INTER-LINKAGES BETWEEN INDIAN AND MAJOR EQUITY MARKETS - IMPACT OF GLOBAL FINANCIAL CRISIS
Bibliographic record
Abstract
This paper examined the short and long run correlating, causal and co-integrating relationship between Indian and other major developed [Australia, Canada, France, Germany, Japan, United Kingdom (UK) and United States of America (USA)] and developing [Argentina, Brazil, China, Mexico, Russia, and South Africa (SA)] markets for the period from April 2003 to December 2014 which was further subdivided in two sub periods: a Pre-crisis period (April 2003 to August 2007) and a Post-crisis period (August 2007 to December 2014). We applied correlation analysis, short and long run granger causality and Johansen Co-integration techniques on the monthly adjusted closing indices values of representative market indices. Overall, results show that India had high correlating, causal and co-integrating relationship with Brazil, China, Russia and South Africa from the developing block and with Australia and Canada amongst the developed economies. This could be due to large bi-lateral trade and/or close political and cultural ties between these countries like the official BRICS group. Also, while the correlations significantly reduce post crisis, causal and co-integrating relationships increase post crisis. So, the nature of relationship between these markets has shifted from being contemporaneous to more of lead-lag nature. Thus, we find evidence for increase in contagion post crisis. This has important implications for all stakeholders. Policy makers and stock market regulators need to be vigilant and take steps to insulate domestic markets as crisis is evidenced to greatly accentuate contagion. Investors can work out possible arbitrage opportunities as we find evidence of several lead-lag relationships among these markets. International investors can breathe easy regarding their international portfolio diversification as we find support for declining correlations among these markets.
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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 | 0.001 |
| Scholarly communication | 0.003 | 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".