The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is applied to one‐ and two‐factor stochastic models, using both simulated and real data. The results demonstrate good agreement between the maximum likelihood and MCMC point estimates. The latter approach, however, provides a more complete characterization of the model uncertainty, an information that can be exploited to obtain a more realistic assessment of the forecasting error. In order to further validate the MCMC approach, the posterior distribution of the Italian electricity price volatility is explored for different maturities and compared with the corresponding maximum likelihood estimates.

中文翻译：

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电力市场中现货价格模型的MCMC校准

研究了用于模拟电力市场​​现货价格的一些随机微分方程的标定。作为依赖标准似然最大化的替代方法，探索了采用完全贝叶斯范式的方法，该方法依赖于马尔可夫链蒙特卡洛（MCMC）随机模拟，并提供模型参数的后验分布。该方法适用于一因素和二因素随机模型，同时使用了模拟数据和实际数据。结果表明，最大似然与MCMC点估计值之间具有良好的一致性。但是，后一种方法提供了模型不确定性的更完整特征，可以利用该信息来获得对预测误差的更实际评估。为了进一步验证MCMC方法，