A regime switching long memory model for electricity prices
电价的状态转换长记忆模型AI 解读 ↓
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电价的状态转换长记忆模型
- 研究问题
- 如何同时刻画电力现货价格的状态转换与状态内长记忆，并解释输电拥堵下不同地区价格关系和相对价格预测的变化？
- 方法与思路
- 作者建立马尔可夫状态转换分数单整模型，用最大似然估计各状态的分数单整阶数，并应用北欧电力市场数据开展蒙特卡洛预测。
- 材料陈述的贡献
- 摘要称状态转换与长记忆在该应用中可同时具有实证意义，单个市场价格行为随输电瓶颈变化，模型对相对价格预测尤其有吸引力。
- 阅读关注点
- 核对状态转换与分数单整的联合定义、似然计算、状态间价格相同所对应的协整关系，以及相对价格预测的基准和评价方案。
- 阅读边界
- 依据所给作者／机构库版本摘要，不能推断刊本变化；摘要未说明样本区间、估计条件或预测改善数值，结论不能直接推广至其他电力市场。
原文摘要
In this paper we develop a regime switching model which can generate long memory (fractional integration) in each of the regime states. This property is relevant in a number of cases. For instance, the deregulated market for electricity power in the Nordic countries is characterized by electricity spot prices with a high degree of long memory. It occurs that in some time periods bilateral prices are identical whereas in other periods the prices differ. The latter occurs when a capacity congestion exists across regions and multiple price areas will result. If the price series are fractionally integrated this means that in some regimes, an extreme form of fractional cointegration amongst prices will exist. We define a Markov switching fractional integration model from which the fractional orders of integration in separate states can be estimated using Maximum Likelihood techniques. The model is adapted to data for the Nordic electricity spot market, and we find that regime swithing and long memory are empirically relevant to co-exist. In particular, we find that the price behaviour for single markets can be very different depending upon the presence or absence of bottlenecks in electricity transmission. Using Monte Carlo forecasting we find that the regime switching model appears to be especially attractive in forecasting relative prices.