A Large Trader in Bubbles and Crashes: An Application to Currency Attacks
Bibliographic record
Abstract
Abreu and Brunnermeier (2003) study stock market bubbles and crashes in a dynamic model with a continuum of rational small traders. We introduce a large trader into their model and apply it to currency attacks. In an attack against a fixed exchange rate regime with a gradually overvaluing currency, traders lack common knowledge about the time when the overvaluation starts. Meanwhile, they need to coordinate to break a peg. In such a setup, both the inability of traders to synchronize their attack and their incentive to time the collapse of the regime lead to the persistent overvaluation of the currency. We find that the presence of a large trader with perfect information will accelerate the collapse of the regime and alleviate currency overvaluation. However, if a large trader has incomplete information, the presence of a large trader may accelerate or delay the collapse of the regime ex post, depending on the size of his wealth and the precision of his information. More specifically, we find that a large trader with both a large amount of wealth and very noisy information can greatly delay the collapse of the regime ex post. Moreover, we find that the presence of a large trader with incomplete information can greatly increase the unpredictability about the time when the regime collapses, implying the difficulty for traders to time the collapse.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".