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Record W3123085160 · doi:10.1002/fut.20263

Target redemption notes

2007· article· en· W3123085160 on OpenAlexaff
Chi Chiu Chu, Yue Kuen Kwok

Bibliographic record

VenueJournal of Futures Markets · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLiborCouponValuation (finance)EconomicsPaymentValue (mathematics)Mathematical economicsShort rateEconometricsInversePath (computing)MathematicsActuarial scienceComputer scienceInterest rateMonetary economicsStatisticsFinanceYield curve

Abstract

fetched live from OpenAlex

Abstract The target redemption note is an index‐linked note that provides a guaranteed sum of coupons (target cap) with the possibility of early termination. In a typical structure, the coupons are calculated based on an inverse floating, London Interbank Offered Rate/Euro Interbank Offered Rate (LIBOR/Euribor) formula. Once the accumulated amount of coupons has reached the prespecified target cap, the note will be terminated with final payment of the par. The knock‐out criterion depends on a path‐dependent state variable defined by the running accumulated coupon sum. In some simplified cases, we manage to obtain a closed form valuation formula for the note value. We propose several numerical schemes for pricing the note under the one‐factor and two‐factor short rate models. Pricing behaviors of the target redemption note are also explored. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:535–554, 2007

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0440.008

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 head0.017
GPT teacher head0.236
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 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

Citations5
Published2007
Admission routes1
Has abstractyes

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