r语言股票下载 ar模型 描述统计 附代码数据Word下载.docx

1、#Microsoft symbol in Google finance is MSFT# d. Download Microsoft stock price data from Jan 3, 2010 to Jan 20, 2016 in R using getSymbols command in# Quantmod, check if data is read properly using head and tail command 5getSymbols（MSFT, from = 2010-01-03, to = 2016-01-20） # As of 0.4-0, getSymbols

2、uses env=parent.frame（） and# auto.assign=TRUE by default.# This behavior will be phased out in 0.5-0 when the call will# default to use auto.assign=FALSE. getOption（getSymbols.env） and # getOptions（getSymbols.auto.assign） are now checked for alternate defaults# This message is shown once per session

3、 and may be disabled by setting # options（getSymbols.warning4.0=FALSE）. See ?getSymbols for more details.# 1 MSFThead（MSFT）# MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume# 2010-01-04 30.62 31.10 30.59 30.95 38409100# 2010-01-05 30.85 31.10 30.64 30.96 49749600# 2010-01-06 30.88 31.08 30.52 30.

4、77 58182400# 2010-01-07 30.63 30.70 30.19 30.45 50559700# 2010-01-08 30.28 30.88 30.24 30.66 51197400# 2010-01-11 30.71 30.76 30.12 30.27 68754700# MSFT.Adjusted# 2010-01-04 25.71042# 2010-01-05 25.71872# 2010-01-06 25.56089# 2010-01-07 25.29506# 2010-01-08 25.46951# 2010-01-11 25.14553tail（MSFT）# M

5、SFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume# 2016-01-12 52.76 53.10 52.06 52.78 36095500# 2016-01-13 53.80 54.07 51.30 51.64 66883600# 2016-01-14 52.00 53.42 51.57 53.11 52381900# 2016-01-15 51.31 51.97 50.34 50.99 70739100# 2016-01-19 51.48 51.68 50.06 50.56 43564500# 2016-01-20 49.98 51.38

6、49.10 50.79 63273000# 2016-01-12 51.37038# 2016-01-13 50.26083# 2016-01-14 51.69157# 2016-01-15 49.62819# 2016-01-19 49.20968# 2016-01-20 49.43353# e. Plot graph for stock price 5plot（MSFT）# Warning in plot.xts（MSFT）: only the univariate series will be plotted# f. Calculate log returns for Adjusted

7、Series and Plot simple time series graph for returns 5 n - length（MSFT,4）;lrest - log（as.numeric（MSFT,4）-1/as.numeric（MSFT,4）-n）# g. Plot returns distribution graph 5hist（lrest,breaks = 50,col = green,freq = F）lines（density（lrest）,col=red）# h. What did you learn about the data in step （g） 5#直方图来看,数据

8、为左偏分布# i. Calculate Basic Statistics for Return Series and report 5summary（lrest）# Min. 1st Qu. Median Mean 3rd Qu. Max. # -0.1210000 -0.0076650 0.0000000 0.0003257 0.0081750 0.0994100# j. Look at the basic stats and comment on the values for skewness and kurtosis 5#library（fBasics） timeDate timeSer

9、iestimeSeries# The following object is masked _by_ .GlobalEnv# MSFT# The following object is masked from # time-# Rmetrics Package fBasics# Analysing Markets and calculating Basic Statistics# Copyright （C） 2005-2014 Rmetrics Association Zurich# Educational Software for Financial Engineering and Comp

10、utational Science# Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY.# https:/www.rmetrics.org - Mail to: informetrics.orgfBasicsTTR# volatilitydatadesc = function（X） result = list（0）;#result list to return mean = mean（X）;#mean var = var（X）#variance, pearsonskew = 3*（mean（X）-median（X）/

11、sd（X）#Pearson coefficient of skewness kurt = kurtosis（X） #kurtosis, quantile1 = quantile（X,probs = 0.25） # first quartile, med = median（X）# median, quantile3 = quantile（X,probs = 0.25）# third quartile, max = max（X）# minimum and min = min（X）# maximum. result = list（ mean = mean, variance = var, skewn

12、ess = pearsonskew, kurtosis = kurt, first quartile = quantile1, median = med,third quartile = 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（,methodexcess# $first quartile#

13、 25% # -0.007664925 # $median# 1 0# $third quartile# $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. Setup Hypothesis for testing mean, skewness and kurtosis 5#H0: If values of mean, skewness and kurtosis is close to zero, then data set is normally

14、distributed.# l. Test the Hypothesis using tests learned in class （i-individual tests; and ii-combined test JB;# report your test results with p-values and whether you reject or accept H0） 5#i-individual tests;# Shapiro-Wilkshapiro.test（lrest）# Shapiro-Wilk normality test# data: lrest# W = 0.93798,

15、p-value 2.2e-16#ii-combined test JBtseries:jarque.bera.test（lrest）# Jarque Bera Test# 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. Comment about data properties based on these tests: #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.

16、 The difference between step （g） vs step （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. why joint tests is better than individual# A joint hypothesis tests more than one condition simultaneously# 7. Download the sp500data using Symbol GSPC from Jan 3, 2011 to Jan 20,

17、 2016 （50 pts）# a. Carry out initial steps, such as calculate log returns, draw time plot, distribution graph,# calculate returns, basic stat and employ related tests 25GSPC2011-01-03GSPChead（GSPC）# GSPC.Open GSPC.High GSPC.Low GSPC.Close GSPC.Volume# 2011-01-03 22.60 22.600 22.4708 22.4900 6600# 20

18、11-01-04 22.59 22.590 22.4500 22.4752 5100# 2011-01-05 22.61 22.760 22.6100 22.7100 15000# 2011-01-06 22.76 22.939 22.7100 22.7600 15900# 2011-01-07 22.93 22.930 22.7900 22.9200 7100# 2011-01-10 22.81 22.970 22.8000 22.9700 21500# GSPC.Adjusted# 2011-01-03 16.9375# 2011-01-04 16.9264# 2011-01-05 17.

19、1032# 2011-01-06 17.1409# 2011-01-07 17.2614# 2011-01-10 17.2990tail（GSPC）# 2016-01-12 21.40 21.41 21.3400 21.4000 6900# 2016-01-13 21.40 21.42 21.3000 21.4000 11900# 2016-01-14 21.44 21.44 21.2201 21.3500 11800# 2016-01-15 21.30 21.30 21.0500 21.2245 11100# 2016-01-19 21.38 21.40 21.0500 21.2659 81

20、00# 2016-01-20 21.25 21.27 21.0746 21.2700 23000# 2016-01-12 20.4356# 2016-01-13 20.4356# 2016-01-14 20.3878# 2016-01-15 20.2680# 2016-01-19 20.3075# 2016-01-20 20.3114plot（GSPC）# Warning in plot.xts（GSPC）:- length（GSPC,4）;- log（as.numeric（GSPC,4）-1/as.numeric（GSPC,4）-n）# 1 -4.395046e-05# 1 6.726461

21、e-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. What did you learn about the data properties in step （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. Plot pacf, acf, also check the ord

22、er of the model using ar command in R# Which AR model is suggested 5acf（lrest）pacf（lrest）ar（lrest）# Call:# ar（x = lrest）# Coefficients:# 1 2 3 4 5 6 7 8 # -0.0060 0.0549 0.0132 0.0118 -0.0237 0.0496 0.0342 0.0983 # 9 10 # -0.0411 0.0416 # Order selected 10 sigma2 estimated as 6.625e-05# d. Estimate

23、both AR（1） and AR（2） model for returns to SP500 index# and comment on significance of parameters 5fit =ar（lrest, FALSE, 1） # fit ar（1）fit # ar（x = lrest, aic = FALSE, order.max = 1）# 1 # -0.0084 # Order selected 1 sigma2 estimated as 6.731e-05 （1-pnorm（abs（fit$ar）/sqrt（diag（fit$asy.var.coef）*2# 1 0.

24、7640941fit=ar（lrest, FALSE, 2） # fit ar（2）# ar（x = lrest, aic = FALSE, order.max = 2）# 1 2 # -0.0079 0.0649 # Order selected 2 sigma2 estimated as 6.708e-05（1-pnorm（abs（fit$ar）/sqrt（diag（fit$asy.var.coef）*2# 1 0.77862276 0.02073683#从结果来,ar2模型的系数显著性相对ar1更强 # e. Check and report if the residuals for models estimated in part （d） are white noise or not 5# ar（x = lrest, aic = FAL