Natural gas storage valuation via least squares Monte Carlo and support vector regression
Natural gas storage valuation via least squares Monte Carlo and support vector regression
复制标题
通过最小二乘蒙特卡罗和支持向量回归进行天然气储存评估
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. Trafalis
中科院分区：
文献类型：
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作者：
A. Malyscheff;T. Trafalis
最小二乘蒙特卡罗（LSMC）方法是一种计算效率高的天然气储存设施的评估方法。LSMC方法在计算上易于处理，同时允许将价格路径模拟与决策向量的优化解耦。然而，使用传统回归技术选择适当的特征可能具有挑战性，特别是当假设几个不确定因素驱动价格过程时。在本文中，我们使用参数可以轻松校准的双因素远期模型来分析天然气储存合同。对于从2004年至2009年NBP日前合约的月平均值得出的远期曲线，我们基于现货价格路径和到期时间为30天的每日远期合约的价格路径的集合计算存储值。我们研究的影响，额外的定价信息的形式，一个天然气储存设施的价值的远期合同。与相应的单因素模型的比较也包括在我们的实验中。值函数近似是通过采用基于核的回归技术的支持向量机回归（SVR）的形式进行的。我们通过模拟下一阶段的目标来报告样本外结果。我们还进行了搜索空间的SVR参数，以确定适当的参数，我们的实验。应用现货交易策略，我们观察到一个更高的存储值的一个因素模型相比，相应的两个因素模型。关于两个因素的模型，我们报告说，一个近似的价值函数在现货和远期合约增加存储价值相比，价值函数是计算在现货合约。
Least squares Monte Carlo (LSMC) approaches represent a computationally efficient method for the valuation of natural gas storage facilities. LSMC methods are computationally tractable while they simultaneously allow for a decoupling of the price path simulation from the optimization of the decision vector. However, selecting the appropriate features using traditional regression techniques can be challenging, particularly when several factors of uncertainty are assumed to drive the price process. In this paper we analyze a natural gas storage contract using a two factor forward model whose parameters can be easily calibrated. For a forward curve derived from monthly averages of the NBP day-ahead contract from 2004 to 2009 we compute storage values based on a collection of spot price paths and price paths of a daily forward contract with a time to maturity of 30 days. We study the impact of additional pricing information in the form of a forward contract on the value of a gas storage facility. A comparison to the corresponding one factor model is also included in our experiments. Value function approximation is carried out by employing a kernel-based regression technique in the form of support vector machine regression (SVR). We report out-of-sample results by simulating the targets for the next stage. We also carry out a search in the space of SVR parameters to identify the appropriate parameters for our experiments. Applying a spot trading strategy we observe a higher storage value for the one factor model when compared to the corresponding two factor model. With respect to the two factor model we report that an approximation of the value function over both a spot and a forward contract increases storage value compared to a value function that is computed over a spot contract only.