Energy-Switching Using Lévy Processes - An Application to Canadian and North American Data

Alexis Arrigoni, Wei-Liang Lu, A. Swishchuk, Stéphane Goutte
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引用次数: 2

Abstract

The Paris agreement in 2016 marks a global effort to limit the increase in temperature. In that spirit, the Federal Government of Canada introduced a carbon tax to reduce greenhouse gas emissions. The main goal of this paper is to define the correct approach to carbon pricing. Following the method, introduce by Goutte and Chevalier (2015), we define the carbon price as the necessary tax to incite electricity producers to switch from coal to natural gas. The novelty of this paper is that we use this method for Alberta and North America. In addition, we consider the case of switching from natural gas to wind as a potential new approach to carbon pricing. After reviewing the two methods, we model prices under three stochastic procedures: Levy Normal Inverse Gaussian (NIG), Levy Normal and Heston model. Finally, we generalize our empirical technique to oil, natural gas and coal individually. The main finding of this article is that the Levy NIG outperforms the Levy Normal and Heston as it is able to take into account the jumpy and volatile nature of energy prices.
使用lsamvy过程的能量转换-加拿大和北美数据的应用
2016年的《巴黎协定》标志着全球限制气温上升的努力。本着这种精神,加拿大联邦政府实行了碳税,以减少温室气体排放。本文的主要目标是定义正确的碳定价方法。根据Goutte和Chevalier(2015)提出的方法,我们将碳价定义为激励电力生产商从煤炭转向天然气的必要税收。本文的新颖之处在于,我们对阿尔伯塔省和北美使用了这种方法。此外,我们认为从天然气转向风能是一种潜在的碳定价新方法。在回顾了这两种方法之后,我们在三种随机过程下建立了价格模型:Levy正态反高斯模型(NIG)、Levy正态模型和Heston模型。最后,将经验技术分别推广到石油、天然气和煤炭。本文的主要发现是,Levy NIG优于Levy Normal和Heston,因为它能够考虑到能源价格的跳跃和波动性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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