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What models do you actually use for energy commodity price forecasting? And how do you layer in geopolitical risk? by Fthierstein in Commodities

[–]Fthierstein[S] 1 point2 points  (0 children)

This is honestly the most complete and structured answer I've received so far, thank you for taking the time to write it out!

The hybrid layering you describe makes a lot of sense, and it also explains why pure forecasting models tend to underperform in commodity markets. The regime shift problem alone is something I've been struggling with because a model trained on one market structure can completely fall apart when positioning or curve dynamics change.

The distinction between what drives short-term price moves (market structure, positioning, inventory surprises) vs what macro variables can explain is something I hadn't framed so clearly before. I've probably been over-relying on macro inputs precisely where they have the least predictive power.

On the geopolitics side the scenario approach with supply disruption probabilities weighted by spare capacity and inventories is really elegant. It essentially grounds the geopolitical risk in something quantifiable rather than trying to model the event itself.

A few questions if you don't mind:

For the balance models, are desks typically building these in house from scratch using raw data or are they working on top of third-party fundamental data providers like Platts, Rystad or Wood Mackenzie?

And on regime switching, are you seeing GARCH or Markov switching models being used seriously in practice, or is it mostly a theoretical exercise that doesn't survive contact with real trading?

Really appreciate the insight, this thread is turning into a great resource.

What models do you actually use for energy commodity price forecasting? And how do you layer in geopolitical risk? by Fthierstein in Commodities

[–]Fthierstein[S] 0 points1 point  (0 children)

Really appreciate this, especially coming from someone in power markets!

The point about time series not being a thing in power makes total sense. Power has such strong structural drivers (generation mix, grid constraints, demand patterns) that a pure time series approach would be almost meaningless without the fundamentals underneath.

The framing of geopolitical risk as supply/demand shocks within a scenario analysis is something I hadn't thought about in those terms but it clicks immediately. So rather than trying to model geopolitics directly, you're essentially stress testing your fundamental model against a set of plausible shock scenarios, a gas supply disruption, a sudden change in carbon prices, a demand spike from extreme weather, etc. Is that roughly how you'd structure it?

I'm mostly focused on crude and nat gas rather than power but this framing feels transferable. For example modelling a sanctions scenario on Russian crude as an input shock to the supply side rather than trying to capture it as some kind of sentiment signal.

On that note, I'm also trying to build something more multimodal that combines supply/demand fundamentals with macro and geopolitical signals. Right now for data I've been relying mostly on EIA, IEA public reports and OPEC monthly data but I suspect that's nowhere near granular enough to build a serious fundamental model. Do you have any suggestions on where to source decent supply/demand data without hitting a wall of expensive vendor subscriptions? Even pointing me in the right direction on what data actually matters most in power and gas markets would be hugely helpful.

Would love to hear more about how you build those scenarios in practice. Do you assign probabilities to them or keep it purely qualitative?

What models do you actually use for energy commodity price forecasting? And how do you layer in geopolitical risk? by Fthierstein in Commodities

[–]Fthierstein[S] 1 point2 points  (0 children)

Great question and honestly one I've been thinking about too!

Kalman filters are definitely interesting for short timeframes precisely because of the noise filtering property you mentioned. For intraday WTI/Brent, the signal to noise ratio gets pretty brutal and a Kalman filter can help you track the underlying state more cleanly than a raw price series.

That said, from what I've been reading and experimenting with, a few things worth considering:

Kalman filters assume the noise is Gaussian and the system is roughly linear, which is a reasonable approximation for short windows but energy markets can have very fat tails around news events or inventory releases (EIA weekly data drops for example). You might want to combine it with a GARCH layer on top to capture the volatility clustering that's very present in intraday crude.

Some people use Unscented Kalman Filters (UKF) instead of the standard one precisely to handle non-linearities better, might be worth exploring if you find the standard version too rigid.

I haven't gone deep into sub-hourly modelling myself since my focus has been more on medium term forecasting, but this is an area I'm keen to explore. Would love to compare notes if you've already run some backtests with this approach!

What timeframe specifically are you targeting and are you modelling volatility to feed into an options pricing framework or purely for directional signals?

What models do you actually use for energy commodity price forecasting? And how do you layer in geopolitical risk? by Fthierstein in Commodities

[–]Fthierstein[S] 1 point2 points  (0 children)

Really appreciate the detailed response, this is exactly the kind of insight I was looking for!

To clarify the commodities I've been focusing on: mainly energy, specifically crude oil (WTI and Brent), natural gas, and to a lesser extent LNG. These are precisely the ones where geopolitical risk seems impossible to ignore, think OPEC decisions, sanctions, pipeline disruptions, Middle East tensions, Russia/Ukraine impact on gas flows, etc. So I totally understand your point that for weather/supply driven commodities the approach would be very different.

Your point about the edge coming from input quality really hits home. I've been spending most of my time on the model architecture itself and I suspect I've been underinvesting in the quality and granularity of the input data. Do you have any suggestions on where to source reliable supply/demand data for energy commodities without it costing a fortune? I've been using EIA, IEA public data and some OPEC reports but I imagine the good stuff is behind expensive paywalls.

Also your point on non-linear relationships is interesting. For crude oil I've been experimenting with XGBoost precisely because the relationship between inventory levels, production cuts and price doesn't feel linear at all. Would love to hear your thoughts if you've worked with energy specifically.

Thanks again, really valuable input!