Date: Wed, 12 Dec 2007 06:05:07 -0600
From: brian at braverock.com
To: feanor0 at hotmail.com; r-sig-finance at stat.math.ethz.ch
Subject: Re: [R-SIG-Finance] Riskmetrics volatility and correlation estimation
Murali Menon wrote:
Are there functions available to compute the Riskmetrics (1996)
volatilities and correlations for financial time-series?
I refer to the exponentially weighted moving average vols
and exponentially smoothed correlations (with lambda = 0.94).
I looked in the VarModelling part of fPortfolio, but this
stuff doesn't seem to be there?
Not that I know of, but they shouldn't be too hard to construct.
So, if you want help constructing them from this group:
1> post the link to the RiskMetrics algorithms
2> do a little research on Google and the list archives
There have been several examples posted of exponentially weighted moving
averages in R which should go a long way toward solving the volatility
question above, for example.
R contains many different smoothing algorithms, as you can see with
help.search("smooth") or help.search("smoothing")
3> suggest an approach, try an approach, post your failures
and I'm sure someone here can probably help you out.
Another interesting (to me anyway) question is "Why do you care?" What
in the literature leads you to want to try these techniques? Are you
just trying to replicate a set of RiskMetrics algorithms in R? Have you
looked at other research on smoothing and rolling windows? I ask this
trailing set of questions because often when I go looking in the
literature, I find that there are several newer techniques which have
been shown to work better than the older methods. Sometimes these newer
techniques are already implemented in R, other times I have to do it
(but at least I'm then implementing a more modern approach).
Regards,
- Brian