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Record W2074834623 · doi:10.1002/fut.20353

Estimation of physical intensity models for default risk

2008· article· en· W2074834623 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

VenueJournal of Futures Markets · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsCredit riskEconometricsContext (archaeology)Mean reversionDefault riskOrnstein–Uhlenbeck processCredit spread (options)Generalized method of momentsEconomicsBondIntensity (physics)Term (time)EstimationMathematicsActuarial scienceStatisticsStochastic processFinancePanel dataGeography

Abstract

fetched live from OpenAlex

Abstract The estimation of physical intensity processes in the context of default risk is investigated here. Using data from Moody's Corporate Bond Default Database, a term structure of default probabilities for different rating classes is constructed each year from 1970 to 2001. Two specifications used for modeling the dynamics of the (risk‐neutral) intensity process in the bond‐pricing literature are then examined empirically: the Ornstein–Uhlenbeck and square‐root cases. The results reveal that the Ornstein–Uhlenbeck case is not an adequate modeling alternative with a rejection of this specification in five out of seven credit classes and nonsignificant mean reverting behavior for all credit classes. The square‐root case obtains better results with four credit classes out of seven for which this specification cannot be rejected and significant mean reversion parameters in many cases. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 29:95–113, 2009

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.300

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)0.0000.000

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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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