Does the source of oil price shock matter for Indian sectoral stock returns? A time-frequency approach to analyse dynamic connectedness and spillovers
Bibliographic record
Abstract
This paper examines the connectedness and spillovers between decomposed oil shocks and Indian sectoral equities in a time-frequency domain using the most recent Barunik and Krehlik (2018) approach. Our empirical results show that the oil demand shock is the major spillover transmitter across all time horizons followed by the risk shocks; while oil supply shock appears to be a net receiver in all the frequency bands indicating the differential impact of oil shocks. Among the sectors, Basic Materials and Finance receive the highest spillovers from oil shocks in the short-term; while Consumer Discretionary Goods & Services and Industrials join the list in the medium- and long-term. FMCG, Health, Telecom and IT sectors receive the least spillovers in all the bands, making them apt for investments during periods of high volatility. Our empirical results are robust to the application of the alternative TVP-VAR time-frequency parameter framework. The portfolio analysis shows that inclusion of stocks in Oil and sectors like Metal and Telecom significantly reduces the portfolio risk. Dynamic connectedness analysis reveals that the spillovers dramatically increase during times of extreme turmoil, especially during the Global Financial Crisis (2008–2009) and the COVID-19 pandemic. Policy implications of our empirical results are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".