@Kevin Schmit can you explain why the impulse response function (IRF) is important
The shortest answer is that your question is answered in exhaustive detail in the Lutkepohl book you reference below. Except, not being Canadian, Lutkepohl uses German consumption/income data and IRF analysis to show that information flows generally from [shocks to] income to changes in consumption but not vice versa (or maybe it is the other way around).
A slightly longer explanation is to note the similarity between the calculation of IRF (or, more properly for a VAR model - FEIR, Forecast Error Impulse Response, which name should give you a clue) and Diebold's calculation of directional info flow. Both start from the MA representation (companion form) of the VAR (in fact, in a certain sense the FEIR is equivalent to the Companion Form). The FEIR alone isn't enough to derive contemporaneous info-flow or causality. However you can gain directional insight by multiplying FEIR by a matrix "F." There are many ways to estimate F or F%*%phi, Easiest if to use Cholesky decomposition:
t(chol(summary(vmod)$covres)) where vmod is your VAR model. Or set ortho=TRUE in building your VAR model, I think it does the same thing. Lukepohl goes over a number of different approaches and explains it much better than I can.
To sum up, you want the contemporaneous impulse response to figure out which half of the pair or which members of a basket VECM is error correcting. However it is not absolutely necessary for simple single pairs trading -- you'll be fine trading both sides of the pair in classic pairs trading fashion.
There were a few papers on the OU model for pairs trading, but I really like the vars package
By all means, stick with what you are most comfortable with. OU is flexible, tractable (i.e. Gaussian Process), easy to recast in state-space representation, and gives you a clear model of the DGP. However your real edge here will come from understanding your model inside out, and you're a lot farther along in the VAR space than you are in OU or state-space space.
I am contemplating Richards recommendation and start on "New Introduction to Multiple Time Series Analysis" What do you think?
That is an excellent idea. The book is a classic and easily available online. The data used in the examples are available on the book's website.
Do you have SVECM/VECM R code lying around?
Nothing specific. I generally use VAR family models for exploratory data analysis and can usually coerce the vars package into doing what I want. When, in the past, I traded baskets of single pairs, I did not use (S)VAR for the final models,
Here is a paper about using VECM for pairs trading.
Thanks. I've put it on my reading list.
The graph has clear buy/sell signals.
Usually I'm a big proponent of stats analysis via visual inspection of plots. But not in this case. I've also never seen a bad hypothetical track record or unprofitable backtest graph.
This is how I pictured it. I find a group of cointegrated pairs -> create vecm -> see how far current residual is from mean -> place trade using beta/intercept from model as hedge ratio (long/short for now
That should work. One other element you might want to add. I'll pm you on it.
A major feature of a VECM model is the IRF and the longRun-shortRun effects. I am having a hard time seeing how this fits into pairs trading.
Skim the Lutkepol book. I'll take a look at the table of contents again (it's been a few years) and let you know which sections I think might be most relevant. For now, given your plan just above, you don't need estimates of one-way information. So just keep reading/studying and add in the bells and whistles later.
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