Price and Volatility Spillovers across North American, European and Asian Stock Markets: With Special Focus on Indian Stock Market
Bibliographic record
Abstract
This paper investigates interdependence of fifteen world indices including an Indian market index in terms of return and volatility spillover effect. Interdependence of Indian stock market with other fourteen world markets in terms of long run integration, short run dependence (return spillover) and volatility spillover are investigated. These markets are that of are Canada, China, France, Germany, Hong-Kong, Indonesia, Japan, Korea, Malaysia, Pakistan, Singapore, Taiwan, United Kingdom and United States. Long run and short run integration is examined through Johansen cointegration techniques and Granger causality test respectively. Vector autoregressive model (VAR 15) is used to estimate the conditional return spillover among these indices in which all fifteen indices are considered together. The effect of same day return in explaining the return spillover is also modeled using univariate models. Volatility spillover is estimated through AR-GARCH in which residuals from the index return is used as explanatory variable in GARCH equation. Return and volatility spillover between Indian and other markets are modeled through bivariate VAR and multivariate GARCH (BEKK) model respectively. It is found that there is greater regional influence among Asian markets in return and volatility than with European and US. Japanese market, which is first to open, is affected by US and European markets only and affects most of the Asian Markets. Also, high degree of correlation among European indices namely FTSE, CAC and DAX is observed. US market is influenced by both Asian and European markets. Specific to Indian context, it is found that Indian market is not cointegrated with rest of the world except Indonesia. This may provide diversification benefits for potential investors. However, strong short run interdependence is found between Indian markets and most of the other markets. Indian and other markets like US, Japan, Korea, and Canada positively affect each others conditional returns significantly. Indian market also has significant effect on Malaysia, Pakistan, and Singapore return. This study found that there is significant positive volatility spillover from other markets to Indian market, mainly from Hong Kong, Korea, Japan, and Singapore and US market. Indian market affects negatively the volatility of US and Pakistan. It is interesting to note that Chinese and Pakistan markets are less integrated with other Asian, European and US 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".