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Record W1841135789

Dynamic Correlations and Volatility Spillovers between Crude Oil and Stock Index Returns: The Implications for Optimal Portfolio Construction

2014· article· en· W1841135789 on OpenAlexaboutno aff
Yen‐Hsien Lee, Ya‐Ling Huang, Chun-Yu Wu

Bibliographic record

VenueDergiPark (Istanbul University) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)EconometricsEconomicsPortfolioStock (firearms)Financial economicsCrude oilStock market indexIndex (typography)Stock marketComputer sciencePetroleum engineeringBiologyGeologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT: This paper researches the portfolio construction between stock price of group of seven (G7) and West Texas Intermediate crude oil from January 2, 1998 to March 1, 2012. We investigate the volatility spillover between stock price and oil price with the dynamic conditional correlation (DCC), constant conditional correlation (CCC) and BEKK models, and also analyze their optimal hedge ratio and portfolio weights. The empirical result is that the hedge effectiveness of DCC model is better than the CCC model and BEKK models. The hedge effectiveness (HE) in Canada is the highest but Japan is the lowest. Moreover, the results show that Japan has the biggest optimal portfolio weight and the lowest hedge ratio. We do this research with expectation of providing investors information to increase the basis of investing.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.205
Teacher spread0.191 · 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 designSimulation or modeling
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

Citations18
Published2014
Admission routes1
Has abstractyes

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