  Based on empirical evidence of fast mean-reverting spikes, we model
electricity price processes $X+Z^\beta$ as the sum of a continuous It\^o
semimartingale $X$ and a a mean-reverting compound Poisson process $Z_t^\beta =
\int_0^t \int_{\mathbb{R}} xe^{-\beta(t-s)}\underline{p}(ds,dt)$ where
$\underline{p}(ds,dt)$ is Poisson random measure with intensity $\lambda
ds\otimes dt$. In a first part, we investigate the estimation of
$(\lambda,\beta)$ from discrete observations and establish asymptotic
efficiency in various asymptotic settings. In a second part, we discuss the use
of our inference results for correcting the value of forward contracts on
electricity markets in presence of spikes. We implement our method on real data
in the French, Greman and Australian market over 2015 and 2016 and show in
particular the effect of spike modelling on the valuation of certain strip
options. In particular, we show that some out-of-the-money options have a
significant value if we incorporate spikes in our modelling, while having a
value close to $0$ otherwise.
