What do market-calibrated stochastic processes indicate about the long-term price of crude oil?

Hahn, Warren J.; DiLellio, James; Dyer, James S.

doi:10.1016/j.eneco.2014.04.007

Cited by 15 publications

(5 citation statements)

References 30 publications

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“…4 Option is a financial derivative that gives the buyer the right, but not the obligation, to buy or sell a pre-determined asset. 5 Futures with expiration dates extending 8 years out on 19 of October 2016, plus call options for 1/12, 3/12, and 8 years were used for calibration found in Davis, 2012;Jafarizadeh and Bratvold, 2012Ozorio et al, 2013;Hahn et al, 2014;Bratvold, 2015, 2017. …”

Section: Oil Price Modelmentioning

confidence: 99%

Offshore Exploratory Drilling Campaigns During Oil Price Downturns: Maximizing Value Creation from Flexibility

Cardenas

Bratvold

Jafarizadeh

2018

Day 3 Wed, June 13, 2018

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During oil price downturns, many operating companies reduce or eliminate large investments with long time horizons such as exploratory drilling campaigns. This reduction in investments induces rig and drilling services providers to decrease their bids to remain competitive. Consequently, the exploration expense is decreased, lessening the initial expenditure in the project. In this research, a valuation approach is implemented to study the impact of this investment reduction on the decision-making process for executing exploratory drilling campaigns during oil price downturns. It is shown that postponing exploration campaigns during oil downturns does not necessary maximize value creation.Value creation from investment in oil price downturns results from the combination of uncertainty and flexibility. Value of flexibility (optionality), also referred to as Real Options value, can be estimated using a variety of methods. In this work, we use the versatile Least-Square Monte Carlo Method (LSM) approach developed by Longstaff and Schwartz (2001) to evaluate the waiting option for exploration drilling. Uncertainties in oil prices and drilling costs are included as they have the largest impact on the alternative chosen and the value achieved. We implement a two-factor stochastic oil price process developed by Schwartz and Smith (2000), as this model provides a good balance between realism and ease of communication. Uncertainty in the drilling costs is modeled as a Geometric Brownian Motion process. We also account for dependencies between oil price and costs by correlating the drilling cost with the previous period's oil prices.We show that the real option methodology will identify the optimal time to start exploration drilling. We also demonstrate the effect of correlation between the drilling cost and the oil price on the optimal time to drill; which for this study is the year with lowest expected oil price. Furthermore, we analyze the sensitivity of project value with respect to the correlation factor and the parameters in the stochastic price model. This work demonstrates that including price-cost uncertainties and correlations leads to more realistic value estimates, resulting in investment decisions that maximize value. This paper contributes to the practice of petroleum project analysis by showing the effect of correlations on the optimal time of drilling and the value of waiting options, demonstrating that it could be optimal to drill exploration wells during oil price downturns. The real option model developed in this paper is applicable to many types of exploration projects in the petroleum industry.

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Section: Oil Price Modelmentioning

confidence: 99%

Offshore Exploratory Drilling Campaigns During Oil Price Downturns: Maximizing Value Creation from Flexibility

Cardenas

Bratvold

Jafarizadeh

2018

Day 3 Wed, June 13, 2018

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“…An example of the latter model for forecasting oil prices is provided by Hahn et al. (2014), with an illustration of its use to estimate real option values for non‐conventional natural gas projects.…”

Section: Introductionmentioning

confidence: 99%

“…Another alternative would be to use the historical and market data to fit a popular commodity price process such as the two-factor mean reverting model of Schwartz and Smith (2000). An example of the latter model for forecasting oil prices is provided by Hahn et al (2014), with an illustration of its use to estimate real option values for non-conventional natural gas projects.…”

Section: Introductionmentioning

confidence: 99%

Valuing Real Options in the Volatile Real World

Harikae¹,

Dyer

Wang

2021

Production and Operations Management

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Motivated by the real‐world challenges of real options evaluation faced by many companies when commodity prices exhibit dramatic volatility and project values can become negative, this study presents a framework for solving a multifactor real options problem by approximating the underlying stochastic process of project value with a generalized implied binomial tree. The proposed approach allows a flexible structure for stochastic processes with fat tail distributions, such as jump diffusion, regime switch or mean reversion and provides a more accurate estimate of the extreme downside risk by allowing negative values for the underlying project values. Our illustrative example shows that the value of a real option estimated by the proposed approach is more accurate and stable than the alternative lattice‐based approaches in the literature under a wide variety of underlying commodity process, which makes this a more robust approach for valuing complex real options under multiple sources of uncertainty in the volatile real world.

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“…Models (2) and(3) also have been widely used in the equity mutual fund (Kellerhals and Schobel, 2002), commodity (Schwartz, 1997;Schwartz and Smith, 2000;Manoliu and Tompaidis, 2002), interest rate and volatility modeling (Windcliff et al 2006;Detemple and Osakwe, 2000;Pan, 2002), and energy price modeling (Hahn et al 2014;Kobari et al 2014;Meade, 2010;Pindyck, 2001;1999) literature. However, it has been discovered that the interest rate and does not fully fulfill the assumption of classical mean reverting Brownian motion, since non-Markovian and long memory features be characterized.…”

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confidence: 99%

The Behavior Comparison between Mean Reversion and Jump Diffusion of CDS Spread

Kim¹,

Park²

2015

EJEF

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This paper empirically investigated the behavior of Korean CDS spread which captures the excess kurtosis and heavier tails (i.e. leptokurtic behavior). In capturing the dynamics of the Korean CDS spread, this study notably focuses on the comparison of mean reverting drifts and jump part of the continuous-time models of CDS spread. The results are as follows. First, Empirical findings indicate that the addition of jumps leads to a lower expected return and volatility. This result implies that jumps account for a substantial portion of the overall volatility of the return data. Second, During Pre and Post Crisis period the GBM is better than competing models in terms of parameter significance, log-likelihood and the BIC. Third, the addition of jumps improves performance significantly since all jump diffusion processes outperform their diffusion counterparts especially during the crisis period. Finally, the addition of mean-reversion appears to increase the goodness-of-fit, especially in the case of the jump-diffusion models during the crisis period.

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What do market-calibrated stochastic processes indicate about the long-term price of crude oil?

Cited by 15 publications

References 30 publications

Offshore Exploratory Drilling Campaigns During Oil Price Downturns: Maximizing Value Creation from Flexibility

Offshore Exploratory Drilling Campaigns During Oil Price Downturns: Maximizing Value Creation from Flexibility

Valuing Real Options in the Volatile Real World

The Behavior Comparison between Mean Reversion and Jump Diffusion of CDS Spread

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