Bibliographic record
Abstract
The recent financial crisis in the US and then the debt crisis in Europe have impacted the economical performance of the world in general, particularly the financial performance. With the increase in globalization and cross-border trade, the dependence of one economy on others increases. Portfolio diversification theory proposed by Markowitz, in 1952, suggests that if two markets are not significantly correlated, taking a position in that market helps in reducing the risk of the portfolio. The recent turmoil in these two markets (US and Europe) has affected the entire world and a lot of volatility has been observed in all financial markets around the world. Hence, it becomes imperative to identify the volatility spillover effect from one market to the other at this point. This article examines the volatility spillover of one market on other by taking five exchange rate markets: Great Britain pound (GBP), Euro (EUR), Canadian dollar (CAD) and Australian dollar (AUD) and Japanese yen (JPY) against the US dollar. Data from June 2008 to December 2012 were taken for consideration. Unit root test was used to identify the stationarity of data and the variance decomposition method was applied to identify the degree of influence of one market on other. The volatility spillover index, as suggested by Diebold and Yilmaz (2009), was created to identify the return spillover effect. Outcomes suggest that volatility on JPY and CAD follows the concept ‘better to give than receive’, while GBP as the net receiver goes along with the findings of Nikolaos (2012). A volatility spillover index of 11.11 per cent indicates low possibility of volatility transmission among considered currency pairs. This article will help traders, portfolio managers, policy makers and other market participants identify the source of volatility in considered currency markets and to take corrective measures.
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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 | 0.017 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".