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Record W2597613201 · doi:10.1080/14697688.2017.1406131

Interest rate trees: extensions and applications

2018· article· en· W2597613201 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

VenueQuantitative Finance · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsShort rateInterest rateEconometricsRange (aeronautics)Short-rate modelVolatility (finance)Tree (set theory)Rendleman–Bartter modelEconomicsFlexibility (engineering)Function (biology)Interest rate derivativeArbitrageComputer scienceMathematical economicsMathematicsFinancial economicsYield curveStatisticsFinanceEngineering

Abstract

fetched live from OpenAlex

This paper provides extensions to existing procedures for representing one-factor no-arbitrage models of the short rate in the form of a tree. It allows a wide range of drift functions for the short rate to be used in conjunction with a wide range of volatility assumptions. It shows that, if the market price of risk is a function only of the short rate and time, a single tree with two sets of probabilities on branches can be used to represent rate moves in both the real-world and risk-neutral world. Examples are given to illustrate how the extensions can provide modelling flexibility when interest rates are negative.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.999

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)0.0000.001

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 head0.074
GPT teacher head0.291
Teacher spread0.216 · 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