MétaCan
Menu
Back to cohort
Record W2884923875 · doi:10.5539/ibr.v11n8p66

The Spillover and Transmission of Chinese Financial Markets Risk

2018· article· en· W2884923875 on OpenAlexvenueno aff
Sha Zhu

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsSpillover effectFinancial crisisFinancial marketStock marketIndex (typography)Financial riskChinaVolatility (finance)Stock market indexBusinessEconomicsFinancial systemMonetary economicsFinanceMacroeconomics

Abstract

fetched live from OpenAlex

After the 2008 financial crisis, the whole world financial markets became more fluctuates, the same to China also. It is necessary to pay great attention to high volatility problem in Chinese market, and also the uncertainty problem, risk accumulation and spillover effect come along with it. This paper calculates stock market return and builds financial stress index to explore the risk spillover effect. Empirical results show that the Chinese financial market have higher volatility than other countries. The Chinese stock market had higher dynamic market co-movement with international financial markets after 2008 financial crisis. What’s more, this article also finds the financial risk spreads between China and US. When the US financial stress index increases, China's financial stress index experiences a larger increase. However, after the change in China's financial stress index, the US financial stress index has no obvious trend of change. So we should pay more attention to periods of Chinese financial market risk and its spillover.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.082
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.044
GPT teacher head0.321
Teacher spread0.276 · 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 teacher head, 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicFinancial Risk and Volatility ModelingFrench-language works237,207