论文标题
分配建模和天然气价格预测
Distributional Modeling and Forecasting of Natural Gas Prices
论文作者
论文摘要
我们研究了建模和预测欧洲日期和预先预测天然气价格的问题。为此，我们提出了两个可用于风险和投资组合管理中的不同概率模型。我们使用从2011年到2020年的每日定价数据。广泛的描述性数据分析表明，时间序列都具有沉重的尾巴，有条件的异质性和差异表现出不对称的行为。我们建议在偏斜的，重尾的分布下提出状态空间时间序列模型，以捕获数据的所有风格化事实。其中包括自相关，季节性，风险溢价，温度，储存水平，欧洲排放津贴的价格以及相关的石油，煤炭和电力的燃油价格的影响。我们提供严格的模型诊断，并详细解释所有模型组件。此外，我们通过显着性测试进行了一项概率预测研究，并将预测性能与文献基准进行比较。与表现最佳的基准模型相比，拟议的日前（月经前）模型导致样本外连续排名概率得分（CRP）的13％（9％）降低，这主要是由于波动性和较重的尾巴的适当建模。
We examine the problem of modeling and forecasting European Day-Ahead and Month-Ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk- and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails, conditional heteroscedasticity, and show asymmetric behavior in their differences. We propose state-space time series models under skewed, heavy-tailed distributions to capture all stylized facts of the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide rigorous model diagnostics and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance tests and compare the predictive performance against literature benchmarks. The proposed Day-Ahead (Month-Ahead) model leads to a 13% (9%) reduction in out-of-sample continuous ranked probability score (CRPS) compared to the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.
