Time-varying volatility spillover of foreign exchange rate in three Asian markets: Based on DCC-GARCH approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".