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Record W1983047083 · doi:10.2118/84236-ms

Effects of Input Price Uncertainty on Asset Valuation

2003· article· en· W1983047083 on OpenAlexaff
David G. Laughton

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDiscounted cash flowCash flowEconomicsEconometricsValuation (finance)MicroeconomicsUnit priceAsset (computer security)Computer scienceFinance

Abstract

fetched live from OpenAlex

Abstract Most future input prices (unit costs) in the upstream petroleum industry are not known with certainty. This can have important effects on asset value and management. One criticism of most past petroleum industry applications of real options analysis (ROA) is that they have neglected to consider the effects of this type of uncertainty. This paper explores a few of the issues that arise in discounted cash-flow (DCF) and ROA asset valuations, if a specific type of input price uncertainty is considered. In particular, we look at the effects of the correlation of unit costs with output petroleum prices. Unit drilling costs, for example, tend to be high/low if petroleum prices are high/low or have gone up/down unexpectedly. The first "rent" effect occurs if the market for drilling services is not completely competitive. The second "quasi-rent" effect occurs if it takes time for the suppliers of these services to adjust the amounts they supply in the face of unexpected changes in demand. We first examine, using both ROA and DCF methods of analysis, some relatively simple assets where the asset cash-flow dependence on input and output prices, and of the input prices on the output prices, is linear. Two points are made. First, for a set of given input price expectations, DCF estimates of the value of these assets will be independent of the level of input price uncertainty, unless the discount rate is adjusted to reflect the change in risk. ROA estimates of value will automatically pick up the effects of different levels of risk. If the unit costs are generally correlated with the macro-economy (as will be the case with a rent effect and most quasirent effects, if the output prices are so correlated), for a set of given input price expectations, a greater level of input price uncertainty would decrease the estimate of the value of the costs in the asset cash-flows, because of the greater risk discounting that it induces. This would increase the ROA estimate of the value of the asset as a whole. Second, it has been conjectured that a greater similarity between revenue and cost uncertainty, by making revenue and cost discounting more similar in ROA analyses, will tend to mitigate some of the differences between the structure of ROA and DCF value estimates. This would decrease the benefits of a shift from the use of DCF methods of analysis to the use of ROA. We analyse variations of two previously published examples of analyses of assets with linear cash-flows to explore these matters. We then examine an asset where there are non-linearities in the dependence of asset cash-flows on prices. These may be due to non-linear price models, non-linear taxes or flexibility in project management. In the specific asset we analyse, which is also based on a previously published example, the non-linearities arise from flexibility. In this case, we find some similarities and some differences in structure of the effects of input price uncertainty on asset value. The example is of an oil field in the final year of its development lease, where there is an option to sanction development immediately, or to appraise or wait for another year and decide then between development or abandonment. At sanction, there is a production capacity choice to be made, and after sanction there is an annual abandonment option. Again, two points are made. First, if the input price expectations are roughly the same, input price uncertainty of the simplest kind (stemming from a linear "rent" effect) again increases ROA estimates of asset value and leaves the DCF value estimates roughly the same. Second, rather than mitigating the differences between DCF methods of analysis and ROA, cost uncertainty in this example accentuates the losses from using DCF methods if ROA should be used. Unit cost uncertainty interacts with the differences in the methods of value estimation to make the management policies suggested by DCF and ROA more different in the presence of this uncertainty. All of this suggests that the industry should keep track of the uncertainty in its input prices, and their correlation with output petroleum prices and with each other. This paper shows how this would be useful for project analysis. It would also be useful in the development of appropriate risk management policies.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.247
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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