Mean Reverting

Energy Pricing

Mean Reverting Processes – Energy Price Processes

Used For Derivatives Pricing & Risk Management

This is the second article in a 3-part series exploring the main stochastic price processes used to model energy spot and forward prices for derivatives valuation and risk management. The first article in this series focused on the “random walk” assumption characterised by the most popular of price processes, Geometric Brownian motion, and the BlackScholes option pricing model that is based on it. In this second article we shift our attention to the Mean Reverting Process, which incorporates the tendency of energy prices to gravitate towards a "normal" equilibrium price level that is usually governed by the cost of production and level of demand. By CARLOS BLANCO & DAVID SORONOW.

WHY DO we need to incorporate mean reversion when modelling energy prices? Suppose we observe that electricity prices jump from $30/MWh to $150/MWh due to an unexpected event (e.g. plant outages, transmission constraints, heat wave, etc.). Most market practitioners would agree that it is highly probable that prices will eventually return to their average level once the cause of the jump goes away. For similar reasons if the price of a barrel of WTI falls to US$7 due to overproduction we would expect the price to eventually rise as producers decrease supply. These expectations are intuitive in nature and are supported by our observations of energy spot price behaviour. These two simple examples illustrate the limitations of Geometric Brownian motion (GBM) when applied to energy prices. In the electricity example above GBM would accept the US$150/MWh price as a normal event and would proceed randomly from there (via a continuous diffusion process) with no consideration of prior price levels (no memory), and no greater probability of returning to the average price level. This result is clearly at odds with market reality, and provides the impetus for the modelling of more...