Pricing Electricity Forwards under Stochastic Volatility
http://www.erasmusenergy.com/articles/85/1/Pricing-Electricity-Forwards-under-Stochastic-Volatility/Page1.html
Philipp Kellerhals
By Philipp Kellerhals
Published on 10/22/2007
Keywords: Electricity Forwards, stochastic volatility, time-continuous model, equilibrium pricing, Kalman filtering.
Published in:
Publication year: 2001
Based on the peculiarities of electricity as underlying commodity of forward contracts we develop a time-continuous pricing model for short-term electricity forwards. The suggested stochastic volatility model utilizes the non-tradeable spot prices of electricity and its variance rate as state variables. This enables us to capture the non-linearities, and the high and time varying volatility seen in electricity prices. Using maximum likelihood estimation based on Kalman filtering we report empirical results on electricity data from the Californian market.
Published in:
Publication year: 2001
Based on the peculiarities of electricity as underlying commodity of forward contracts we develop a time-continuous pricing model for short-term electricity forwards. The suggested stochastic volatility model utilizes the non-tradeable spot prices of electricity and its variance rate as state variables. This enables us to capture the non-linearities, and the high and time varying volatility seen in electricity prices. Using maximum likelihood estimation based on Kalman filtering we report empirical results on electricity data from the Californian market.
Pricing Electricity Forwards under Stochastic Volatility
Based on the peculiarities of electricity as underlying commodity of forward contracts we develop a time-continuous pricing model for short-term electricity forwards. The suggested stochastic volatility model utilizes the non-tradeable spot prices of electricity and its variance rate as state variables. This enables us to capture the non-linearities, and the high and time varying volatility seen in electricity prices. Using maximum likelihood estimation based on Kalman filtering we report empirical results on electricity data from the Californian market.