysingMarketsandcalculatingBasicStatistics
##Copyright(C)2005-2014RmetricsAssociationZurich
##EducationalSoftwareforFinancialEngineeringandComputationalScience
##RmetricsisfreesoftwareandcomeswithABSOLUTELYNOWARRANTY.
##https:
//www.rmetrics.org---Mailto:
info@rmetrics.org
fBasics'
TTR'
##volatility
datadesc=function(X){
result=list(0);
#resultlisttoreturn
mean=mean(X);
#mean
var=var(X)#variance,
pearsonskew=3*(mean(X)-median(X))/sd(X)#Pearsoncoefficientofskewness
kurt=kurtosis(X)#kurtosis,
quantile1=quantile(X,probs=0.25)#firstquartile,
med=median(X)#median,
quantile3=quantile(X,probs=0.25)#thirdquartile,
max=max(X)#minimumand
min=min(X)#maximum.
result=list(
mean=mean,
variance=var,
skewness=pearsonskew,
kurtosis=kurt,
"
firstquartile"
=quantile1,
median=med,
thirdquartile"
=quantile3,
maximum"
=max,
minimum=min
)
return(result)
}
datadesc(lrest)
##$mean
##[1]0.0003256584
##$variance
##[1]0.0002164387
##$skewness
##[1]0.06640734
##$kurtosis
##[1]7.519028
##attr(,"
method"
excess"
##$`firstquartile`
##25%
##-0.007664925
##$median
##[1]0
##$`thirdquartile`
##$maximum
##[1]0.09941299
##$minimum
##[1]-0.1210332
#从峰度和偏度的值来看,由于偏度大于零,因此数据成右偏分布。
峰度大于0,说明它是比正态分布要陡峭
#k.SetupHypothesisfortestingmean,skewnessandkurtosis5
#H0:
Ifvaluesofmean,skewnessandkurtosisisclosetozero,thendatasetisnormallydistributed.
#l.TesttheHypothesisusingtestslearnedinclass(i-individualtests;
andii-combinedtest–JB;
#reportyourtestresultswithp-valuesandwhetheryourejectoracceptH0)5
#i-individualtests;
#Shapiro-Wilk
shapiro.test(lrest)
##Shapiro-Wilknormalitytest
##data:
lrest
##W=0.93798,p-value<
2.2e-16
#ii-combinedtest–JB
tseries:
jarque.bera.test(lrest)
##JarqueBeraTest
##X-squared=3599.1,df=2,p-value<
##从结果来看,由于p小于0.05,因此可以拒绝原假设H0
#m.Commentaboutdatapropertiesbasedonthesetests:
#从正态分布的Shapiro-Wilk检验和jarque-Bera正态性检验的结果来看,有p小于0.05,因此可以拒绝对数收益率符合正态分布的假设,可以认为收益率,不是正态分布
#i.Thedifferencebetweenstep(g)vsstep(j)5
#从密度直方图的结果只能从直觉上判断数据是否符合正态分布,而通过峰度和偏度来判断,更加精确,可以从数字的角度来判断数据是否呈现偏正态分布。
#ii.whyjointtestsisbetterthanindividual
#Ajointhypothesistestsmorethanoneconditionsimultaneously
#7.Downloadthesp500datausingSymbol‘^GSPC’fromJan3,2011toJan20,2016(50pts)
#a.Carryoutinitialsteps,suchascalculatelogreturns,drawtimeplot,distributiongraph,
#calculatereturns,basicstatandemployrelatedtests25
GSPC'
2011-01-03"
GSPC"
head(GSPC)
##GSPC.OpenGSPC.HighGSPC.LowGSPC.CloseGSPC.Volume
##2011-01-0322.6022.60022.470822.49006600
##2011-01-0422.5922.59022.450022.47525100
##2011-01-0522.6122.76022.610022.710015000
##2011-01-0622.7622.93922.710022.760015900
##2011-01-0722.9322.93022.790022.92007100
##2011-01-1022.8122.97022.800022.970021500
##GSPC.Adjusted
##2011-01-0316.9375
##2011-01-0416.9264
##2011-01-0517.1032
##2011-01-0617.1409
##2011-01-0717.2614
##2011-01-1017.2990
tail(GSPC)
##2016-01-1221.4021.4121.340021.40006900
##2016-01-1321.4021.4221.300021.400011900
##2016-01-1421.4421.4421.220121.350011800
##2016-01-1521.3021.3021.050021.224511100
##2016-01-1921.3821.4021.050021.26598100
##2016-01-2021.2521.2721.074621.270023000
##2016-01-1220.4356
##2016-01-1320.4356
##2016-01-1420.3878
##2016-01-1520.2680
##2016-01-1920.3075
##2016-01-2020.3114
plot(GSPC)
##Warninginplot.xts(GSPC):
-length(GSPC[,4]);
-log(as.numeric(GSPC[,4])[-1]/as.numeric(GSPC[,4])[-n])
##[1]-4.395046e-05
##[1]6.726461e-05
##[1]-0.0160765
##[1]5.903059
##-0.003570522
##[1]0.04301739
##[1]-0.05164179
##W=0.92202,p-value<
##X-squared=1866.5,df=2,p-value<
#b.Whatdidyoulearnaboutthedatapropertiesinstep(a)5
#从描述统计量的结果来看,数据为左偏分布,从假设检验的结果来,由于p小于0.05,因此拒绝原假设,可以认为数据不符合正态分布
#c.Plotpacf,acf,alsochecktheorderofthemodelusing‘ar’commandinR
#WhichARmodelissuggested5
acf(lrest)
pacf(lrest)
ar(lrest)
##Call:
##ar(x=lrest)
##Coefficients:
##12345678
##-0.00600.05490.01320.0118-0.02370.04960.03420.0983
##910
##-0.04110.0416
##Orderselected10sigma^2estimatedas6.625e-05
#d.EstimatebothAR
(1)andAR
(2)modelforreturnstoSP500index
#andcommentonsignificanceofparameters5
fit=ar(lrest,FALSE,1)#fitar
(1)
fit
##ar(x=lrest,aic=FALSE,order.max=1)
##1
##-0.0084
##Orderselected1sigma^2estimatedas6.731e-05
(1-pnorm(abs(fit$ar)/sqrt(diag(fit$asy.var.coef))))*2
##[1]0.7640941
fit=ar(lrest,FALSE,2)#fitar
(2)
##ar(x=lrest,aic=FALSE,order.max=2)
##12
##-0.00790.0649
##Orderselected2sigma^2estimatedas6.708e-05
(1-pnorm(abs(fit$ar)/sqrt(diag(fit$asy.var.coef))))*2
##[1]0.778622760.02073683
#从结果来,ar2模型的系数显著性相对ar1更强
#e.Checkandreportiftheresidualsformodelsestimatedinpart(d)arewhitenoiseornot5
##ar(x=lrest,aic=FAL
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