Spillover effects of volatility between the Chinese stock market and selected emerging economies in the middle east: A conditional correlation analysis with portfolio optimization perspective
Bibliographic record
Abstract
In recent years, the rapid transmission of information and interconnectedness of global financial markets have amplified the convergence and influence among them. Consequently, the occurrence of spillover effects in one market can significantly impact other markets. Accurately identifying and understanding these spillover effects is crucial for effectively managing and controlling market fluctuations. This research aims to measure and analyze the spillover effects between China's stock market and selected emerging economies in the Middle East, with a focus on exploring diversification opportunities. The analysis encompasses three distinct time periods, including the overall period from May 1, 2005, to May 31, 2023. The sub-periods consist of the first sub-period from May 1, 2005, to October 31, 2009, and the second sub-period from December 1, 2010, to May 31, 2023. Multivariate Generalized Heterogeneous Autoregression (MGARCH) is employed in this study to examine the spillover effects between China's economy and the emerging economies under consideration. The Granger causality analysis reveals a unidirectional causality running from the Chinese stock market to Jordan, as well as from the UAE to China throughout the entire observation period. However, no spillover effects are found between China and Saudi Arabia in either direction during any of the periods. Notably, a two-way causality is detected between the Chinese and UAE markets in the second sub-period. Furthermore, MGARCH results indicate no spillover effects from China to the emerging economies during the overall period, first sub-period, or second sub-period. The findings of this research offer valuable insights for investment portfolio managers in the Chinese economy, who may consider the examined emerging economies as potential destinations for risk diversification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".