Efficiency of the Black Foreign Exchange Market
Bibliographic record
Abstract
This empirical study is first of its nature to examine the weak-form of efficiency for unofficial foreign exchange market of Pakistan proxied by Japanese Yen (JPY/PKR), Swiss Franc (CHF/PKR), British Pound (GBP/PKR), and US Dollar (USD/PKR) exchange rates. For this we have employed Ljung Box Q-test, unit root tests including Dickey-Fuller (Dickey 1979), Augmented Dickey-Fuller (Dickey 1981) tests and Phillips and Perron (1988) test, Durbin Watson test, Runs-test, and Variance ratio test by using unofficial foreign exchange rate time series of Yen/PKR, CHF/PKR, GBP/PKR and USD/PKR from 1994M07 to 2001M06. Empirical results lead to the conclusion that the unofficial foreign exchange market of Pakistan is weak-form efficiency. The implications of this empirical research are of great importance for designing foreign exchange policy i.e. policy makers (be it accounting, export/import or public policy makers) are to consider fluctuations in unofficial foreign exchange rates while designing official foreign exchange rate policy of developing country like Pakistan. Further, policymakers can enhance the efficiency of official foreign exchange market by intervention subject to a widening of unofficial foreign exchange premium beyond a certain limit in developing countries like Pakistan.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".