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Record W1934274879 · doi:10.1111/fmii.12010

On the Value of Municipal Bond Insurance: An Empirical Analysis

2013· article· en· W1934274879 on OpenAlexafffund
Van Son Lai, Xueying Zhang

Bibliographic record

VenueFinancial Markets Institutions and Instruments · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaShandong UniversityUniversité Laval
KeywordsMunicipal bondBondBond marketYield (engineering)Interest rateEconomicsIntuitionValue (mathematics)BusinessMonetary economicsFinanceStatistics

Abstract

fetched live from OpenAlex

Using a large sample of municipal bond data from 2001 to 2010 in the U.S., this paper documents the time variation of the value of municipal bond insurance, estimated from the insured and uninsured bonds yield at issue differentials. We find that insured municipal bonds carry significant lower yields at issue compared to those of the equivalent uninsured bonds before 2008. However, this cost saving disappeared with the aftermath of the subprime credit crisis. We find that the supply of bonds and the level of market interest rates to have significant positive impacts on the time‐varying value of bond insurance. We also detect asymmetric response of these yield differentials to rises and declines of market interest rates. Economic intuition suggests that the value of municipal bond insurance is a function of business cycles but our tests support the contrary, which may be explained by the habitat preference of municipal bonds issues.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.264
Teacher spread0.228 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2013
Admission routes2
Has abstractyes

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