Value-at-Risk Estimation on Foreign Exchange Portfolio Using DCC Multivariate GARCH Model
Bibliographic record
Abstract
本研究應用Engle（2002）提出之動態條件相（Dynamic Conditional Correlation，DCC）多變量GARCH模型去估計八國貨幣（歐元、英鎊、日圓、加拿大元、新台幣、韓元、新加坡元及澳元）所組成外匯投資組合的風險值。比較SMA、EWMA、CCC-GARCH及DCC-GARCH等四種模型在風險值之預測能力，在回溯測試採用Kupiec PF 檢定與RMSE資金運用效率之評估準則下，實證結果發現DCC-GARCH(1,1)-t模型因較能捕捉厚尾及波動群聚現象，其風險管理績效較為優異，故為估算外匯投資組合風險值的較佳選擇。 另八國匯率報酬率拒絕固定條件相關（Constant Conditional Correlation）之虛無假設，顯示國際匯率報酬率相關性並非固定，應適用動態相關係數之模型。本文亦發現八國匯市間之相關性及風險值會隨著波動性之提高而上升，說明國際匯市之波動性及相關係數為動態之時間序列，此可做為資產管理及投資組合分散風險之良好參考。 In this study, we apply the Dynamic Conditional Correlation (DCC) multivariate GARCH model, proposed by Engle (2002), to estimate Value-at-Risk (VaR) on foreign exchange portfolio composed of eight currencies including Euro, British pound, Japanese yen, Canadian dollar, Taiwan dollar, South Korea won, Singapore dollar and Australian dollar. By comparing the performance based on the Kupiec PF test in backtesting and RMSE for capital efficiency among SMA, EWMA, CCC-GARCH and DCC-GARCH models, we conclude that the DCC-GARCH(1,1)-t model, which accounts for characteristics of fat-tail and volatility clustering, is the better choice to compute VaR on foreign exchange portfolio. In addition, the returns of eight currencies lead to reject the null hypothesis of a constant conditional correlation, which reveals that the dynamic correlation model should be adopted. We also find that the correlation and VaR rise in periods when the conditional volatility of markets increases, implying that the volatility and correlation in international currency markets are dynamic time series. We could use such criterions as a good reference to allocate assets and diversify portfolio risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".