Jeffrey, Thanks. I apologize for not providing sample data. I think I need some more help.

 

Although I really did recognize that xts is a superior time series class, my initial efforts used standard R. I think I could eventually have produced the correct results, but probably not very efficiently.

 

Jan = subset(theReturnsData,months(theReturnsData[,1]) == "January")

Feb = subset(theReturnsData,months(theReturnsData[,1]) == "February")

 

Jansd = apply(Jan[,-1],2,sd, na.rm = TRUE)

Febsd = apply(Feb[,-1],2,sd, na.rm = TRUE)

 

rbind(Jansd, Febsd) # would have to do this for all months. 

 

Below is the head and tail of the data and some other information.



WhatI am trying to do.

For each column, I want to determine the volatility for all of the Q1 data. Similarly for Q2, Q3, Q4. So I will have an output of just four rows for each column. I will also be doing this for the 12 months (need 12 rows) and for each of the years (unspecified in advance; I am writing a function).

 

I also have a second question. Say I wanted to break the data up by quarters. What would I do to produce correlations? For each pair of columns, each quarter would have a correlation. I would be looking to produce four correlation matrices. For example for Q1 there would be a 50 by 50 correlation matrix; and similarly for Q2, Q3, Q4.

 

theReturns = read.zoo("returns large.csv", sep = ",", header = TRUE, format = "%m/%d/%Y")

> dim(theReturns)

[1] 2386   50

 

 

> head(theReturnsData[,1:4]) # data frame format

  Date               SEC1     SEC2    SEC3

  2001-10-04  0.0657   0.0249   0.0236

  2001-10-05 -0.0237  -0.0106  -0.0070

  2001-10-08 -0.0234   0.0027   0.0022

  2001-10-09  0.0058   0.0013   0.0013

  2001-10-10  0.0143   0.0022   0.0031

  2001-10-11  0.0509   0.0360   0.0318

 

> tail(theReturnsData[,1:4])

 Date               SEC1     SEC2    SEC3

2011-04-14  0.011   0.0093   0.0092

2011-04-15  0.012   0.0143   0.0140

2011-04-18 -0.019  -0.0232  -0.0230

2011-04-19  0.008   0.0096   0.0055

2011-04-20  0.024   0.0305   0.0335

2011-04-21  0.014   0.0075   0.0075

  ----- Original Message ----- 
  From: Jeffrey Ryan 
  To: Ira Sharenow 
  Cc: r-sig-finance@r-project.org 
  Sent: Thursday, August 25, 2011 5:20 PM
  Subject: Re: [R-SIG-Finance] Combined seasonal data using xts


  Its a bit hard to tell what you want as you haven't provided a reproducible example as requested in the posting guidelines.


  Without that I can guess.  First, there are no alternatives to xts ;-)


  Second, you probably want to look at ?split.xts (which is just the xts method split) combined with lapply.  A quick search of this list over the last month will get you examples of its use and power.


  Aside from that, post a sample code and we can help further if need be.


  Best,
  Jeff


  On Thu, Aug 25, 2011 at 5:37 PM, Ira Sharenow <irasharenow100@yahoo.com> wrote:

    I have multivariate time series data and I am using xts but am open to alternatives. I am trying to write a function for a user.



    For all years combined, I would like to find quarterly volatility (sd). I want to have just four outputs: Q1, Q2, Q3, Q4.



    Can someone help me out?



    If instead I wanted to find the volatility for each quarter of each year, I would use something like:



    apply.quarterly(xInput, function(x) apply(x,2,sd))



    This would give me results for 2008Q1, 2008Q2, ., 2009Q1, ., 2011Q2

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  Jeffrey Ryan
  jeffrey.ryan@lemnica.com

  www.lemnica.com

  www.esotericR.com


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