GLOBAL RISK SPILLOVERS TO INTERNATIONAL EQUITY MARKETS: AN APPLICATION TO NON-PARAMETRIC CAUSALITY IN QUANTILES
Bibliographic record
Abstract
Purpose: This study examines the global risk spillover to International Equity Markets e.g., gold volatility index (GVX), crude oil volatility index (OVX), Volatility Index (VIX), Treasury Bills (TVX), Volatility of volatility index (VVIX), and Èconomic Ƥolicy Ưncertainty index (EPU). Design/Methodology: Following non-parametric causality in quantiles method we utilize weekly data of Canada, Japan, the UK, and the USA from June 12, 2008, till September 29, 2018. The Granger causality in quantiles detects and quantifies both linear and non-linear causal effects between random variables. Findings: Results of the study shows strong correlations between volatility of volatility index and stock markets. whereas weak correlation exist between Èconomic Ƥolicy Ưncertainity and stock markets. Increase in uncertainty indices cause a decline in equity stock markets. Uncertainty indices does not cause volatility in stock returns of TSX, TSE, LSE and NYSE. VVIX granger cause volatility of Japanese stock market returns. There is no evidence of risk spillover from uncertainty to international equity markets. uncertainty do not cause volatility in stock market returns of Canada, Japan, UK and USA. Originality: The results provide important insights for asset allocation, investment portfolio, and risk management to minimize the effect of volatility spillovers. As financial spillover amplifies in the absence of monetary stabilization, both conventional and unconventional monetary easing can increase spillover. Thus, the study would also benefit the policymakers in devising monetary policies which mitigate the influence of risk spillovers to international equity markets. The findings of the study have important implications for market regulators.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".