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I am trying to calculate the log returns of a dataset in R using the usual log differencing method. However, the calculated data is simply a vector of zeroes. I can't see what I'm doing wrong.

Here is the snippet showing what I'm doing

> prices <- data$cl
> head(prices)
[1] 1108.1 1095.4 1095.4 1102.2 1096.3 1096.7
>
>
> lrets <- log(lag(prices)) - log(prices)
> head(lrets)
[1] 0 0 0 0 0 0
> summary(lrets)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0       0       0       0

What am I doing wrong?

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    $\begingroup$ lrets <- diff(log(prices)) $\endgroup$ – Vishal Belsare Feb 6 '12 at 11:55
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    $\begingroup$ @VishalBelsare you should add that as an answer. $\endgroup$ – Louis Marascio Nov 23 '12 at 15:42
  • $\begingroup$ @Patrick Burns, vonjd, aajajim thanks to all of you for answering. I learned new information. $\endgroup$ – thelearner Sep 21 '16 at 12:39
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You are simply doing $log(S_t) - log(S_t) = 0$ for all $t$. Instead, try

> n <- length(prices);
> lrest <- log(prices[-1]/prices[-n])

Should do the trick.

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    $\begingroup$ Or the more traditional, fewer characters: diff(log(prices)) which also works when 'prices' is a matrix with times in the rows and assets in the columns. The other lesson is that 'lag' doesn't do what we naively expect it to do. $\endgroup$ – Patrick Burns Feb 6 '12 at 10:54
  • $\begingroup$ To be sure that lag works as you expect, it is much safer to store your time series as zoo or xts objects: if you use vectors (or even ts objects), many operations will discard or ignore the timestamps. $\endgroup$ – Vincent Zoonekynd Feb 6 '12 at 11:48
  • $\begingroup$ hadnt noticed the "diff" function yet. A handy one, indeed. $\endgroup$ – Nemis Feb 6 '12 at 12:39
  • $\begingroup$ @PatrickBurns: +1 for your input. I preferred your more succinct syntax. Would have accepted that as an answer. $\endgroup$ – Homunculus Reticulli Feb 6 '12 at 13:06
  • $\begingroup$ i have been using diff(log(prices)) for a while, but was starting to doubt as it seems almost noone use it. thanks :) $\endgroup$ – tagoma Jul 26 '12 at 17:35
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An easy way to perform what you need is do it this way:

if your data are daily then :

> prices <- data$cl
> log_returns <- diff(log(prices), lag=1)

would provide you with daily log returns, if you change the $lag=1$ to $lag=5$ then you will get weekly moving log returns.

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  • $\begingroup$ While I have some experience in finance, I'm new to R (and this site). Question on this post, @aajajim: your suggestion looks very appealing, but another commenter suggests working in zoo, with which I am familiar. Since zoo allows for an irregular times series, how does your answer change if using zoo? $\endgroup$ – W Barker Apr 15 '14 at 18:28
  • $\begingroup$ Doesn't change at all, it's still the same code. At least for my zoo object the function he posted worked without any flaws. $\endgroup$ – Olorun May 31 '14 at 4:09
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I think the simplest method for calculating log returns is ROC from the TTR package:

> data(ttrc)
> roc <- ROC(ttrc[,"Close"])

https://CRAN.R-project.org/package=TTR

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just to add another method:

>lrtn=diff(log(prices))

for daily log returns, if you have daily prices.

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if you want to get rid of the first NA produced you can either start at 0 or omit the first row like this:

require(quantmod)
getSymbols("MSFT")
MSFT$Log_Returns <- diff(log(MSFT$Adjusted)); MSFT$Log_Returns[1] <- 0 # this will make the first row of your returns series equal 0, which arguably is correct for any starting date.

MSFT$Log_Returns <- diff(log(MSFT$Adjusted))
MSFT <- MSFT[2:nrow(MSFT),] # this option will remove the first row which is a NA and therefore not introduce a 0.

Both options work fine, hope it helps.

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