Economic and Geopolitical Shocks and Their Influence on the Saudi Stock Market, Saudi Aramco, and Bitcoin: Evidence from ARDL and VAR Models
Bibliographic record
Abstract
This study examines the dynamic relationships between the Tadawul All Share Index (TASI) returns, Saudi Aramco stock returns, and Bitcoin (BTC/SAR) returns from January 2, 2019, to December 31, 2023. Using the Autoregressive Distributed Lag (ARDL) model, Vector Autoregressive (VAR) models, impulse response functions, and Granger causality tests, the research explores how these relationships evolved across distinct macroeconomic periods, particularly during and after the COVID-19 pandemic and the Russo-Ukrainian war. The ARDL model results indicate stable long-run relationships between TASI and Saudi Aramco returns across all periods. However, these relationships weakened post-COVID-19, likely due to the pandemic’s structural impact. Short-run dynamics exhibited higher volatility during crises, with external shocks playing a significant role. The bounds test confirmed a long-term relationship pre-COVID-19, which weakened post-pandemic. The VAR model highlights strong interlinkages, especially pre-crisis, with TADAWUL returns leading Saudi Aramco returns. This relationship weakened in the post-COVID-19 and post-war periods, indicating global market disruptions. Granger causality tests revealed that causal dynamics are period-specific, with stronger causality observed before the crises. Impulse response functions show that TADAWUL shocks have a more substantial impact on Saudi Aramco than on Bitcoin returns. These findings provide valuable insights for market participants navigating an uncertain economic landscape.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".