Monetary policy and efficiency in over‐the‐counter financial trade
Bibliographic record
Abstract
Abstract We develop a monetary model that incorporates over‐the‐counter (OTC) asset trade. After agents have made their money holding decisions, they receive an idiosyncratic shock that affects their valuation for consumption and, hence, for the unique liquid asset, namely money. Subsequently, agents can choose whether they want to enter the OTC market in order to sell assets and thus boost their liquidity or to buy assets and thus provide liquidity to other agents. In our model, inflation affects not only the money holding decisions of agents, as is standard in monetary theory, but also the entry decision of these agents in the financial market. We use our framework to study the effect of inflation on welfare, asset prices and OTC trade volume. In contrast to most monetary models, which predict a negative relationship between inflation and welfare, we find that inflation can be welfare improving within a certain range, because it mitigates a search externality that agents impose on one another when they make their OTC market entry decision. Also, an increase in the holding cost of money will lead to a decrease in asset prices, a regularity that is well documented in the data and often considered anomalous.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".