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Record W3125676491 · doi:10.1111/eufm.12054

Higher‐moment Risk Exposures in Hedge Funds

2014· article· en· W3125676491 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

VenueEuropean Financial Management · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsHedge fundKurtosisSkewnessVolatility (finance)Financial economicsEquity (law)EconometricsAlternative betaBusinessEconomicsHedge accountingGlobal assets under managementActuarial scienceOpen-end fundInstitutional investorFinanceMathematicsStatisticsCorporate governance

Abstract

fetched live from OpenAlex

Abstract This paper singles out the key roles of US equity skewness and kurtosis in the hedge fund return generating process. We propose a conditional higher‐moment model with location, trading, and higher‐moment factors to describe the dynamics of the equity hedge, event‐driven, relative value, and fund of funds styles. If the volatility, skewness, and kurtosis implied in US options are used by fund managers as instruments to anticipate market movements, managers should adjust their market exposure in response to variations in these moments. We indeed show that higher‐moment premia improve the conditional asset pricing model across all hedge fund styles.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.002

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 head0.020
GPT teacher head0.186
Teacher spread0.165 · 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