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Record W4225643522

Time-varying volatility spillover of foreign exchange rate in three Asian markets: Based on DCC-GARCH approach

2021· article· en· W4225643522 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSpillover effectVolatility (finance)Autoregressive conditional heteroskedasticityForeign exchangeMonetary economicsExchange rateEconomicsEconometricsFinancial economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

This empirical analysis endeavors to examine the return volatility, co volatility and spillover impact of Australian dollar, Canadian dollar, Japanese yen, and Swiss franc in pertinent Asian economies such as India, Malaysia, and Singapore, by using the variance decomposition and GARCH-DCC techniques with the help of daily time series data from five years from 2012 to 2019. The result of GARCH-DCC analysis shows the evidence of ARCH and GARCH effect on all the tradable currencies, in the foreign exchange markets of above countries. The consequence of volatility spillover proves that, the Australian dollar is a net transmitter of volatility while the Canadian dollar is a net receiver of volatility in the Indian foreign exchange market. As per as Malaysian and Singapore’s foreign exchange market is concerned it can be inferred that Japanese yen is dominant currency in Malaysian market while Swiss franc is relevant in Singapore’s exchange market. These outcomes have vital ramifications that financial organizer should consider in recurrence volatility of tradable currencies of above foreign exchange market to forestall the financial risk.

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)0.039
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.122
GPT teacher head0.388
Teacher spread0.266 · 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