3d (edited) • 💼 Career Corner
A common interview question: Mean Reversion
No need to know quantitative finance to answer this question.
I am attaching a presentation of 3 slides .
A very common interview question in Energy (not only energy finance) is about the electricity price and its characteristics, one of which is 'mean reversion' .
Such an interview question is when you apply for roles like the following. I also show company names.
- Energy Quantitative Analyst (Vitol, BP, Shell, Citadel, Millennium, RWE, Statkraft)
- Power / Gas Trader (Mercuria, TotalEnergies, EDF, Goldman Sachs, JP Morgan, Morgan Stanley)
- Energy Structurer / Originator (Engie, Vattenfall, Macquarie, D.E. Shaw, DRW, ExxonMobil)
- Market Risk Analyst (Uniper, Centrica, J.P. Morgan, Barclays)
- Data Scientist - Commodities (Freepoint Commodities, Castleton Commodities, Koch Industries)
If you look at a graph for the electricity price, for every hour of the year you will see them fluctuating, spiking high during sudden shortages or dropping low during excess electricity supply.
However, the price always snaps back to a central equilibrium level (mean). So it 'reverts' back to the mean.
So the price fluctuates wildly but it is always being pulled back toward its seasonal trend line.
This trend line (mean) is not a flat / horizontal line; but it also fluctuates , but slowly, throughout the year due to seasonal demand .
So in the interview question they show a plot of the electricity price. Just like the one attached.
They ask you to explain where mean reversion is and why it happens.
Click below to download the presentation .
By the way, there are many mathematical models to describe this behavior. We will see them all in Python.
The most common is called "Ornstein-Uhlenbeck" model. It is used by all quantitative analysts.
Another one , more realistic version, is the Mean-Reverting Jump Diffusion model.
You can just know the name of these models. Nothing more. We will see the code and theory later.
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A common interview question: Mean Reversion
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