An Impact of the Canada and the U.K. Return Volatility on the Hong Kong and the Singapore Stock Market Returns: A DCC and Bivariate AIGARCH Model
Bibliographic record
Abstract
This paper discusses the model construction and the association between the Hong Kong and the Singapore stock markets. The data period is from January 2001 to August 2010. The empirical results show that the dynamic conditional correlation (DCC) and the bivariate AIGARCH(1, 1) model are appropriate in evaluating the relationship of the Hong Kong and the Singapore stock markets. The empirical results also indicate that the Hong Kong and the Singapore stock markets are in a positive relation. The average estimation value of correlation coefficient equals 0.645, which implies that the two stock markets is synchronized influence. Besides, the empirical result also shows that the Hong Kong and the Singapore stock markets have an asymmetrical effect. The return volatility of the Hong Kong and the Singapore stock markets receives the influence of the positive and negative values of the Canada and the U.K. return volatility rates. The evidence might suggest that stock market investors or international fund manager must consider the Canada stock price return volatility risk and its close connection with the U.K. market while making investment decision on the Hong Kong and the Singapore stock market. In other words, in addition to considering the stability of stock market time, investors should take into consideration the foreign country stock market return volatility behavior in order to achieve the anticipated effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".