On the seasonality in the implied volatility of electricity options

Abstract

Seasonality is an important topic in electricity markets, as both supply and demand are dependent on the time of the year. Clearly, the level of prices shows a seasonal behaviour, but not only this. Also, the price fluctuations are typically seasonal. In this paper, we study empirically the implied volatility of options on electricity futures, investigate whether seasonality is present and we aim at quantifying its structure. Although typically futures prices can be well described through multi-factor models including exponentially decreasing components, we do not find evidence of exponential behaviour in our data set. Generally, a simple linear shape reflects the squared volatilities very well as a curve depending on the time to maturity. Moreover, we find that the level of volatility exhibits clear seasonal patterns that depend on the delivery month of the futures. Furthermore, in an out-of-sample analysis we compare the performance of several implementations of seasonality in the one-factor framework.

Keywords:

• Implied volatility
• Electricity options
• Seasonality
• Factor models
• Settlement prices
• Season cycle

JEL Classification:

• C1
• C5
• G1

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 See e.g. the ‘Settlement pricing procedure’ on www.eex.com.

2 The RMSE represents the square root of the quadratic mean of differences, called errors, between the volatilities predicted by the model $\widehat{{\sigma }_t}$ and the empirical values ${\sigma }_t$, with variables observed from $t=1,\dots ,N$: (8) $RMSE=\sqrt{\frac{\sum _{t=1}^N(\widehat{{\sigma }_t}-{\sigma }_t{)}^2}{N}}.$(8)

3 The MAPE is the mean of the absolute deviations, as a percentage, between the volatilities predicted by the model $\widehat{{\sigma }_t}$ and the empirical values ${\sigma }_t$, with variables observed from $t=1,\dots ,N$: (11) $MAPE=\frac{100\mathrm{\%}}{N}\sum _{t=1}^N\left|\frac{{\sigma }_t-\widehat{{\sigma }_t}}{{\sigma }_t}\right|.$(11)