Testing the Random Walk Hypothesis for Real Exchange Rates
Bibliographic record
Abstract
This chapter aims to analyze the efficiency, in its weak form, in the exchange rates of Brazil vs. USA, Australia, Canada, Europe (Euro Zone), Switzerland, United Kingdom, and Japan from July 1, 2019 to September 20, 2020. The results suggest that exchange rates show signs of (in)efficiency, in their weak form (i.e., the values of the variance ratios are lower than the unit), which implies that returns are autocorrelated over time, and there is reversal to the average. In corroboration, the results of detrended fluctuation analysis (DFA) show persistence in yields (i.e., the existence of long memories), thus validating the results of the Lo and Mackinlay model that show autocorrelation between the series of yields. As a conclusion, the authors show that the assumption of market efficiency may be questioned, since the forecast of market movement may be improved if the lagged movements of the other markets are taken into account, allowing the occurrence of arbitrage operations in these foreign exchange markets.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".