#Illustration of S-Plus functions (Used on Day 5)
#getReturns
#autoCorTest
#Reminder of the "@" extractor and the "$" extractor
#for slotnames and names (for two flavors of S objects)

module(finmetrics)

#We load the finmetrics module which has a data set in it called singleIndex.dat
#We looked at it before, but lets look again.

class(singleIndex.dat)

slotNames(singleIndex.dat)

#Lets put the MSFT prices into a vector.


msft.p=singleIndex.dat[, "MSFT"]

msft.p

#Let's look at these prices

plot(msft.p)

#Tools for getReturns()
#See discussion in ZW

args(getReturns)

msft.ret=getReturns(msft.p,percentage=T)

#we looked at the plot before; let's look again.

plot(msft.ret)

#Now, when we look at this can we tell if this is just noise.

help(autocorTest)

#Now, lets give it a look.

autocorTest(msft.ret)

#This is non-significant. Irritating --- disappointing... maybe wrong?

#Let's explore. First, let's look at price --- just for fun

autocorTest(msft.p)

#Wow, insanely significant ... is this COOL .. or is it BOGUS?

#What are the assumtions of the autocorTest? At a minimum, correlations have to
#make sense. Do correlations in prices make sense? NO!
#Why? Well, there is no chance they are stationary!

#We have to look at returns ... they have a chance of being stationary.

#So, OK, Let's look directly at the autocorrelations..

acf(msft.ret)

#There looks like there is something there
#Maybe if we don't that the defacult number of lags in our Ljung-Box test.

autocorTest(msft.ret, lag.n=2)

#Pretty close to significant. I'm not going to disregard the possibility that MSFT
#had an eploitable level of month-to-month correlation during this study period.
#There are lots of things to worry about, but we have a little nibble --- 
#an indication of "signal." 

#Supponse we just look exactly at one month.

autocorTest(msft.ret, lag.n=1)

#Now, just to cover some ground for HW 2, lets use the $ extractor to understand the #information provided by acf()

v=acf(msft.ret, plot=F)

v

names(v)

w=v$acf

w[1:5]

#You can use acf() and this way of extracting information from the value 
#the acf returns in order to build the function for LB that you need for HW2.

#Note that the first value of w is the lag 0 corrleation, which is 1.
#Note also from the definition of the LB statistic that it just contains
#terms with lag 1,2,...,k.
#Correct code for HW2 will need to take this into consideration.