Neural Network Derivatives Pricing for Electricity Commodity Markets

电力商品市场的神经网络衍生品定价

• 批准号: 9908086
• 负责人: Oluseyi Olurotimi
• 金额: $ 8万
• 依托单位: Adaptive Data Engines, L.L.C.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9908086&HistoricalAwards=false
• 关键词: Neural; Network; Derivatives; Pricing; Electricity

9908086OlurotimiThis research project will test a recurrent neural network approach to pricing electricity commodity derivatives. The method is based is based on the ability of dynamic recurrent neural networks to approximate properly parameterized arbitrary functionals. The network explicitly estimates financial derivative valuations. The underlying asset spot price process is not explicitly replicated, but is implicit in the estimation. This approach therefore overcomes the problems in many other approaches that focus on modeling the underlying price process. Namely, derivative pricing equations explicitly based on the underlying process for exotic contracts such as electricity are rarely found in closed form. Virtually all known real time methods for valuing electricity derivatives are extremely computationally expensive. The approach here relegates the computational expense to offline design, and should be able to generate online derivative valuations in dramatically less time than is usually done. The approach is conceptually similar to recent transform based approaches that also avoid explicit estimation of the underlying price process, but compute the derivative prices implicitly through substitutions in the transform domain. However, the desired analytical expressions do not readily emerge in those transform approaches either, leading to very high computational expense in order to implement them. The design approach presented in this proposal is completely rigorous, while admitting a computational cost barely more than typical offline neural network training. Analytical measures of performance based on recent results in stochastic recurrent neural networks are used to iteratively optimize the design in this offline stage.Descriptive statistics are extracted from electricity spot price data series, including parameters related to jumps, price spikes and process regime-switching. These quantities are used to parameterize the functional operator that generates the derivative asset prices. This approach is consistent with those few and limited cases where closed-form derivative pricing formulas have been obtained. The recurrent neural network is then trained to generate the derivative asset price functionals using an input vector that consists of a subvector of the recurrent neural network state feedback, and a subvector of the exogenous (to the recurrent neural network ) parameters. The dynamic neural network approach is suited for this application because the ideal computation of, for example, futures or forward derivative contracts at a given time requires some autonomous form of estimating quantities between the given date and the derivative maturation date.The training data will be selected from actual electricity price series, and the trained recurrent neural network output will be compared with actual derivative (e.g. forward) prices.

9908086Olurotimi 该研究项目将测试循环神经网络方法对电力商品衍生品进行定价。该方法基于动态循环神经网络近似正确参数化的任意函数的能力。该网络明确估计金融衍生品估值。标的资产现货价格过程并未明确复制，而是隐含在估计中。因此，这种方法克服了许多其他专注于对基础价格过程进行建模的方法中的问题。也就是说，明确基于电力等奇异合同的基本过程的衍生品定价方程很少以封闭形式存在。事实上，所有已知的用于评估电力导数的实时方法在计算上都极其昂贵。这里的方法将计算费用归咎于离线设计，并且应该能够在比通常完成的时间少得多的时间内生成在线衍生品估值。该方法在概念上类似于最近的基于变换的方法，这些方法也避免了对基础价格过程的显式估计，而是通过变换域中的替换隐式地计算衍生品价格。然而，所需的解析表达式也不容易出现在这些变换方法中，导致实现它们的计算费用非常高。该提案中提出的设计方法是完全严格的，同时承认计算成本仅比典型的离线神经网络训练高。基于随机循环神经网络最新结果的性能分析测量用于迭代优化该离线阶段的设计。从电力现货价格数据系列中提取描述性统计数据，包括与跳跃、价格尖峰和过程状态切换相关的参数。这些数量用于参数化生成衍生资产价格的函数运算符。这种方法与那些已经获得封闭式衍生品定价公式的少数和有限的情况是一致的。然后，训练循环神经网络以使用输入向量生成衍生资产价格函数，该输入向量由循环神经网络状态反馈的子向量和外生（对于循环神经网络）参数的子向量组成。动态神经网络方法适合这种应用，因为给定时间的期货或远期衍生品合约的理想计算需要在给定日期和衍生品到期日期之间进行某种自主形式的数量估计。训练数据将从实际电价序列中选择，训练后的循环神经网络输出将与实际衍生品（例如远期）价格进行比较。