Volatility Spillover Effects among Securities Exchanges in East Africa
Bibliographic record
Abstract
This paper aims at examining volatility spillover effects among the returns of three out of the four securities exchanges in East Africa. Vector autoregressive model was used to model return series evolution; and, Johansen co-integration test, was further applied to examine any possibilities of co-integration. Dynamic conditional correlation model was then employed to explore the dynamics of conditional variances. Daily closing all share indices data spanning the period 29 February 2008 to 28 February 2018 was used. The results of the study revealed that, there is bidirectional causality between Nairobi securities exchange and Dar es salaam securities exchange; unidirectional effect between Nairobi securities exchange and Uganda securities exchange; while between Dar es salaam securities exchange and Uganda securities exchange, there is a unidirectional effect. The study findings also indicate evidence of no co-integration, thus, no long-run relationship among the exchanges. The dynamic conditional correlation proved to be the most parsimonious model whose results indicated evidence of volatility spillover among the securities exchanges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".