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Record W2088894581 · doi:10.1002/asmb.936

Pricing of mountain range derivatives under a principal component stochastic volatility model

2012· article· en· W2088894581 on OpenAlexaff
Marcos Escobar, Pablo Olivares

Bibliographic record

VenueApplied Stochastic Models in Business and Industry · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStylized factStochastic volatilityEconometricsVolatility (finance)Principal component analysisVolatility smileEconomicsImplied volatilityRange (aeronautics)SABR volatility modelStochastic modellingStochastic processMathematicsComputer scienceFinancial economicsApplied mathematicsMathematical economicsStatisticsFinanceEngineeringKeynesian economics

Abstract

fetched live from OpenAlex

In this paper, a multidimensional stochastic volatility process is introduced. This process is simpler than existing ones in terms of number of parameters while keeping practical stylized facts like stochastic correlation and volatility. The pricing of two mountain range derivatives, Altavista and Everest, is analyzed under this framework, showing sensitivities to parameters, number of eigenvalues, and maturity time. Copyright © 2012 John Wiley & Sons, Ltd.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.060
GPT teacher head0.247
Teacher spread0.187 · 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.

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

Citations20
Published2012
Admission routes1
Has abstractyes

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