Volatility of Stock Markets (an Analysis of South Asian and G8 Countries)
Bibliographic record
Abstract
The objective of this study is to make an analysis of volatility of stock markets between South Asian Stock Markets and Stock Markets of Group of Eight Countries. This study important for the investors whose want to invest in stock markets. This study helps investors to determine what stock market is more volatile. To make the analysis three South Asian stock markets and Group of Eight countries stock markets are selected. South Asian stock markets indexes include KSE 100 (Pakistan), SENSEX (India), ASPI (Sri Lanka), CAC 40 (France), DAX (Germany), S &P / TSX Composite (Canada), FTSE MIB (Italy), RTS (Russia), Nikkei 225 (Japan), S & P 500 (USA) and FTSE 100 (UK). Data is collected from the period of January 1st 2005 to August 31st 2015. ARCH and GARCH model is used to analyze the volatility of South Asian Stock Markets and stock markets of Group of Eight Countries. The findings show that South Asian Stock Markets are less volatile while Stock Markets of Group of Eight Countries are high volatile. This study is useful for investment institutions and portfolio managers because it focuses on current issues and takes the current data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".