Neural Network Derivatives Pricing for Electricity Commodity Markets

9908086OlurotimiThis研究项目将测试一种循环神经网络方法来为电力商品衍生品定价。该方法是基于动态递归神经网络的能力，适当地近似参数化的任意泛函。该网络明确估计金融衍生品估值。标的资产现货价格过程没有明确复制，但隐含在估计中。因此，这种方法克服了许多其他方法中的问题，这些方法专注于对基础价格过程进行建模。也就是说，衍生品定价方程明确的基础上的异国情调的合同，如电力的基本过程很少发现在封闭的形式。实际上，所有已知的用于评估电力衍生物的真实的时间方法在计算上是极其昂贵的。这里的方法将计算费用转移到离线设计中，并且应该能够在比通常更短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，也避免了对基础价格过程的显式估计，但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，导致非常高的计算费用，以实现它们。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高于典型的离线神经网络训练。基于随机递归神经网络的最新研究成果，分析了模型的性能指标，并从电力现货价格数据序列中提取描述性统计量，包括与跳变、价格尖峰和过程状态切换相关的参数，用于迭代优化离线阶段的设计。这些量用于参数化生成衍生资产价格的函数运算符。这种方法是符合那些少数和有限的情况下，封闭形式的衍生品定价公式已获得。然后训练递归神经网络以使用输入向量生成衍生资产价格泛函，该输入向量由递归神经网络状态反馈的子向量和（递归神经网络的）外生参数的子向量组成。动态神经网络方法适合于这种应用，因为例如在给定时间的期货或远期衍生品合约的理想计算需要估计给定日期和衍生品成熟日期之间的量的某种自治形式。并且将训练的循环神经网络输出与实际的衍生（例如远期）价格进行比较。

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.