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Record W3124128148

A Semiparametric Two-Factor Term Structure Model

2008· article· en· W3124128148 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsBank of CanadaWestern University
Fundersnot available
KeywordsEstimatorNonparametric statisticsDiscretizationTerm (time)Applied mathematicsDiffusionMathematicsFunction (biology)Short rateMathematical optimizationEconometricsYield curveStatisticsPhysicsMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

This article proposes a semiparametric two-factor term structure model based on a consol rate and the spread between a short rate and the consol rate. The diffusion functions in both the consol rate and spread processes are nonparametrically specified so that the model allows for maximal flexibility of diffusion functions in fitting into data. The drift function of the spread process is specified as a mean-reverting function, while the drift function of the consol rate process is left unrestricted. A nonparametric procedure is developed for estimating the diffusion functions. The asymptotic biases of the nonparametric estimators are quantified when the step of discretization is fixed, while the asymptotic distributions of the nonparametric estimators are derived when the step of discretization tends to zero. The pricing and hedging performances of the model are evaluated in a simulated economic environment. Results show that the model performs quite well in the simulated economy.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.120
GPT teacher head0.333
Teacher spread0.213 · 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