使用混合正态分布将油气公司价值评估与二元性理论联系起来

S. Casault, A. Groen, J. Linton
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引用次数: 1

摘要

石油和天然气勘探和生产公司的回报曲线不容易用当前的金融理论来解释——它们的市场回报变化是非高斯分布的。在本文中,考虑了这些显著偏离预期行为的性质和潜在原因。了解金融市场行为的这些差异很重要,原因有很多,包括:评估投资、投资者关系、筹集资金的决定、评估公司和管理绩效。我们表明,在描述油气公司价值的行为时,使用两个正态分布的“厚尾”混合物比传统的高斯方法提供了一个更准确的模型。这种正态分布的混合也更有效地弥合了管理理论与实践之间的差距,而不需要引入复杂的时间敏感GARCH和/或跳跃扩散动力学。这种混合分布与二元性理论是一致的,二元性理论认为企业在两种截然不同的状态下运作,这两种状态是由企业的主要关注点驱动的:具有高不确定性的勘探状态和具有低不确定性的开采(或生产)状态。研究结果对提高实物期权定价技术的准确性和期货风险管理分析具有直接意义。传统的期权定价模型假设这些资产的商业回报是用正态随机漫步来描述的。然而,一个正常的随机游走模型忽略了诸如发现重要储量或引进新技术等事件对市场产生巨大变化的可能性。事实证明,混合分布非常适合描述与油气生产和勘探相关的异常大的风险和机会。一项对554家油气勘探和生产公司的显著性检验研究从经验上支持使用基于二元性理论的混合分布来描述这些公司的价值波动。
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Linking the Value Assessment of Oil and Gas Firms to Ambidexterity Theory Using a Mixture of Normal Distributions
Oil and gas exploration and production firms have return profiles that are not easily explained by current financial theory – the variation in their market returns is non-Gaussian. In this paper, the nature and underlying reason for these significant deviations from expected behavior are considered. Understanding these differences in financial market behavior is important for a wide range of reasons, including: assessing investments, investor relations, decisions to raise capital, assessment of firm and management performance. We show that using a “thicker tailed” mixture of two normal distributions offers a significantly more accurate model than the traditionally Gaussian approach in describing the behavior of the value of oil and gas firms. This mixture of normal distribution is also more effective in bridging the gap between management theory and practice without the need to introduce complex time-sensitive GARCH and/or jump diffusion dynamics. The mixture distribution is consistent with ambidexterity theory that suggests firms operate in two distinct states driven by the primary focus of the firm: an exploration state with high uncertainty and, an exploitation (or production) state with lower uncertainty. The findings have direct implications on improving the accuracy of real option pricing techniques and futures analysis of risk management. Traditional options pricing models assume that commercial returns from these assets are described by a normal random walk. However, a normal random walk model discounts the possibility of large changes to the marketplace from events such as the discovery of important reserves or the introduction of new technology. The mixture distribution proves to be well suited to inherently describe the unusually large risks and opportunities associated with oil and gas production and exploration. A significance testing study of 554 oil and gas exploration and production firms empirically supports using a mixture distribution grounded in ambidexterity theory to describe the value fluctuations for these firms.
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