Term-Structure-Yield-Curve | Macro Paper Warehouse

Balance-Sheet Policy and the Term Premium: High-Frequency Evidence

Mon, 01 Jan 0001 00:00:00 +0000

When a central bank shrinks its balance sheet, how much do long-term interest rates actually move — and through which channel? Using minute-by-minute market data around balance-sheet announcements, the authors estimate that much of the long-rate response works through the term premium rather than through changed expectations of future short rates. The result is an estimate for their 2009–2024 sample under their identifying assumptions — evidence consistent with a term-premium channel, not a universal constant.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. Does balance-sheet policy move long rates through the term premium or through expected short rates?

The paper estimates that a substantial share of the long-rate response operates through the term premium, with a smaller role for revised short-rate expectations — though it frames this as identification within their window, not a structural decomposition that holds in all regimes. This sits against a literature that has split the response into a signaling channel and a portfolio-balance channel; the contribution here is using intraday yields to isolate the announcement effect from contaminating macro news.

Q2. How is the effect identified, and why high-frequency?

By measuring yield changes in narrow windows around scheduled balance-sheet announcements, so that other macroeconomic news is unlikely to move rates within the window. The maintained assumption is that within a tight enough window, the announcement is the dominant shock — a standard high-frequency identification premise. The authors note the assumption is weaker around unscheduled communications, and restrict the main sample accordingly.

Q3. What does this imply for the pace of balance-sheet runoff?

If transmission runs through the term premium, the pace and predictability of runoff plausibly matter for long rates — but the paper presents this as suggestive, stopping short of a calibrated policy rule. The reading is that quantity and communication interact, consistent with prior work on announcement effects, rather than that runoff has a single mechanical effect on yields.

Key concepts

term premium: The extra return investors require for holding a long-term bond instead of rolling over short-term ones — here, the part of long rates not explained by expected future short rates.
balance-sheet policy: A central bank changing the size or composition of its asset holdings (expansion via purchases, runoff via “quantitative tightening”) as a policy tool distinct from setting the short-term rate.
high-frequency identification: Inferring a policy action’s effect from price moves in a very short window around the announcement, on the assumption that little else moves markets inside that window.

Exchange Rates and Asset Prices in a Global Demand System

Mon, 01 Jan 0001 00:00:00 +0000

The paper develops an asset demand system to analyze, jointly and across all countries, how international portfolio holdings and flows, exchange rates, short-term rates, long-term yields, and equity prices are determined in equilibrium. The authors specify a nested logit model of asset demand (substitution across countries within an asset class, and across asset classes) and introduce a new instrumental-variables identification strategy based on the size distribution of countries and bilateral distances; estimating on portfolio-holdings data for 37 countries and three asset classes from 2003 to 2020, they find demand is relatively inelastic, with mean demand elasticities of 27.9 (s.e. 1.9) for short-term debt, 3.2 (0.4) for long-term debt, and 1.2 (1.1) for equity. A variance decomposition attributes 82% of exchange-rate variation, 86% of short-term-rate variation, and 60% of log market-to-book equity variation to ’latent demand’ (the residual demand shifter), while portfolio flows (54%) and macro variables (43%) dominate long-term yields. Applying the framework to the European sovereign debt crisis, latent demand explains essentially all of the Italian long-term-yield variation and 74% of the Portuguese, whereas macro fundamentals are relatively more important for Greece (46% vs. 32% for latent demand), which the authors read as consistent with Greece being insolvent while Italy and Portugal were solvent but perceived as vulnerable. Estimating the convenience yield on US assets, they find, in units of expected annual returns, 1.41% on the US dollar, 2.71% on US long-term debt, and 0.50% on US equity. All estimates are specific to their sample, model, and identification assumptions.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is a ‘global demand system’ and what does it explain?

The authors represent the equilibrium of an international macro model as an asset demand system and replace traditional optimal portfolios with estimated asset demand functions that match observed international portfolio holdings, so that portfolio flows and shifts in asset demand explain all movements in exchange rates and asset prices. This lets them reinterpret the exchange rate disconnect (Meese and Rogoff 1983) as the finding that shifts in asset demand through macro variables explain much less variation than portfolio flows and latent demand, and to identify which countries’ latent demand matters for exchange rates and asset prices.

Q2. What is the nested logit model of asset demand?

Asset demand follows a nested logit model with substitution across countries in the inner nest and across asset classes in the outer nest, where demand depends on expected returns (asset prices or yields and real exchange rates), macro variables (GDP, GDP per capita, inflation, equity volatility, sovereign rating), bilateral distance (the gravity effect), a domestic-ownership indicator (home bias), and latent demand. The nested structure gives more flexible substitution than the logit model of Koijen and Yogo (2019), while latent demand captures heterogeneous beliefs about risk exposure across investors and assets.

Q3. How are the demand elasticities identified?

The authors develop an instrumental-variables strategy in which an exogenous component of one investor group’s demand shifters generates variation in residual supply that identifies another group’s demand elasticity, isolating cross-sectional variation in residual supply from the size distribution of countries and the bilateral distances between them. Intuitively, smaller issuer countries in close proximity to larger investor countries have lower residual supply and thus higher asset prices and/or real exchange rates (the example contrasts Dutch with Australian long-term debt).

Q4. What are the estimated demand elasticities, and why do they matter?

Averaged across years and issuer countries, the mean demand elasticities are 27.9 (s.e. 1.9) for short-term debt, 3.2 (0.4) for long-term debt, and 1.2 (1.1) for equity — so, e.g., a country’s aggregate equity demand falls about 1.2% per 1% rise in its price. The authors present these as empirical targets for international macro models that rely on inelastic demand and demand shocks unrelated to fundamentals to resolve long-standing puzzles, and they note the estimates are broadly consistent with prior, more granular estimates for narrower sets of countries and asset classes once differences in aggregation and identification are accounted for.

Q5. What does the variance decomposition reveal?

Latent demand is relatively more important for exchange rates, short-term rates, and equity prices — explaining 82% of exchange-rate variation (of which foreign-exchange reserves explain 10%), 86% of short-term-rate variation, and 60% of log market-to-book equity variation — whereas portfolio flows (54%) and macro variables (43%) are relatively more important for long-term yields (latent demand explains only about 3%). For equity, North American investors explain 13% and European investors 26% of the log market-to-book variation.

Q6. How does the framework interpret the European sovereign debt crisis?

Applied to extreme long-term-yield movements in Greece, Italy, and Portugal, the decomposition shows macro variables are relatively more important for Greece (46% vs. 32% for latent demand), while latent demand explains all of the Italian and 74% of the Portuguese yield variation, with European investors alone explaining 98% of the Italian and 65% of the Portuguese movements. The authors read this as consistent with the narrative that Greece was insolvent while Italy and Portugal were solvent but perceived as vulnerable.

Q7. What are the estimated convenience yields on US assets?

Computing counterfactual prices that remove the special demand for US assets, the authors estimate convenience yields, in units of expected annual returns, of 1.41% on the US dollar, 2.71% on US long-term debt, and 0.50% on US equity. In the absence of special status, a value-weighted US-dollar exchange rate would be 5.23% higher, the US long-term yield 0.73% higher, and US market-to-book equity 3.35% lower, consistent with the view that the dollar is the global reserve currency and US Treasury debt the global safe asset.

Q8. How does the framework connect to monetary policy?

The authors note in their conclusion that, because unconventional monetary policy fundamentally concerns changes in the supply of long-term debt and its impact on exchange rates and asset prices through substitution effects, the demand-system approach is suited to study the simultaneous and cumulative impact of conventional and unconventional monetary policy across many countries — and they flag this as a direction for future research rather than a result of the current paper. This scope condition matters: the present paper estimates the demand system and its decompositions, not the effects of monetary policy itself.

Key concepts

asset demand system / demand system asset pricing : an approach (introduced in Koijen and Yogo 2019 and here extended to international finance) that estimates asset demand functions on portfolio holdings data and analyzes the equilibrium relation between holdings/flows and prices, in place of traditional optimal portfolios.

nested logit asset demand : the specific functional form for demand, with substitution across countries in the inner nest and across asset classes in the outer nest, allowing flexible substitution patterns.

latent demand : the residual component of demand shifters — capturing heterogeneous beliefs about risk exposure — that, together with portfolio flows and macro variables, accounts for movements in exchange rates and asset prices; it is the dominant driver of exchange rates and short-term rates in the decomposition.

demand elasticity (inelastic markets) : the percentage change in a country’s aggregate asset demand per 1% change in its price; the paper’s low estimates (especially 1.2 for equity) are offered as empirical targets for ‘inelastic markets’ macro-finance models.

convenience yield : the extra demand for (and hence lower expected return on) US assets owing to their special status as global reserve currency and safe asset; measured here as 1.41% (USD), 2.71% (US long-term debt), and 0.50% (US equity) in expected-annual-return units.

gravity effect and home bias : the empirical regularities that portfolio holdings decline with bilateral distance (gravity) and are tilted toward domestic assets (home bias), which the demand system captures via distance and a domestic-ownership indicator.

Passive Quantitative Easing: Bond Supply Effects through Lower Debt Issuance

Mon, 01 Jan 0001 00:00:00 +0000

The paper introduces the concept of “passive quantitative easing” (passive QE): a deliberate reduction in government debt issuance that lowers anticipated future bond supply and reduces long-term yields through the same supply channel as central bank asset purchases, without involving asset purchases or reserves creation. The authors develop a unified classification scheme for central bank balance sheet policies organized by their net effect on anticipated future bond supply, and show that the Danish government’s unexpected January 2015 debt halt — which removed approximately 29.9 billion DKK from the outstanding bond stock over roughly nine months — was followed by a two-day yield decline of approximately 25 basis points across the entire yield curve. Regression estimates controlling for concurrent ECB and SNB actions imply that the halt raised the safety premium on Danish bonds by 17–22 basis points and reduced the ten-year term premium by 37–70 basis points, with combined effects pointing to 54–92 basis points in lower yields relative to the counterfactual. The Danish episode ranks approximately on par with the Federal Reserve’s QE3 in the classification scheme, and the paper argues that passive QT — unexpectedly higher debt issuance — is contractionary through two additional portfolio balance channels not present in active QT and should be treated as an active policy tool rather than a neutral background condition.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is “passive QE” and what distinguishes it from conventional QE?

The paper defines passive QE as a reduction in government debt issuance that lowers anticipated future bond supply, arguing this is functionally equivalent to central bank asset purchases in its effects on long-term yields, even though it involves neither asset purchases nor reserves creation. The supply-side equivalence holds because what matters for term premia and safe-asset premia is the anticipated future stock of bonds available to private investors: whether the central bank withdraws bonds via outright purchases or the government simply issues fewer new ones, the anticipated future supply declines, requiring downward adjustment in the compensation investors demand for duration risk and scarcity. The distinction from active QE is therefore operational rather than economic: passive QE leaves the central bank’s balance sheet unchanged, makes no reserve injection, and requires no fiscal–monetary coordination beyond the government’s own debt management decisions.

Q2. How do the authors classify central bank balance sheet policies?

The paper proposes a unified classification scheme that maps central bank balance sheet policies by their net effect on anticipated future bond supply, placing passive QE in the same stimulative category as active QE programs and ranking the Danish halt at approximately −0.0104 on this measure — nearly on par with the Federal Reserve’s QE3 at −0.0120. The scheme allows cross-country and cross-program comparisons of unconventional monetary policy actions by reducing them to a common currency of anticipated supply change. The classification also distinguishes passive QT from active QT: the paper argues that passive QT (higher-than-anticipated issuance) is more contractionary than active QT of equal magnitude because higher issuance also reduces safe-asset scarcity value and shifts duration risk back to the market through two additional portfolio balance channels.

Q3. What does the Danish debt halt episode show?

The January 30, 2015 announcement by Denmark’s debt management office that it would halt new government bond issuance for the remainder of the year was unexpected and was followed within two trading days by a yield decline of approximately 25 basis points across the entire yield curve. The halt lasted roughly nine months and reduced the outstanding Danish government bond stock by approximately 29.9 billion DKK. The reaction is interpreted as evidence that market participants immediately revised down their expectations of future bond supply, compressing the compensation required for holding duration risk and raising the relative value of the now-scarcer safe assets.

Q4. What do the regression estimates imply?

Controlling for the concurrent SNB and ECB announcements in January 2015, the authors’ regression estimates imply that the Danish halt raised the safety premium on Danish bonds by 17–22 basis points and reduced the ten-year term premium by 37–70 basis points, pointing to a combined reduction in bond yields of 54–92 basis points relative to the counterfactual without the halt, measured over the halt period. The term-premium decline is interpreted as consistent with supply-induced portfolio balance effects: fewer bonds requiring lower duration-risk compensation. The safety-premium increase is consistent with safe-asset scarcity effects: a tighter supply of high-quality government bonds raising their relative scarcity value. These two channels are identified separately in the yield decomposition and estimated to be independently significant.

Q5. How does the paper treat passive QT?

The paper argues that passive QT — a higher-than-anticipated level of government debt issuance — is not a neutral background condition but an active contractionary force, and potentially more contractionary than active QT of equal magnitude through two additional portfolio balance channels. The argument is that higher issuance reduces safe-asset scarcity value and directly shifts duration risk from the central bank to the market, while active QT (central bank balance sheet reduction) lacks these two additional channels. This implies that fiscal authorities’ debt issuance decisions carry monetary policy implications that are not captured in frameworks treating issuance as a non-monetary decision.

Key concepts

passive QE : a deliberate reduction in government debt issuance that lowers anticipated future bond supply and reduces long-term yields through supply effects; the paper treats it as functionally equivalent to central bank asset purchase programs despite involving no asset purchases or reserves creation.

passive QT : higher-than-anticipated government debt issuance; the paper treats it as an active contractionary tool, potentially more contractionary than active QT of equal magnitude, because it triggers two additional portfolio balance channels.

safety premium : the premium on high-quality safe assets such as government bonds reflecting their scarcity value; in the Danish halt episode this rose as supply tightened.

term premium : the component of a long-term bond yield compensating investors for bearing duration risk; in the Danish halt episode this fell as anticipated future bond supply declined.

classification scheme : the paper’s taxonomy of central bank balance sheet policies organized by their net effect on anticipated future bond supply, allowing cross-program comparisons including passive QE and passive QT.

Danish debt halt : the January 30, 2015 announcement by Denmark’s debt management office of a halt to new government bond issuance for the remainder of the year, used as the natural experiment to test the passive QE hypothesis.