Efficient estimation of Markov regime-switching models: An application to electricity wholesale market prices

In this paper we discuss the calibration issues of models built on mean-reverting processes combined with Markov switching. Due to the unobservable switching mechanism, estimation of Markov regime-switching (MRS) models requires inferring not only the model parameters but also the state process values at the same time. The situation becomes more complicated when the individual regimes are independent from each other and at least one of them exhibits temporal dependence (like mean reversion in electricity spot prices). Then the temporal latency of the dynamics in the regimes as to be taken into account. In this paper we propose a method that greatly reduces the computational burden induced by the introduction of independent regimes in MRS models. We perform a simulation study to test the efficiency of the proposed method and apply it to a sample series of wholesale electricity spot prices from the German EEX market. The proposed 3-regime MRS model fits this data well and also contains unique features that allow for useful interpretations of the price dynamics.