Co-integration of Karachi stock exchange with major Asian markets
Bibliographic record
Abstract
The purpose of this research is to study the long run relationships and co movement amongthe stock markets of Pakistan and other Asian stock marketsi.e.India,Malaysiaand Indonesia.Overthe period of Jan 1,1998 to October 3,2011.This paper examines the co-movement among stockmarkets of Pakistan,India,Malaysia and Indonesia.Descriptive statistics,correlation,co-integrationtests are run to check the behavior and co movement of markets.Granger causality test is used tocheck the lead lag relationship.Impulse response tells about the one standard deviation change inmarket bring what standard deviation change in other market.Variance decomposition technique isused to decompose the variance in one market due to change in another market and due to its owndynamicsi.e.economic and political conditions also affect the market.The results shows that the fourmarkets Pakistan,India,Malaysia and Indonesia areweakly correlated with each other and find noco-integration.Variance decomposition shows that most of the change in above listed countries is dueto their own factors.Number of studies has been conducted on developed markets like United Statesof America,United Kingdom,France,Japan Canada and underdeveloped countries,but this paperfocuses on emerging markets of Asia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".