Volatility dynamics of stock returns, liquidity and exchange rates in ASEAN Countries
Bibliographic record
Abstract
In this study, we examined the volatility trend of stock return in eight ASEAN stock markets. These includes the Singapore Exchange (SGX), Bursa Malaysia Stock Exchange (YSX), the Stock Exchange of Thailand (SET), Indonesia stock exchange, the Vietnam Stock Exchange (VNX), the Cambodia Securities Exchange (CSX), the Lao Securities Exchange (LSX), and the Philippine Stock Exchange. Secondly, we evaluated the factors that influence the level of return in those stock markets with exchange rate volatility as a control variable. By employing FIGARCH-DCC and ARDL models, the study aimed to provide a more robust understanding of stock market dynamics. The findings reveal significant negative returns effect of market volatilities and liquidity crisis in all the stock exchanges of all sample countries in the study. In Singapore, money supply variation, market volatility, liquidity risks, and exchange rate volatility significantly influenced stock returns positively. The short-run model explains 52.26% of the variation in stock returns. Only in Malaysia, we had significant positive returns from exchange rate volatility. Nevertheless, the Russian model explains just 22.22% of the variation in stock returns. In Thailand and Indonesia alike, returns significantly and positively responded to variation in money supply, while the volatility in the market and currency rate exchange adversely impacted returns. The short-run models explain 53.66% and 65.21% of the variation in stock returns for Vietnam and Indonesia, respectively. The variation in money supply does not significantly affect stock returns and has no significant contribution to returns in Cambodia. The Cambodia model explains around 48.34% of the variation in returns. For Lao Stock Exchange, return effects of liquidity risk, and exchange rate instability were significant and negative. Market volatility had insignificantly impacted stock returns in Nigeria. The Lao model explains 50.38% of the variation in stock returns. In the Philippine Stock Exchange, the returns effect of exchange rate volatility and liquidity crisis are adverse and significant. Money supply variation and market volatility had insignificant influence on returns. The model explains 68.11% of the variation in returns. In the Philippines, market volatility, liquidity risks, and exchange rate volatility adversely impacted returns. Money supply variation had no such significant influence on returns. The panel model of the Philippines explains 62.9% of the variation in stock returns. The research accentuates the need for governments to stabilize exchange rates, boost liquidity, through targeted policies aimed at managing stock market dynamics especially as it relates to stock volatility in order to foster meaningful growth and development of the financial market.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".