Bayesian Calibration and Number of Jump Components in Electricity Spot Price Models
Bayesian Calibration and Number of Jump Components in Electricity Spot Price Models
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DOI：
10.1016/j.eneco.2017.04.022
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Jhonny Gonzalez;J. Moriarty;Jan Palczewski
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文献类型：
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作者：
Jhonny Gonzalez;J. Moriarty;Jan Palczewski
我们发现经验证据表明，均值回复跳跃过程在统计上不足以模拟电力现货价格尖峰，但独立的，有符号的总和，这样的过程在统计上是足够的。此外，我们证明了一个重大的经济事件后，这些款项的组成的变化。这是通过开发用于贝叶斯模型校准的马尔可夫链蒙特卡罗（MCMC）程序和模型充分性的贝叶斯评估（后验预测检查）来实现的。特别是，我们确定了APXUK和EEX市场所需的有符号均值回复跳跃组件的数量，在最近的全球金融危机之前和之后的时间段内。从统计数据来看，两个市场都发生了一致的结构性变化，后期正价格峰值的强度和规模都有所减少，甚至消失。提供所有代码和数据以实现结果的复制。
We find empirical evidence that mean-reverting jump processes are not statistically adequate to model electricity spot price spikes but independent, signed sums of such processes are statistically adequate. Further we demonstrate a change in the composition of these sums after a major economic event. This is achieved by developing a Markov Chain Monte Carlo (MCMC) procedure for Bayesian model calibration and a Bayesian assessment of model adequacy (posterior predictive checking). In particular we determine the number of signed mean-reverting jump components required in the APXUK and EEX markets, in time periods both before and after the recent global financial crises. Statistically, consistent structural changes occur across both markets, with a reduction of the intensity and size, or the disappearance, of positive price spikes in the later period. All code and data are provided to enable replication of results.