[R-SIG-Finance] Riskmetrics volatility and correlation estimation

[Murali Menon]

Brian,

Thanks for the suggestions and tips. Indeed, after nabbling away in the archives, I found a Zivot statement to the effect that EWMA (function available in fMultivar) on squared returns is equivalent to RiskMetrics vol. No equivalent methodology as far as I can see for correlations, so I thought I'd post a query before attempting to implement it myself.

As for RiskMetrics, I've seen the following paper that discusses why RiskMetrics seems to work reasonably well in practise even if it is mis-specified for the data (algorithms given herein):

http://www.colbud.hu/pdf/Kondor/riskm.pdf

The idea is that for the short-horizon, 95%-confidence level sort of VaR computation, even non-normal distributions have quantiles close to the normal one, so RiskMetrics works okay; but will fail to do so for longer horizons and/or 99%-ile queries.

Engle's DCC paper: http://pages.stern.nyu.edu/~rengle/dccfinal.pdf shows that RiskMetrics methodology outperforms the rolling technique in estimating and forecasting correlation, even if not always so well as dynamic conditional correlations (which I don't think is available in R either).

Thanks,

Murali

> 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

_________________________________________________________________
[[replacing trailing spam]]