Nonlinear Spillovers from Stock, Gold, Oil, and T-bill Volatilities to Predict Economic Policy Uncertainties
Bibliographic record
Abstract
Economic policy uncertainity (EPU) shapes the economic development of a country and any instability in policy results in financial markets downturn. Several elements are considered as predictors of EPU. Of these, commodities (oil, gold) are the most common. This study consider financial markets with four major asset classes gold, crude oil, 10-year treasury bonds, and stock prices to examine a nonlinear and asymmetric spillover that influences EPU. The dataset comprises oil price volatility, gold price volatility, T-bills volatility, stock price volatility, and the EPU index of eight countries. NARDL model is used to capture the impact of the nonlinear behavior of uncertainties on gold, oil, T-Bills, and stock market volatilities. It captures both long-run and short-run non-linearities by separating explanatory variables into partially positive and partially negative components. The outcomes reveal positive and negative shocks to oil price volatility, gold price volatility, T-Bills volatility, and stock price volatility which positively affect the EPU of all countries. However, Canada does not bear any effect of negative shocks in the short-run to gold price and oil price volatilities to predict the EPU. USA shows the negative impact of negative shocks for all asset classes. T-Bills derived negative shocks adversely affect China at 5% level of significance. Furthermore, the effect of positive shocks is more pronounced than negative shocks. The outcomes support the short-run and long-run asymmetric impact of oil, gold, T-Bills, and VIX volatilities to predict EPU. This study helps investment funds in managing risk, asset pricing, and formulating economic policy differentiated to positive and negative shocks.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".