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Record W2626418900 · doi:10.16980/jitc.12.4.201608.333

Estimation of Volatility in International Business Cycles in Korea and Selected Developed Countries under the Financial Crises

2016· article· en· W2626418900 on OpenAlexaboutno aff
Kyung-Hee Lee, Kyung-Soo Kim

Bibliographic record

VenueKorea International Trade Research Institute · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)EconomicsFinancial crisisBusiness cycleStructural breakMonetary economicsEconometricsMacroeconomics

Abstract

fetched live from OpenAlex

This study examined the structural changes in volatility in the international business cycles using the real GDP during financial crises within the 1971-2016 period. The coefficients did not appear to reflect unstable structural changes during a financial crisis as seen in Chow tests. On the other hand, the 1-Step tests showed stable and/or unstable coefficients except in Canada and Korea, but these were persistently stable in the N-Step tests except for Germany and Japan where such coefficients were unstable. In the CUSUM tests, the coefficients were stable over the periods. The CUSUMSQ tests showed stable coefficients in the country except Korea and UK. Korea exhibited higher regime switching volatility probability which suggests that significant changes in SWARCH model may have to be made. Transition probability showed the highest duration in the low regime in Canada. The MS-VAR models demonstrated significant simultaneous structural changes in volatility in international economic cycles as a result of the market crash. Korea was significantly affected and transmitted by the real GDP of the countries through the Asia and Global financial crisis. Therefore, we found structural changes in volatility in the international business cycles as a result of simultaneous symmetric or asymmetric shocks between Korea and major developed countries and we confirmed that the high volatility (contraction) regime coincided with the financial crises.

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)0.003
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 score0.401
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.119
GPT teacher head0.342
Teacher spread0.223 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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