Co-integration and Causal Relationships: The Case of the Jordanian and Developed Stock Markets
Bibliographic record
Abstract
The main purpose of this study is to investigate co-integration and causal relationships among the Jordanian, the US, and the UK stock markets. The vector error correction model is applied using yearly stock market indices series for the period, 1978 – 2018. The results reveal the existence of co-integration and causal relationships among stock markets indices. These results indicate the scope for diversification profits, where the Jordanian investors secure higher levels of mean returns on the diversified portfolios. This study is important for individual investors and policy makers in macroeconomics and finance, as stock markets affect consumption, wealth, and capital flows. The contribution of this study to the present literature is threefold. Firstly, it adds to the empirical literature an up-to-date dataset and employs a dynamic and causal approach, vector error correction model, which establishes whether market long-run equilibrium (i.e., co-integration) is stable for stock markets of Jordan, the USA, and the UK. Secondly, it analyses the performance of the Amman stock exchange for the period 1978-2018. Finally, this paper has implications for international portfolio diversification. If stock markets are co-integrated, this implies that there is an opportunity of arbitrage activity. In other words, stock markets are moving together towards long-term, and there are limited possibilities of gaining abnormal returns by diversifying investment portfolios.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".