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I wanted to know if anyone here has applied multiple regression to their crude oil trading. I've built a relative-value model using some US Gulf Coast physical grades. I wanted to see if anyone else has done this or not.
Creating fundamental models can be difficult when it comes to backtesting mostly because the dynamics of the market change every couple of years. How have you tackled this issue?
Trading: Primarily Energy but also a little Equities, Fixed Income, Metals and Crypto.
Frequency: Many times daily
Duration: Never
Posts: 5,049 since Dec 2013
Thanks Given: 4,386
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I have models that look at curve shape but not at all the different grades. Very interesting. How do you handle the issue that part of the crude differentials are location driven rather than quality/value driven?
I think most market participants can't differentiate between grades so it becomes less of an issue in my opinion when trading financials. But grade diffs drive global arbs for crude so I try to model that in.
Trading: Primarily Energy but also a little Equities, Fixed Income, Metals and Crypto.
Frequency: Many times daily
Duration: Never
Posts: 5,049 since Dec 2013
Thanks Given: 4,386
Thanks Received: 10,206
Oh I agree. I was just thinking that when you look at the price of WTS at Midland, how much of that discount is due to grade and how much due to logistic constraints in the Permian Basin.
Good point, that's very hard to segregate in this particular model. If you think about it though, we (US) like to consume medium grade stuff, which we don't produce much of, and we export lighter bbls, which is mostly all we produce. I think they'll push as much as possible down to the coast for export. Last few weeks you're seeing Rig count either stay constant or actually decline and because of that I believe you're seeing Midland appreciate against WTI-Houston and Cushing. Pipes are running full but the magnitude of location-diffs can really help us predict where the main financial benchmark, Cushing, is heading ... at least this is what I'm trying to prove statistically ..
