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Multi-Factor Model of Correlated Commodity - Forward Curves for Crude Oil and Shipping Markets

机译:相关商品的多因素模型 - 原油和航运市场的前向曲线

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摘要

An arbitrage free multi-factor model is developed of the correlated forward curves of the crude oil, gasoline, heating oil and tanker shipping markets. Futures contracts trading on public exchanges are used as the primary underlying securities for the development of a multi-factor Gaussian Heath-Jarrow-Morton (HJM) model for the dynamic evolution of the correlated forward curves. An intra- and inter-commodity Principal Component Analysis (PCA) is carried out in order to isolate seasonality and identify a small number of independent factors driving each commodity market. The cross-commodity correlation of the factors is estimated by a two step PCA. The factor volatilities and cross-commodity factor correlations are studied in order to identify stable parametric models, heteroskedasticity and seasonality in the factor volatilities and correlations. The model leads to explicit stochastic differential equations governing the short term and long term factors driving the price of the spot commodity under the risk neutral measure. Risk premia are absent, consistently with HJM arbitrage free framework, as they are imbedded in the factor volatilities and correlations estimated by the PCA. The use of the model is described for the pricing of derivatives written on inter- and intra-commodity futures spreads, Asian options, the valuation and hedging of energy and shipping assets, the fuel efficient navigation of shipping fleets and use in corporate risk management.
机译:针对原油,汽油,取暖油和油轮运输市场的相关正向曲线建立了无套利的多因素模型。在公共交易所买卖的期货合约被用作开发多因素高斯希思-贾罗-莫顿(HJM)模型以动态发展相关远期曲线的主要基础证券。进行商品内和商品间主成分分析(PCA)是为了隔离季节性并确定少量驱动每个商品市场的独立因素。因子的跨商品相关性通过两步PCA进行估算。为了确定稳定的参数模型,要素波动率和相关性的异方差和季节性,研究了要素波动率和跨商品因素的相关性。该模型导致显式随机微分方程,该方程控制在风险中性度量下驱动现货商品价格的短期和长期因素。与HJM无套利框架一致,没有风险溢价,因为它们被嵌入到PCA估计的因子波动率和相关性中。描述了该模型的使用,用于商品间和商品间期货价差,亚洲期权,能源和运输资产的估值和对冲,船队的燃料高效导航以及在公司风险管理中使用的衍生产品的定价。

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