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Record W3122662712 · doi:10.1142/s0219024917500443

IRREVERSIBLE INVESTMENTS AND AMBIGUITY AVERSION

2017· article· en· W3122662712 on OpenAlexafffund
Álvaro Cartea, Sebastian Jaimungal

Bibliographic record

VenueInternational Journal of Theoretical and Applied Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaUniversity of the Sunshine Coast
KeywordsAmbiguity aversionAmbiguityEconomicsValuation (finance)MicroeconomicsComplementarity (molecular biology)Mathematical economicsEconometricsIncomplete marketsRisk aversion (psychology)Complete marketExpected utility hypothesisComputer scienceFinance

Abstract

fetched live from OpenAlex

Real option valuation has traditionally been concerned with investment under project value uncertainty while assuming that the agent has perfect confidence in a specific model. However, agents do not generally have perfect confidence in their model and this ambiguity may affect their decisions. In addition, the value of real investments is not typically fully spanned by tradable assets because markets are incomplete as is typically the case in energy and commodities. In this paper, we account for the agent’s aversion to model ambiguity and address market incompleteness through the notion of robust indifference prices. We derive analytical results for the perpetual option to invest and the linear complementarity problem that the finite-time version of this problem satisfies. Ambiguity aversion has a number of effects on decision making some of which cannot be explained by altering the agent’s risk aversion. For example, ambiguity averse agents are found to exercise real options both earlier and later than their ambiguity neutral counterparts, depending on whether ambiguity stems from uncertainty in the dynamics of the project value or the dynamics of a hedging asset.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 score0.038
Threshold uncertainty score0.279

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 head0.015
GPT teacher head0.232
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

Citations14
Published2017
Admission routes2
Has abstractyes

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