Currency Options, Implied Interest Rates and Inflation Targeting
Bibliographic record
Abstract
The currency option price is a powerful tool used regularly to determine market expectations on volatility in currencies using the implied volatility measure. This research tests and analyzes whether similar inferences can be made regarding interest rate and inflation expectations. Using historical options data, we derive and analyze implied interest rates during non-inflation targeting (non-IT) and inflation targeting (IT) periods for Australia, Canada, and the United Kingdom. We compare the results to a control group of countries that had not yet adopted inflation targeting during the period under study: Germany, Japan and Switzerland. Our results show that options prices can provide insights on market expectations on interest rates, that the adoption of inflation targeting strengthens the relationship between market expectations and inflation, and that shocks in interest rates and inflation lead to higher implied interest rates. In determining the potential uses of implied interest rates derived from currency options prices, our goal is not to replace the Federal Funds futures or equivalent tools in advanced economies, rather to present the usefulness of currency options as a tool to provide information to policymakers in emerging market economies. Central banks, such as the Banco Central de Colombia and Banco de Mexico, have been using currency options as tools for foreign exchange intervention or reserve accumulation/decumulation since the early 2000’s, and options markets in these economies have grown rapidly since then. Therefore, establishing the usefulness of implied interest rate measures derived from currency options prices may provide insights to policymakers and practitioners alike.
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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".