Distributional properties of EMS and non‐EMS exchange rates before and after German reunification
Bibliographic record
Abstract
Abstract The paper explores the stochastic behaviour of four EMS (Belgian franc, French franc, Spanish peseta and Italian lira) and non‐EMS (Canadian dollar, US dollar, Japanese yen and British pound) deutschemark exchange rates, using a GARCH‐type model along with the generalized error distribution, before and after Germany's unification in 1990. The results indicate that there was a fundamental change in the distributions of all exchange rates after Germany's reunification and so the single normal distribution assumption is not appropriate. Although the presence of GARCH remained, all rates' conditional distributions resembled the uniform, in the first period, but they approximated the double exponential in the second. Further, during and after the pound's brief and the lira's longer associations with the ERM their distributions were unchanged. Finally, two other notable results were the reduction in the degree of volatility persistence in all Deutschemark rates during the after‐unification period, and the rates' strong mean‐reverting tendencies. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 |
| 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".