[R-SIG-Finance] [R-sig-finance] Rolling Beta

[Brian G. Peterson]

As with most things in R, you can see the code by typing the function 
name without the parentheses.

In the case of rolling beta, the important line is this:

rollapply(na.omit(merged.assets[,, drop = FALSE]),
                  width = width, FUN = function(x) lm(x[,1, drop = FALSE]
                  ~ x[, 2, drop = FALSE])$coefficients[2],
                  by = 1, by.column = FALSE, na.pad = na.pad,
                  align = "right")

Regards,

   - Brian


Eduard Pieterse (Macquarie Securities) wrote:
> Hi Brian,
>
> Chart.rollingRegression seems to be exactly what I'm after. How would I
> "disconnect" the chart output and be able to save/use the data behind
> it?
>
> I am still finding my feet with R and know what I need to do, but my R
> language knowledge is lacking somewhat.
>
> Thanks,
> Eduard
>
> -----Original Message-----
> From: Brian G. Peterson [mailto:brian at braverock.com] 
> Sent: 19 October 2009 17:28
> To: Eduard Pieterse (Macquarie Securities)
> Cc: r-sig-finance at stat.math.ethz.ch
> Subject: Re: [R-SIG-Finance] [R-sig-finance] Rolling Beta
>
> see PerformanceAnalytics functions
>
> CAPM.beta
> and
> chart.RollingRegression
> and
> charts.RollingRegression
>
> Cheers,
>
>   - Brian
>
> ehxpieterse wrote:
>   
>> Hi,
>>
>> I have had a *very* rough attempt at estimating the beta between 
>> indices. I am convinced that someone on this list has had experience 
>> with this before, but my search yielded little information. I have 
>> daily log returns from 01/01/2000 on 13 different equity indices. I am
>>     
>
>   
>> just looking for an efficient way to produce a rolling beta for all
>>     
> index pairs.
>   
>> I would appreciate any advice.
>>
>> Thanks,
>> Eduard
>>
>> CODE:
>>
>> IndexLocal <- read.csv("IndexLocal.csv", header=TRUE, sep = ",") MyLag
>>     
>
>   
>> <- 30 IndexDim <- dim(IndexLocal) Results <- NULL reg <- NULL k <- 0
>>
>> for (i in 2:14)
>> {
>> 	for (j in 2:14)
>> 	{
>> 			k <- k + 1
>> 			for (r in MyLag :IndexDim[1])
>> 			{
>> 				x. <- IndexLocal[(r-MyLag+1):r,i]
>> 				y. <- IndexLocal[(r-MyLag+1):r,j]
>>     
>
>   
>> 				glm.linear<-coef(glm(y. ~ x.))
>> 				reg[r] <- glm.linear[2]
>> 			}
>> 				if (j >= i)
>> 				{
>> 					Results <- cbind(Results,reg)
>> 				}
>> 	}
>> }
>>
>>     

-- 
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock