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Record W3109318473 · doi:10.1080/21642583.2020.1851804

Semi-analytic pricing formulas for basket credit-linked notes with and without counterparty risks

2020· article· en· W3109318473 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

VenueSystems Science & Control Engineering · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsWilfrid Laurier University
FundersNational Natural Science Foundation of China
KeywordsCredit riskValuation (finance)CounterpartyCredit valuation adjustmentMathematicsEconometricsActuarial scienceEconomicsMathematical economicsApplied mathematicsFinance

Abstract

fetched live from OpenAlex

This paper discusses the pricing of basket credit linked notes (BCLN) under the reduced model. Three types of BCLNs are discussed: the first, the second and the mth-to-default BCLNs with and without counterparty risks. Based on conditional independence, the joint distribution of default times can be calculated and thus the price problems are transformed into partial differential equation (PDE) forms. Then the semi-analytical pricing formulas for the above BCLNs are obtained with PDE method and the calculation of credit valuation adjustments are also obtained. Finally, numerical test results show the high precision of the derived pricing formulas.

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 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 score0.698
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.039
GPT teacher head0.237
Teacher spread0.197 · 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