Parameter estimation of a new energy spot model from futures prices

Filtering and Identification of Stochastic Risk Premium for Electricity Spot Price Models

“…(3) where se(t) denotes the seasonality function. This function is determined a-priori from the historical data by using FFT as used in Aihara et al [2009], Imreizeeq [2011]. For the data shown in Fig.1, the seasonality function is given by se(t) = 0.1526 cos(2π5.5 × 10 −3 t + 2.12) +0.1641 cos(2π8.2 × 10 −3 t − 1.18) +0.1739 cos(2π16.4 × 10 −3 t + 1.36).…”

“…Setting the seasonality function is set as shown in Table -1, we simulate (13) by using the finite difference method with dx = 0.01, dt = 0.005. For details, see Imreizeeq [2011].…”

Bayesian calibration of the Schwartz-Smith Model adapted to the energy market

“…In the literature, often for simplicity, the arithmetic average is approximated by a geometric average, so that a tractable affine structure is preserved and the standard Kalman filter can be used [6]. To avoid this ad-hoc approach, recently particle filter (PF) based Bayesian parameter estimation method has been proposed [7]. However, unlike the point estimation (using particle based maximum likelihood or EM [8,9]), Bayesian parameter estimation with PF has largely remained an unresolved issue and Markov chain Monte Carlo (MCMC) still appears to be the main workhorse for the Bayesian parameter inference.…”