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电力市场随机风险溢价的定价测度

文章 arXiv papers · 作者: Fred Espen Benth et al.
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总结

本文为现货价格均值回归且存在剧烈价格尖峰的电力市场构建定价测度。其双因子模型用布朗运动驱动的 Ornstein–Uhlenbeck 过程表示较小波动，并用由纯跳 Lévy 过程驱动的第二个均值回归过程表示尖峰。作者将该方法与 Esscher 变换进行比较：后者保留驱动过程的概率结构，但可能产生符号确定性变化的随机风险溢价。

对 Esscher 变换的扩展可以减缓均值回归，并生成符号随机变化的随机风险溢价，包括在算术现货模型中。这使得风险结构可以呈现短期限正远期溢价和长期限负远期溢价。该测度也能保留平稳的现货动态，同时允许远期价格随机波动。本文介绍模型与定价性质，但没有提供实证校准、表现比较，或某种特定测度最符合观测市场价格的证据。

核心观点

  • 电力现货价格用两个均值回归因子建模，分别表示常规波动和价格尖峰。
  • 尖峰部分由纯跳 Lévy 过程驱动。
  • Esscher 变换的扩展改变均值回归行为，并允许随机风险溢价符号变化。
  • 模型可以表示远期曲线短端溢价为正、长端溢价为负的情况。
  • 平稳的现货动态可以与远期合约价格波动并存。

标签

大宗商品期货衍生品定价均值回归波动率
全文
# A pricing measure to explain the risk premium in power markets


# A pricing measure to explain the risk premium in power markets









In electricity markets, it is sensible to use a two-factor model with mean reversion for spot prices. One of the factors is an Ornstein-Uhlenbeck (OU) process driven by a Brownian motion and accounts for the small variations. The other factor is an OU process driven by a pure jump Lévy process and models the characteristic spikes observed in such markets. When it comes to pricing, a popular choice of pricing measure is given by the Esscher transform that preserves the probabilistic structure of the driving Lévy processes, while changing the levels of mean reversion. Using this choice one can generate stochastic risk premiums (in geometric spot models) but with (deterministically) changing sign. In this paper we introduce a pricing change of measure, which is an extension of the Esscher transform. With this new change of measure we also can slow down the speed of mean reversion and generate stochastic risk premiums with stochastic non constant sign, even in arithmetic spot models. In particular, we can generate risk profiles with positive values in the short end of the forward curve and negative values in the long end. Finally, our pricing measure allows us to have a stationary spot dynamics while still having randomly fluctuating forward prices for contracts far from maturity.

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。