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Record W2000049960 · doi:10.5539/ijef.v2n4p102

An Integral Equation Method with High-Order Collocation Implementations for Pricing American Put Options

2010· article· en· W2000049960 on OpenAlexvenueno aff
Jingtang Ma, Kaili Xiang, Yingjun Jiang

Bibliographic record

VenueInternational Journal of Economics and Finance · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsnot available
FundersProject 211Southwestern University of Finance and Economics
KeywordsCollocation (remote sensing)Boundary (topology)Volterra integral equationOrder (exchange)Polygon meshCollocation methodMathematicsBlack–Scholes modelIntegral equationSingular boundary methodApplied mathematicsMathematical optimizationComputer scienceMathematical analysisBoundary element methodDifferential equationEconomicsFinanceOrdinary differential equationEconometricsGeometryPhysics

Abstract

fetched live from OpenAlex

The aim of this paper is to solve a free boundary problem arising in pricing American put options. It is known that the free boundary (optimal exercise boundary) satisfies a “nonstandard” Volterra integral equation. This Volterra integral equation is resolved by a high-order collocation method based on graded meshes. With the computed free boundary, a Black-Scholes equation for pricing the American put options is solved by a moving mesh method. Numerical examples are provided to confirm the efficiency of the approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.301
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations9
Published2010
Admission routes1
Has abstractyes

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