Quantitative Tightening Around the Globe: What Have We Learned?
Bibliographic record
Abstract
This paper uses the recent cross-country experience with quantitative tightening (QT) to assess the impact of shrinking central bank balance sheets.We analyze the experience in seven advanced economies (Australia, Canada, Euro area, New Zealand, Sweden, UK and US)documenting different strategies and the substantive reduction in central bank balance sheets that has already occurred.Then we assess the macroeconomic and financial impact of QT announcements on yields and a range of other market prices.QT announcements increase government bond yields, steepening the yield curve and potentially signaling a greater commitment to raising policy interest rates, but have more limited effects on most other financial market indicators.Active QT has a larger impact than passive QT, particularly on longer maturities.The implementation of QT has been associated with a modest rise in overnight funding spreads and a decline in the "convenience yield" of government bonds, but QT transactions did not significantly affect the pricing and market liquidity of government debt securities.Finally, we evaluate who buys assets when central banks unwind balance sheets, an issue which will become increasingly important if central banks continue to reduce their security holdings while government debt issuance remains elevated.We find that increased demand by domestic nonbanks has largely compensated for reduced bond holdings by central banks.This series of cross-country results suggests that QT has had more of an impact than "paint drying", but far less than simply reversing the effects of the quantitative easing programs launched during periods of market stress.Looking ahead, although QT has been smooth to date, frictions could increase in the future so that QT quickly evolves into more like watching "water boil".
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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".