[R-SIG-Finance] Possible enhancement to volatility in TTR
Joshua Ulrich
josh.m.ulrich at gmail.com
Mon Sep 28 19:08:38 CEST 2009
Hi Cedrick,
Sorry for the slow response. How about this quick change to
TTR::volatility() and the code below? I agree that OHLC should be
allowed to be univariate in the case of calc="close". I'm hesitant to
add the other logic though, since the same results can be achieved
with an external call to apply() -- see below.
volatility3 <-
function (OHLC, n = 10, N = 260, calc = "close", ...)
{
<snip>
if (calc == "close") {
# Add univariate case for calc="close"
if (NCOL(OHLC) == 1) {
r <- ROC(OHLC, 1, ...)
} else {
r <- ROC(OHLC[, 4], 1, ...)
}
rBar <- runSum(r, n - 1)/(n - 1)
s <- sqrt(N/(n - 2) * runSum((r - rBar)^2, n - 1))
}
<snip>
reclass(s, OHLC)
}
Then you can get pretty volatilities via a command like:
> require(xts)
> data(sample_matrix)
> # Pretty volatility (note "v" will always be a matrix, even if OHLC is xts)
> v <- round(apply(sample_matrix, 2, volatility3)*100,0)
> tail(v)
Open High Low Close
2007-06-25 6 5 5 6
2007-06-26 6 6 6 6
2007-06-27 6 6 6 6
2007-06-28 6 5 6 5
2007-06-29 5 5 6 5
2007-06-30 5 5 6 5
> tail( volatility3(sample_matrix)*100 )
[,1]
2007-06-25 5.656552
2007-06-26 5.729933
2007-06-27 5.732217
2007-06-28 4.771530
2007-06-29 4.764216
2007-06-30 4.818589
HTH,
Josh
--
http://www.fosstrading.com
On Mon, Sep 28, 2009 at 9:34 AM, Cedrick Johnson
<cedrick at cedrickjohnson.com> wrote:
> As promised, here's my attempt at incorporating it into the original
> volatility() function (for the sake of testing, I've renamed the new
> function volatility2(..). Thanks to Murali for the suggestion to use
> apply.rolling, I've added in a few new variables in the function definition.
> So far it seems to work well here.
>
> volatility2 <- function (OHLC, n = 10, N = 260, calc = "close",
> roundValue=FALSE, roundNum=2, normalize=FALSE, nonOHLC=FALSE, ...)
> {
> if (nonOHLC == FALSE) {
>
> OHLC <- try.xts(OHLC, error = as.matrix)
> calc <- match.arg(calc, c("close", "garman.klass", "parkinson",
> "rogers.satchell"))
> if (calc == "close") {
> r <- ROC(OHLC[, 4], 1, ...)
> rBar <- runSum(r, n - 1)/(n - 1)
> s <- sqrt(N/(n - 2) * runSum((r - rBar)^2, n - 1))
> }
> if (calc == "garman.klass") {
> s <- sqrt(N/n * runSum(0.5 * log(OHLC[, 2]/OHLC[, 3])^2 -
> (2 * log(2) - 1) * log(OHLC[, 4]/OHLC[, 1])^2, n))
> }
> if (calc == "parkinson") {
> s <- sqrt(N/(4 * n * log(2)) * runSum(log(OHLC[, 2]/OHLC[,
> 3])^2, n))
> }
> if (calc == "rogers.satchell") {
> s <- sqrt(N/n * runSum(log(OHLC[, 2]/OHLC[, 4]) * log(OHLC[,
> 2]/OHLC[, 1]) + log(OHLC[, 3]/OHLC[, 4]) * log(OHLC[,
> 3]/OHLC[, 1]), n))
> }
> } else {
> #still using the OHLC object, since this is indeed a matrix
> # diff(log(OHLC)) provides the same output as ROC(x,1) so we should be
> goog. Thanks to Murali Menon
> # for the suggested use of apply.rolling
> s <- apply.rolling(R = diff(log(OHLC)), width=n, FUN="sd", na.pad=FALSE)
> * sqrt(N)
> }
>
> if (roundValue == TRUE) {
> s <- round(s, roundNum)
> }
> if (normalize == TRUE) {
> s <- s * 100
> }
>
> reclass(s, OHLC)
> }
>
>
> Cedrick Johnson wrote:
>
> Howdy-
>
> I came up with some code that has served me well, perhaps if anyone runs
> into the problem of needing to calculate historical volatility(close) on a
> non-OHLC series (or a bunch of "univariate" series stored in a matrix):
>
> US2Y US5Y US10Y US30Y UKG5 UKG10 USSP2 USSP5 USSP10 USSP30
> 2009-01-02 0.88 1.72 2.46 2.83 2.67 3.36 1.50 2.17 2.61 2.78
> 2009-01-05 0.78 1.67 2.49 3.00 2.75 3.47 1.60 2.31 2.82 3.03
> 2009-01-06 0.80 1.68 2.51 3.04 2.79 3.57 1.55 2.34 2.88 3.18
> 2009-01-07 0.82 1.66 2.52 3.05 2.80 3.60 1.44 2.16 2.69 2.99
> ....
> blahblah..
>
> I adapted the code to use 'close' from Josh Ulrich's volatility function
> (since all of these are closing prices), and here's my result:
>
> volatility.matrix <- function(Matrix, n = 10, N = 252, pretty=TRUE, ...) {
> volm <- Matrix
> for(i in 1:ncol(Matrix)) {
> r <- ROC(Matrix[,i], 1,...)
> rBar <- runSum(r, n -1)/(n - 1)
> s <- sqrt(N/(n - 2) * runSum((r - rBar)^2, n - 1))
> #because i like to make it pretty
> volm[,i] <- round(s,2) * 100
> }
> volm <- na.omit(volm)
> reclass(volm, Matrix)
> }
>
>
> The Result:
>> volatility.matrix(Yields, n=30)
> US2Y US5Y US10Y US30Y UKG5 UKG10 USSP2 USSP5 USSP10 USSP30
> 2009-03-24 116 110 73 45 54 57 67 65 60 51
> 2009-03-25 115 112 75 46 51 55 67 65 60 51
> 2009-03-26 114 110 74 44 50 55 65 64 60 50
> 2009-03-27 114 110 74 44 50 55 67 64 60 51
> 2009-03-30 111 104 68 39 52 55 67 63 58 51
> 2009-03-31 107 100 68 39 51 55 67 63 58 50
> 2009-04-01 107 100 67 38 51 54 67 62 57 49
> 2009-04-02 107 100 67 37 58 57 65 61 56 48
> ......
>
> What I get is a nice matrix of volatilities in a pretty format (rounded,
> etc.)
>
> I'll give it a shot tonight trying to implement it into the current
> volatility function, however I'm sure the code that I have here could be
> done better.
>
> Regards,
> c
>
>
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