The Preliminary Study on the Spillover Effects across Commodity Index and the Stock Markets of Different Types of Countries
Bibliographic record
Abstract
[[abstract]]本研究的目的是探討商品指數(標準普爾高盛商品現貨指數；Standard and Poor Goldman Sachs Commodity Index, S&P GSCI) 和世界上幾個重要的新興國(巴西、俄羅斯、印度、中國與南非)及先進國 (加拿大、日本、荷蘭、挪威與西班牙)之股市的動態互動關係。而它們之間的長期關係與 2008 年美國金融海嘯前後之關係的差異性也會一併進行分析。本研究使用動態條件相關門檻一般性自我迴歸條件異質性(DCC-T-GARCH)模型以探討股市與商品相關性之變動，並分析正向或負向衝擊(shocks)對商品或股市指數之條件變異數造成的影響。主要發現如下: (i)負向衝擊比正向衝擊對股市或商品指數之條件變異數會造成更大的影響。(ii)巴西與加拿大股市的變動對商品指數的變化具有解釋能力；而商品指數的變動則可以影響俄羅斯、印度、中國、南非等新興國與日本、挪威等先進國股市的走向。(iii)商品指數的報酬率 (return) 會對俄羅斯、印度、南非等新興國與加拿大、荷蘭、挪威等先進國股市的報酬率產生波動性外溢 (volatility spillover) 的效果;而印度、加拿大與挪威股市則會對商品指數產生負向的波動性外溢；中國與日本股市對商品指數會產生正向的波動性外溢。(iv)動態條件相關性存在於商品指數和這十個國家的股市之間。藉由這些發現，我們可以設計一套交易這十國之中任一國股票與商品指數的方案以達成有效避險的目的。 The purpose of this study is to investigate the dynamic interrelationship between Standard and Poor Goldman Sachs Commodity Index(S&P GSCI) and the stock markets of several important emerging countries: Brazil、Russia、India、 China、South Africa (BRICS) and advanced countries: Canada、Japan、Netherlands、 Norway、Spain(CJNNS). The difference between their long term relationships and the correlations before, during, and after financial tsunami in 2008 will be analyzed together. Asymmetric DCC-GARCH is employed to explore the variations of the connection between S&P GSCI and the stock markets of these countries, and capture the impacts of positive or negative shocks on the conditional variances of the stock markets or commodity index. The main findings of this research list below: (i) Negative shocks exert a stronger influence on the conditional variances than positive shocks. (ii) The change of commodity index can be explained by the variation of Brazilian and Canadian stock market; and the alteration of commodity index has a certain level of interpretative power for the analysis of the stock markets of Russia、India、China、South Africa、Japan and Norway. (iii) Commodity index returns exert volatility spillover effects on the stock market returns of Russia、India、South Africa、Canada、Netherlands and Norway, itself is positively influenced by the volatility of stock market returns of China and Japan, and negatively influenced by the volatility of stock market returns of India、Canada and Norway. (iv) Dynamic conditional correlation (DCC) exists between commodity index and the stock markets of these ten countries. An effective hedging strategy by trading stocks of these ten countries and commodities can be schemed by these discoveries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".