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Record W4212905002 · doi:10.1017/s0022109022000096

Synthetic Options and Implied Volatility for the Corporate Bond Market

2022· article· en· W4212905002 on OpenAlex
Steven Shu-Hsiu Chen, Hitesh Doshi, Sang Byung Seo

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Financial and Quantitative Analysis · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsnot available
FundersBanca d'ItaliaMcGill UniversityUniversity of Wisconsin-Madison
KeywordsCorporate bondVolatility (finance)BondEconomicsBond marketFinancial economicsEconometricsBond market indexBusinessMonetary economicsFinance

Abstract

fetched live from OpenAlex

Abstract We synthetically create option contracts on a corporate bond index using CDX swaptions, overcoming the limitations that stem from the lack of traded corporate bond options. Our approach allows us to estimate forward-looking moments concerning the corporate bond market in a model-free manner. By constructing an aggregate volatility measure and the associated variance risk premium, we examine the role of volatility risk in the corporate bond market. We highlight that the ex ante conditional second and higher moments we estimate from synthetic corporate bond options carry important implications for credit risk models, providing an extra basis for testing their validity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.827
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.052
GPT teacher head0.263
Teacher spread0.210 · 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