The RATS Software Forum

I hope you understand that it's not our job to search the literature on your behalf. The fact that you seem to get similar results is not surprising. The QMLE results suggest that even though some of the results that you cite are generally designed assuming independent (or at least uncorrelated) obs...

I think you're missing the point. Yes, if you use overlapping ranges there will be serial correlation induced. (You're not "testing" for clustering. The serial correlation is there by construction). No, the fix for that is not likely to be simple as these are not statistics that fall into ...

@TVARSET sets up a VAR SYSTEM for time-varying coefficients estimation. It is based upon the standard Minnesota prior for the mean with the variances for time variation derived proportionately from those. An example of its use is provided in TVARSET.RPF.

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Are you asking me how to adjust those tests for clustering? Isn't that covered in the literature (somewhere)?

That's not correct; the covariance matrix of the sum of a set of uncorrelated random vectors is the sum of the covariances.

The series are correct; the display in the Series window isn't. The Series window was designed for displaying information with standard dates (it was originally for displaying RATS data files) and wasn't adapted to mapped dates, which require a separate "time series" of information with th...

Have you looked at TARMODELS.RPF: https://estima.com/webhelp/topics/tarmodelsrpf.html? Note (as that notes when analyzing the cycles in the branches) that the behavior of two branches tells you nothing about the overall behavior of the model. For instance, that has two non-stationary branches but th...

The formula in Tsay works in the one specific case and in no other and you can't tweak it to work in general. Yes, if the distribution of returns is MV Normal, those are the formulas.

Obviously, the ES is completely wrong. And it looks like you are missing the means in the VaR calculation---the formula in 7.2.2 of Tsay applies only to multivariate Normals with mean 0. The GARCH model generates a multivariate Normal distribution for the future returns. Given a set of portfolio wei...

The Tsay code is extremely clumsy. The model gives you a multivariate normal (or multivariate t) and a portfolio converts that into a univariate Normal (or t) as a weighted average of the elements. That is much more easily computed by using matrix calculations than by expanding the way Tsay does---I...

The formulas used in @MVGARCHFORE are covered on https://estima.com/webhelp/topics/garch-mv-forecast.html. It uses the raw residuals (for the first step; after that, the extrapolation uses the earlier forecasts). It can do any number of steps, not just one. And yes, you have to decide upon the portf...

Only in the trivial sense that a different model will give different results. The variance of a single asset portfolio depends only upon its particular diagonal element in the covariance matrix (i.e. its variance), not anything involving the other assets.

Isn't a single asset a particular portfolio?

VaR (and ES) to what?

The DVECH has an analytical formula for the extrapolated variance and, as with the univariate model, the extrapolated covariance matrix for the multiple step return is the sum of the covariance matrices you get for the intermediate horizons.

My understanding is that "technical trading" algorithms take moving window statistics (means, variances, percentiles,...) and produce a trading strategy from those. In what way is a (moving window) MV-GARCH model different from that? It's the same idea, just a different set of statistics. ...