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用随机波动率预测日前电价

文章 arXiv papers · 作者: Andrei Renatovich Batyrov
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总结

这项研究使用随机波动率模型，对日前电力现货价格进行概率预测。研究将价格视为方差随时间变化的非平稳时间序列，并在离散时间中把波动率表示为潜在随机过程。研究首先考察一个基准随机波动率模型，随后加入若干外生回归变量对其进行扩展。

描述称，扩展模型的拟合优于基准模型，样本外预测结果也支持其适用性和稳健性。描述没有指出具体市场、回归变量、预测评估指标或改进幅度，因此无法根据所提供的摘要独立评估证据。作者认为，该模型可用于设计对冲电力交易风险的金融衍生品。这只是拟议用途；描述没有提供衍生品设计、对冲效果或预测在其他电力市场中的适用情况。

核心观点

  • 研究使用随机波动率模型，对日前电力现货价格进行概率预测。
  • 基准模型将波动率表示为随时间变化的潜在过程。
  • 据报告，加入外生回归变量后，模型拟合优于基准模型。
  • 据描述，样本外预测结果支持扩展模型的适用性和稳健性。
  • 预测结果可能有助于通过电力衍生品进行对冲，但文中没有提供对冲结果。

标签

大宗商品波动率统计学衍生品定价风险管理
全文
# Electricity Spot Prices Forecasting Using Stochastic Volatility Models


# Electricity Spot Prices Forecasting Using Stochastic Volatility Models









There are several approaches to modeling and forecasting time series as applied to prices of commodities and financial assets. One of the approaches is to model the price as a non-stationary time series process with heteroscedastic volatility (variance of price). The goal of the research is to generate probabilistic forecasts of day-ahead electricity prices in a spot marker employing stochastic volatility models. A typical stochastic volatility model - that treats the volatility as a latent stochastic process in discrete time - is explored first. Then the research focuses on enriching the baseline model by introducing several exogenous regressors. A better fitting model - as compared to the baseline model - is derived as a result of the research. Out-of-sample forecasts confirm the applicability and robustness of the enriched model. This model may be used in financial derivative instruments for hedging the risk associated with electricity trading. Keywords: Electricity spot prices forecasting, Stochastic volatility, Exogenous regressors, Autoregression, Bayesian inference, Stan

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。