Risk-Premia | Macro Paper Warehouse

Credit Easing versus Quantitative Easing: Evidence from Corporate and Government Bond Purchase Programs

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

Using security-level data on individual corporate bond prices and the Bank of England’s published purchase quantities across its gilt purchase programs (QE1: £200bn, QE2: £125bn, QE3: £50bn, QE4: £60bn) and Corporate Bond Purchase Scheme (CBPS: £10bn of investment-grade sterling corporate bonds), this paper estimates supply effects of QE and CE on UK corporate bond prices, credit spreads, and new issuance separately, exploiting cross-sectional variation in quantities purchased as identifying variation via an instrumental variables approach. In the case of QE alone, supply effects on corporate bond prices are significant at announcement and larger over the full stock-effect horizon, but pass-through to credit spreads is found to be limited to the default-free component of corporate yields under normal market conditions — an exception is QE1 during the financial crisis, when QE’s cross-asset supply effects also significantly lowered credit spreads in the longer run. CE via the CBPS is found to be more effective than QE in reducing credit spreads for higher-rated investment-grade bonds even under normal conditions, and is the only program that generates a statistically significant increase in sterling corporate bond issuance. The results are consistent with QE and CE working through partially distinct channels — QE primarily affecting the default-free component of corporate yields, CE additionally compressing the credit-spread component — and complementing each other for higher-rated bonds.

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 the empirical strategy and why use a security-level approach?

The paper uses a two-stage instrumental variables (IV) approach at the individual corporate bond level, with pre-program bond characteristics — maturity, yield-curve fitting errors, the BoE’s prior ownership share in the gilt bucket — serving as instruments for the expected distribution of purchases across bonds, allowing isolation of the supply channel from signaling and duration channels. The security-level approach offers three advantages over aggregate or event-study methods: it enables construction of “substitute buckets” (bonds whose maturity is close to the purchased bonds’) to estimate cross-asset supply effects; it permits direct comparison of the price elasticity with respect to gilt purchases (cross-asset effect) versus corporate bond purchases (within-asset effect); and it allows estimation of both the announcement-day effect and the stock effect — the cumulative price and spread change over the life of each program — which captures the longer-run portfolio-rebalancing contribution separately from the initial market reaction.

Q2. What are QE’s effects on corporate bond prices and credit spreads?

For QE alone (QE1–3), the instrumented gilt substitute purchases have positive and statistically significant effects on corporate bond prices at announcement across all three programs — in the case of QE1, the average 30 basis-point decline in corporate yields on the announcement day is attributed in full to QE supply effects in the paper’s regression. The stock effect — estimated over the full life of each program — is significantly larger than the announcement-day effect, consistent with gradual portfolio rebalancing as predicted by Greenwood, Hanson, and Liao (2018). However, except for QE1, the supply effects do not carry through to credit spreads in either the short run or the longer run, which the paper interprets as consistent with QE working primarily through the default-free component of the corporate yield: corporate yields fell in line with gilt yields, but spreads over gilts were unchanged.

Q3. When does QE affect credit spreads?

QE1’s cross-asset supply effects significantly lowered credit spreads in the longer run, even though QE2 and QE3 do not generate significant credit spread compression in either the short or long run, suggesting that the supply channel interacts with the liquidity channel specifically under conditions of financial market distress. The paper interprets the QE1 exception as reflecting the severe disruption during the 2008–09 financial crisis: when capital mobility across markets is constrained and liquidity premia are elevated, central bank purchases of safe assets may also improve trading conditions in indirectly targeted, less liquid markets such as the corporate bond market, reducing the liquidity component of corporate spreads. This interaction does not appear to be operative in the more normal market conditions of QE2 and QE3.

Q4. How does CE compare to QE in reducing credit spreads and stimulating issuance?

CE via the CBPS is found to be more effective than QE in reducing credit spreads for higher-rated investment-grade bonds even under normal financial market conditions, and a corporate bond’s price sensitivity to its own CBPS purchases is substantially higher than its price sensitivity to gilt substitute purchases; CE is also the only program with a statistically significant positive effect on new sterling corporate bond issuance. Across QE1–3, there is no statistically significant impact of gilt purchases on sterling corporate issuance, while CBPS purchases have positive and statistically significant effects on new sterling corporate bond issuance. The paper characterizes CE and QE as complementary for higher-rated bonds: CE’s credit-spread reduction layers on top of QE’s default-free component effect, making the total stock effect larger than either program alone.

Q5. What happens for lower-rated investment-grade bonds?

For lower-rated investment-grade bonds, the evidence for both cross-asset QE supply effects and within-asset CE supply effects is weaker, and the paper suggests that CE’s stimulation of new bond issuance may have counterbalanced its positive price effects for these bonds through the dilutive effect of new supply. The mechanism is that CE’s reduction in the cost of corporate bond issuance for lower-rated firms induced enough new bond issuance to partially offset the price increase from CBPS purchases, consistent with the issuance channel being most active for the market segment where CBPS created the largest pricing improvement. This dilution effect implies that the net price benefit of CE for lower-rated bonds is smaller than the gross supply-effect estimate.

Key concepts

stock effect : the cumulative effect of the total quantity of bonds purchased under a program on bond prices and spreads, estimated over the full life of the program; in this paper the stock effect is significantly larger than the announcement-day effect, consistent with gradual portfolio rebalancing.

cross-asset supply effect : the pass-through of government bond (gilt) purchase supply shocks to the prices of corporate bonds — an asset class not directly targeted by QE; the paper provides the first estimates of this cross-market supply channel at the security level.

credit spread : the difference between the yield on a corporate bond and the yield on a risk-free government bond of the same maturity; the paper finds QE pass-through is generally limited to the default-free component of corporate yields rather than the credit spread.

default-free component : the part of a corporate bond’s yield attributable to the risk-free interest rate rather than credit risk; the paper finds that QE supply shocks affect this component but generally leave the credit spread unchanged in normal market conditions.

within-asset substitution effect : the price effect of CE purchases on the bonds directly purchased and their corporate bond substitutes, as distinct from cross-asset effects; the paper finds this effect is substantially larger in magnitude than the cross-asset QE effect on corporate bonds.

issuance channel : the mechanism by which lower corporate borrowing costs induced by CE stimulate new corporate bond issuance; the paper finds this channel operates under CE (CBPS) but not under QE (gilt purchases).

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.

Exorbitant Privilege Gained and Lost: Fiscal Implications

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

Layer 1: Overview

This paper studies three centuries of U.K. fiscal history to understand the fiscal implications of safe asset supplier status — what the authors call “exorbitant privilege” — and how it can be gained and lost. Using the discounted cash flow approach to fiscal capacity developed in Jiang, Lustig, Van Nieuwerburgh, and Xiaolan (2019), the paper measures the present discounted value of expected future primary surpluses (inclusive of convenience yield seigniorage) and compares it to the observed market value of outstanding government debt. The central finding is a sharp historical discontinuity: before World War I, when the U.K. was the world’s dominant safe asset supplier and its gilts served as the global reserve asset, roughly only three-quarters of U.K. debt was backed by future surpluses even after accounting for convenience yields earned from global safe asset demand. After World War II, when the U.K. lost its safe asset supplier status to the U.S., the U.K.’s debt became fully backed by surpluses and fiscal capacity became closely tied to its own macro fundamentals. By contrast, the U.S. after World War II shows a pattern similar to the pre-war U.K. but more extreme: less than one-third of outstanding U.S. Treasury debt is backed by future surpluses according to the paper’s estimates, with the gap between debt and estimated fiscal capacity growing sharply over recent decades.

In depth

Q1. How does the paper measure fiscal capacity?

The paper follows the Jiang-Lustig-Van Nieuwerburgh-Xiaolan (2019) methodology, expressing the market value of outstanding government debt as the present risk-adjusted discounted value of expected future primary surpluses under the government’s intertemporal budget constraint — the no-arbitrage condition that rules out rational debt bubbles. The market value of the government debt portfolio equals the present value of tax revenues minus the present value of government spending. A Vector AutoRegression (VAR) imposing cointegration of GDP with tax revenues and government spending is used to forecast the joint dynamics of the surplus. The paper uses the market or output risk premium as the discount rate, imputing the risk properties of GDP to spending and tax revenue claims.

A key methodological challenge is handling structural breaks: before World War I, U.K. fiscal policy was pre-Keynesian — acyclical spending and taxes (except during wars) — so spending and tax revenue as shares of output inherit the risk properties of output, and the market risk premium is the appropriate discount rate. After World War II, spending becomes counter-cyclical and taxes pro-cyclical in the Keynesian framework; the paper argues that applying the market risk premium in this regime produces an upper bound on the PDV of surpluses. For the U.K., this methodology is validated: the correlation of fiscal capacity with the debt/output ratio is 0.90 in the pre-WW-I sample and remains high after WW-II, despite the fiscal regime change.

Q2. What are the quantitative findings for the U.K.?

The paper finds that before World War I, U.K. fiscal capacity fell systematically short of the observed market value of U.K. debt: the average debt/GDP ratio was 87.06% while the estimated fiscal capacity was only 69.32%, with the ratio of fiscal capacity to debt averaging 74.32% — implying roughly 26% of U.K. debt was not backed by future surpluses even after including convenience yield seigniorage. The U.K. earned average long-term convenience yields of approximately 100 basis points per annum from 1873 to 1931 (translating to approximately 0.47% of GDP in annual seigniorage), reflecting its dominant position as the world’s safe asset supplier and the quasi-monopoly position of gilts in global securities markets (U.K. national debt accounted for more than half of the world’s traded securities around 1815). Despite these convenience yields, the gap between fiscal capacity and debt persisted throughout the 19th and early 20th century.

After World War II, the picture reverses: the U.K.’s average post-war fiscal capacity of 82.03% of GDP exceeds its average debt/GDP ratio of 53.42%, leaving more than 50% of fiscal capacity unborrowed. The correlation with debt dynamics persists but the sign changes — U.K. borrowing is now constrained by own macro fundamentals rather than extended by global coordination.

Q3. What do the authors find for the United States?

The U.S. experience mirrors the pre-war U.K. after World War II but with much larger magnitudes: the paper’s estimates indicate that less than one-third (32.20%) of post-war U.S. Treasury debt is backed by future surpluses, with the gap between fiscal capacity and debt growing sharply toward the end of the sample to exceed U.S. GDP. Before World War I, the U.S. did not earn convenience yields — it was forced to borrow at higher rates than the U.K. despite having lower debt-to-output ratios — and its fiscal capacity exceeded its debt, with the ratio of capacity to debt averaging 169.36%. After World War II, when the U.S. became the global safe asset supplier under the Bretton-Woods architecture, the relationship inverted: average U.S. fiscal capacity of 13.20% of GDP represents only 32.20% of outstanding debt. The gap is increasingly large in recent decades as U.S. debt has grown while surplus projections have not expanded commensurately.

Q4. Why does safe asset supplier status allow a country to borrow beyond its fiscal capacity?

The paper argues that global investors coordinate on a single safe asset issuer based on relative macro fundamentals; this coordination is self-reinforcing because each additional investor holding the asset reduces rollover risk and renders the debt safer for all others, creating strategic complementarities that concentrate global fiscal capacity in one country beyond what its own surpluses would warrant. Unlike domestic convenience yields (arising from household demand for safe assets to insure idiosyncratic risks), the global safe asset effect creates a form of extra-fiscal capacity that depends on investors’ common belief about which country is the hegemon. The measured seigniorage from convenience yields — about 0.47% of U.K. GDP before WW-I and 0.36% per year for the U.S. post-war — does not fully capture this coordination benefit; the remaining gap between fiscal capacity and debt reflects the additional “license to borrow” that comes with global hegemon status.

The transition from U.K. to U.S. hegemony illustrates the mechanism: as U.K. macro fundamentals deteriorated relative to U.S. fundamentals after the world wars, investors shifted the concentration of fiscal capacity toward the U.S. The U.K. lost its license to borrow beyond its fundamentals; the U.S. gained it. The paper notes that the U.K. debt/output ratio exceeded 200% after WW-II — a level associated with the loss of hegemony.

Q5. What historical data and institutional context does the analysis use?

The paper uses annual data for the U.K. from 1729 to 2020 (from the Bank of England’s Millennium of Macroeconomics dataset and the Ellison-Scott dataset on individual bond market values from 1694 onward) and for the U.S. from 1791 to 2020 (from Hall-Sargent and CRSP), constructing primary surpluses, tax revenues, spending, GDP, and convenience yields consistently over nearly three centuries. U.K. convenience yields before WW-I are measured as the interest rate differential between U.K. government securities and otherwise comparable bonds from countries on the gold standard; the sample average is approximately 147 basis points at the short end and 110 basis points at the long end, with the spread declining at longer maturities (the opposite of what default risk would predict), providing evidence that convenience yield rather than residual default risk drives the differential. U.S. post-war convenience yields are constructed from the spread between the 3-month Treasury yield and the 3-month CD rate (or bankers’ acceptance rate before 1964), averaging 36 basis points per year from 1947 to 2020.

Q6. What are the implications for models of fiscal capacity and debt sustainability?

The results favor models in which the safe asset supplier’s fiscal capacity is determined partly by relative macro fundamentals (which country the global financial system coordinates on) rather than solely by absolute fundamentals (its own surpluses), and challenge models that treat the transversality condition as a binding constraint at all times for all countries. The finding that a large fraction of U.S. Treasury debt is not backed by future surpluses — even when the market risk premium is used to discount — has implications for debt sustainability analyses: standard present-value-of-surpluses calculations will understate the true fiscal capacity of the safe asset supplier, while overstating it for others. The paper’s framework suggests this extra capacity depends on maintaining relative macro fundamentals and global investor coordination, and can be lost — as the U.K. experience demonstrates — when relative fundamentals deteriorate.

Key Concepts

fiscal capacity: the present risk-adjusted discounted value of a government’s expected future primary surpluses, computed from the government’s intertemporal budget constraint under no-arbitrage; in this paper, inclusive of seigniorage revenue from convenience yields earned on government debt.
exorbitant privilege: the ability of the safe asset supplier country to borrow at below-market interest rates due to global demand for its government debt as a safe asset; quantified in this paper as the gap between the market value of debt and estimated fiscal capacity from surpluses alone, which exceeds fiscal capacity for the pre-WW-I U.K. and post-WW-II U.S.
convenience yield: the yield reduction (below comparable risky borrowing rates) that the safe asset supplier earns from global safe asset demand; measured as approximately 100 bps long-term for the pre-WW-I U.K. and approximately 36 bps on average for the post-WW-II U.S., contributing 0.47% and 0.36% of GDP annually in seigniorage revenue respectively.
transversality condition (TVC): the condition ruling out rational government debt bubbles, requiring the expected discounted value of outstanding debt to approach zero at long horizons; the paper imposes the TVC and finds that for the pre-WW-I U.K. and post-WW-II U.S., the observed debt level exceeds fiscal capacity even under this constraint, interpreted as evidence of the extra borrowing license conferred by safe asset supplier status.

Identifying Preference for Early Resolution from Asset Prices

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

Layer 1: Overview

This paper develops a revealed-preference theory that uses asset-market data to identify whether investors have a preference for early resolution of uncertainty (PER), a property of non-expected utility preferences that is distinct from risk aversion. The central theorem shows that, under a condition called generalized risk sensitivity (GRS), the representative agent prefers early resolution if and only if claims to future stock market volatility earn a positive premium during the period in which the informativeness of upcoming macroeconomic announcements is resolved — a window the authors call the Resolution of Information Quality (ROIQ) period. Using S&P 500 index option data from 1996 to 2019, the paper identifies the ROIQ period as the five weekdays before FOMC announcements, demonstrates that the inverse slope of the implied-volatility term structure (9-day/90-day VIX ratio) significantly predicts the informativeness of upcoming announcements, and finds a statistically significant positive ROIQ premium on synthetic variance claims (beta = 1.085, t = 2.44) and on at-the-money straddles (beta = 0.428, t = 2.25). The evidence supports Epstein-Zin recursive utility with the intertemporal elasticity of substitution exceeding the reciprocal of risk aversion, and hence is consistent with the Bansal-Yaron long-run risk framework. Crucially, this identification requires no parametric calibration of the full asset pricing model.

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

In depth

Q1. What is preference for early resolution (PER) and why is it hard to identify?

PER means that an agent with a given distribution over future outcomes strictly prefers to learn the outcome sooner rather than later, as formalized by Kreps and Porteus (1978); under Epstein-Zin recursive utility, PER is equivalent to risk aversion exceeding the reciprocal of the IES (or IES > 1/risk aversion). In standard applied asset pricing models with constant-elasticity recursive utility, PER is intertwined with risk aversion and the IES, so that the separate role of the timing of resolution is obscured. Existing papers either test joint implications of the full calibrated model (conflating PER with other preference properties) or use thought-experiment willingness-to-pay calculations without market-data grounding. The authors’ goal is to provide a necessary and sufficient condition for PER directly from asset prices, independent of a fully specified model.

Q2. What is the role of Generalized Risk Sensitivity (GRS) in the identification theorem?

GRS — the condition that the certainty-equivalent functional I is increasing in second-order stochastic dominance — provides the bridge between the unobservable ranking of utility levels across states and the observable ranking of marginal utilities (stochastic discount factors) across those states. The authors prove that under GRS (Theorem 1), the vector of partial derivatives of I with respect to continuation utility is strictly negatively comonotone with the level of continuation utility: higher utility states have lower marginal utility. This inversion is what allows asset prices to reveal the ordering of utility levels. GRS itself is empirically supported by the well-documented fact that assets earn positive announcement premia around scheduled macroeconomic releases (Savor and Wilson, 2013).

Q3. How does the main theorem (Theorem 2) identify PER from a single asset class?

Theorem 2 establishes that, under strict GRS, the premium earned by any asset comonotone with the informativeness of upcoming macroeconomic announcements during the ROIQ period is strictly positive if and only if the agent has PER; a negative ROIQ premium would indicate preference for late resolution. The intuition is that if the agent prefers early resolution, she assigns higher continuation utility to the early-resolution state (0E) than to the late-resolution state (0L); under strict GRS, higher continuation utility maps to lower marginal utility, meaning assets paying off more in the early-resolution state are negatively correlated with the SDF and therefore carry a positive risk premium. Claims to stock market return variance serve as the test asset because expected variance is high before informative announcements (early resolution) and low before uninformative ones (late resolution).

Q4. How do the authors operationalize the ROIQ period empirically?

The ROIQ period is identified as the five weekdays before FOMC announcements, during which market attention to the Fed (measured by RavenPack Fed-related news intensity) is significantly positively correlated with the change in the inverse slope of the implied-volatility term structure (coefficient = 1.076, t = 4.09), while no such correlation exists in the ten days 6–10 before or after the announcement. This correlation arises because, during those five days, investors regularly update their expectations about whether the upcoming FOMC statement will be informative; more expected informativeness raises the demand for short-dated options (driving up the 9-day VIX relative to the 90-day VIX) and simultaneously raises Fed-related news coverage. Outside the ROIQ window, the two series are uncorrelated (coefficient = −0.242, t = −1.13 unconditionally), confirming that the window is the correct testing period.

Q5. What is the empirical evidence for a positive ROIQ premium, and how is it constructed?

Synthetic variance claims constructed as option portfolios following Bakshi, Kapadia, and Madan (2003) earn a ROIQ premium (coefficient beta in the panel regression) of 1.085 percentage points per day (t = 2.44) above their average daily return; at-the-money straddles earn 0.428 pp/day (t = 2.25), both significantly positive. The panel regression controls for maturity fixed effects (11 dummies for weeks to expiration), FOMC-day effects, and day-of-week effects. Crucially, the market itself earns approximately 8 basis points lower than average during the ROIQ period, and the market loading on variance claims does not increase during the ROIQ window (Table 5), ruling out an interpretation in which the premium simply reflects a higher market beta at announcement times.

Q6. How does the paper rule out alternative explanations for the ROIQ premium?

A placebo test using VIX futures — which pay the forward-looking VIX level (expected volatility over the next 30 days after expiry) rather than realized variance over the announcement — shows no significant ROIQ premium, confirming that the effect operates specifically through exposure to volatility during the announcement itself rather than through general volatility-level exposure. The paper also shows that controlling for the Fama-French three factors does not appreciably change the ROIQ coefficient. An additional test using individual stock options (5 weekdays before earnings announcements) also yields positive ROIQ premiums, extending the result beyond FOMC to firm-level announcements.

Q7. What does the finding imply for macroeconomic preference modeling and policy?

The empirical finding that investors have a positive ROIQ premium — i.e., PER — without assuming any particular utility functional form confirms the central calibration assumption of Bansal-Yaron long-run risk models (risk aversion > 1/IES) and provides the market-based evidence that Epstein, Farhi, and Strzalecki (2014) stated was unavailable. The paper’s approach is significant for macro modeling because it establishes PER from minimal assumptions (GRS and monotonicity of preferences), meaning that the result holds across expected utility deviations including robust control, smooth ambiguity, and disappointment aversion preferences — as long as they satisfy GRS — making it a broadly applicable empirical anchor for calibrating non-expected utility models.

Q8. What are the identification limitations and scope conditions?

The identification relies on three maintained conditions: (i) GRS holds for the representative agent, (ii) FOMC announcements genuinely resolve macro uncertainty (so that the ROIQ window is correctly specified), and (iii) the pre-announcement period does not contain price-relevant news (so that market return premia during the ROIQ are not confounded with the news content of the announcement itself). The empirical support for condition (iii) comes from the fact that the market does not earn abnormal returns during the ROIQ (negative, not positive, as expected from the announcement drift literature), and from the lack of a ROIQ premium for VIX futures that expire after but not over the announcement. The framework abstracts from heterogeneous agents and assumes a representative-agent economy, which is standard but may not fully capture distributional effects.

Key Concepts

preference for early resolution of uncertainty (PER) : the property of a dynamic preference that the agent strictly prefers to learn the realization of a future uncertain outcome earlier rather than later, holding the distribution unchanged; equivalent in Epstein-Zin recursive utility to risk aversion exceeding the reciprocal of the IES.

generalized risk sensitivity (GRS) : the condition that the certainty-equivalent functional I is strictly increasing in second-order stochastic dominance; equivalent to the existence of strictly positive announcement premia for all assets comonotone with continuation utility; the paper’s key maintained assumption connecting utility levels to asset prices.

resolution of information quality (ROIQ) period : the period during which investors learn whether the upcoming macroeconomic announcement will be informative; empirically identified as the five weekdays before FOMC meetings, during which Fed-related news intensity co-moves with the inverse slope of the VIX term structure.

ROIQ premium : the excess return earned by a claim to market volatility (synthetic variance claim or straddle) during the ROIQ period over its average daily return on non-ROIQ days; the paper’s operational test for PER; estimated at 1.085 percentage points per day for variance claims.

inverse slope of the implied-volatility term structure : the ratio IV9/IV90 (9-day CBOE VIX divided by 90-day CBOE VIX); the paper’s market-based predictor of FOMC announcement informativeness; a higher ratio reflects investor anticipation of large announcement-day volatility relative to long-run baseline uncertainty.

The Zero-Beta Interest Rate

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

Layer 1: Overview

This paper proposes and measures the zero-beta rate — the expected return on a portfolio of stocks with zero market beta, constructed to be orthogonal to the SDF innovations spanned by standard factors — as the correct intertemporal price of consumption, and argues that safe interest rates (Treasury bill yields) are not. Using 130 stock portfolios (81 sorted on combinations of beta, size, value, investment, and profitability; 49 industry portfolios) and GMM estimation with five macro instruments (T-bill yield, inflation, term spread, excess bond premium, and the U6 unemployment rate) over January 1973 to December 2020, the paper estimates the zero-beta rate to average 8.3% per year in real terms with a standard deviation of 9.3%, producing a spread of roughly 7.6% per year over the expected real Treasury bill yield. The paper then shows that this zero-beta rate fits the aggregate consumption Euler equation remarkably well: the macro instruments that best predict the real return of the zero-beta portfolio are nearly proportional to those that predict real consumption growth, a non-mechanical result that survives when the sample is restricted to exclude COVID. Statistical Euler equation tests (Stock-Wright [2000] weak-instrument-robust GMM) reject the Euler equation for all IES values when applied to the Treasury bill, fail to reject it for any IES value when applied to the volatile market return (weak identification), but fail to reject it only for IES below 0.5 (risk aversion above 2) when applied to the zero-beta rate — providing identification from the intermediate predictability of the zero-beta portfolio. Monetary policy shock regressions using Romer-Romer and Nakamura-Steinsson shocks further show that an unexpected monetary tightening raises the real Treasury bill yield but lowers the real zero-beta rate, consistent with the Euler equation’s prediction that the intertemporal price should fall when expected consumption growth falls. Finally, the high level and volatility of the zero-beta rate implies that the entire variation of the price-dividend ratio of a consumption claim can be attributed to variation in the zero-beta rate without requiring time-varying equity risk premia — resolving the equity premium puzzle and Campbell’s [1991] excess volatility puzzle simultaneously, at the cost of an unexplained convenience spread on safe assets.

In depth

Q1. What is the zero-beta rate and how does the paper construct it?

The zero-beta rate is the expected return on a portfolio of stocks that is constructed to have zero covariance with all included asset-pricing factors; in a model where the SDF innovations are spanned by those factors, the expected return of this portfolio equals the intertemporal marginal rate of substitution — the correct price for rearranging consumption over time. The paper follows a three-step procedure. First, it estimates the betas of 130 CRSP stock portfolios with respect to seven factors (Fama-French 5-factor model augmented with a bond excess return factor and a default spread factor) using the Ledoit-Wolf (2017) shrinkage estimator for the factor covariance matrix, to mitigate the rank problem arising from 130 portfolios and 574 monthly observations. Second, it uses the betas to construct the minimum-variance zero-beta portfolio — the portfolio that minimizes return variance subject to having zero exposure to each factor, exploiting all 130 portfolios. Third, it regresses the return of this portfolio on five macro instruments (T-bill yield, lagged inflation, term spread, excess bond premium [EBP], and U6 unemployment) using GMM, in an exactly-identified system in which the same instruments used to predict the portfolio return are used as moment conditions. The fitted value of this regression is the zero-beta rate — the predictable component of the zero-beta portfolio return.

The key econometric property is that the GMM procedure simultaneously estimates the factor loadings and the predictive regression in a way that accounts for the estimation error in both. The standard errors for the predictive coefficients (γ) account for the fact that the betas used to construct the zero-beta portfolio are themselves estimated. Comparatively, an infeasible OLS regression predicting the zero-beta portfolio return has nearly identical point estimates and only slightly smaller standard errors, showing that the main estimation uncertainty comes from the predictive regression rather than from the beta estimation step.

Q2. What are the key properties of the estimated zero-beta rate?

The estimated zero-beta rate has three properties that distinguish it from safe rates and make it a plausible intertemporal price: it is predictable by macro instruments (with all predictors except the unemployment rate individually significant at 5%), it is high and volatile (8.3% annually on average, std dev 9.3%, compared to a low and stable Treasury bill yield), and it co-moves in the direction economic theory predicts with macro conditions. The spread between the zero-beta rate and the expected real Treasury bill yield averages roughly 7.6% per year. The average real zero-beta rate is similar to the average real return of the CRSP market index (which averages 11.8% annualized nominal, or about 8.1% real), consistent with earlier estimates of the average zero-beta return by Hong and Sraer (2016) and Bali et al. (2017). The standard deviation of the zero-beta portfolio’s excess return over its expected value is about 2.7% per month (9.4% annualized), substantially below the standard deviation of the market return, which is why the paper can reject predictability for the zero-beta portfolio even though predicting market returns is notoriously difficult.

In terms of time-series patterns: (1) the zero-beta rate increases more than one-for-one with the Treasury bill yield (consistent with Treasury bills having money-like qualities per Nagel [2016]); (2) it is decreasing in lagged inflation; (3) it falls when a recession is likely — specifically, the unemployment rate, term spread, and EBP collectively predict the zero-beta rate so that it is particularly low when U6 unemployment is low, the yield curve is inverted, and the EBP is high, conditions associated with elevated recession risk (Kiley [2022]).

Q3. How does the zero-beta rate fit the aggregate consumption Euler equation?

The central empirical finding is that the macro instruments that best predict the real return of the zero-beta portfolio are nearly proportional to the macro instruments that best predict real consumption growth — a non-mechanical result because the two regressions are estimated entirely separately, on different data series, with no consumption data used to construct the zero-beta rate. In the linearized Euler equation, the real zero-beta rate should predict real consumption growth in proportion to 1/σ (the IES). If the prediction coefficients on the instruments for the zero-beta rate are γ₀, and the corresponding coefficients for consumption growth are γc, then the vector Δ = γ₀ − σ × γc should be close to zero. Graphically, the expected real zero-beta rate and expected real consumption growth track each other closely over the full sample (Figure 1 in the paper), while the expected real Treasury bill return bears essentially no resemblance to expected consumption growth. With five instruments (L=5), the result is not mechanical: one would need L=1 to always find a σ that makes the result hold. The result is even stronger when the sample ends in December 2019, excluding the COVID episode, because the COVID consumption collapse introduces four-to-seventeen standard deviation consumption growth realizations that attenuate the fit.

Robustness: ridge-penalized estimation (using cross-validation to minimize out-of-sample squared forecast error) substantially attenuates both expected consumption growth and the zero-beta rate toward zero, but they remain approximately proportional. The result holds across alternative specifications (different factor models, different instruments) tested in Appendix Section G.

Q4. What do the statistical Euler equation tests find?

Using Stock-Wright (2000) weak-instrument-robust GMM tests of the non-linear consumption Euler equation, the paper finds: (a) the T-bill return fails the Euler equation test for all IES values (rejected); (b) the market return fails to reject the Euler equation for almost any IES values (weak-instruments problem, consistent with Yogo [2004]); and (c) the zero-beta rate fails to reject the Euler equation for IES values below 0.5 (σ above 2, i.e. risk aversion above 2 under CRRA), and rejects for IES above 0.5. The procedure tests the instrumented non-linear Euler equation: for a conjectured value of σ, it estimates δ (the discount factor) from the unconditional Euler moment, then tests the instrumented Euler moments for each of the five instruments. The test statistic is chi-square with 5 degrees of freedom; the confidence set is the set of σ values that cannot be rejected. The zero-beta portfolio’s intermediate predictability (between the easily-predicted T-bill and the hard-to-predict market return) provides the identification that gives this test meaningful power. The paper obtains its preferred IES estimate of approximately 0.2 (σ ≈ 5) by noting that scaling the zero-beta rate down by a factor of five makes it match expected consumption growth most closely.

The test faces a boundary problem: when σ is very large (above 10), the April 2020 consumption collapse creates a very large SDF realization that dwarfs all others, making the variance-covariance matrix nearly singular and the test uninformative. For this reason, the analysis is restricted to σ ≤ 10.

Q5. What happens to the zero-beta rate after a monetary policy shock?

Using the Romer-Romer (2004) and Nakamura-Steinsson (2018) identified monetary policy shocks, the paper finds that a surprise tightening raises the nominal and real Treasury bill yield but lowers the real zero-beta rate — opposite to what the standard Euler equation with the Treasury bill predicts. Both shocks are normalized to a 100-basis-point increase in the federal funds rate on impact. The estimated effect on the real Treasury bill yield is an immediate increase of roughly the same magnitude (slightly more transitory for the Romer-Romer shock). In contrast, the real zero-beta rate falls following the shock, and the effect is larger and more persistent for the Nakamura-Steinsson shock.

This result is consistent with the consumption Euler equation applied to the zero-beta rate, because the monetary shock also lowers expected consumption growth (well-established in the impulse-response literature). The decomposition in Appendix Section E shows the mechanism: a higher Treasury bill yield raises the zero-beta rate, but the monetary tightening also flattens the yield curve and widens credit spreads (raises the EBP); since both the term spread and EBP are predictors of the zero-beta rate with large coefficients, the indirect effects through these variables dominate and lower the zero-beta rate overall. This finding resolves a tension in structural macro models: while the standard New Keynesian model uses the Euler equation with the safe rate (as in Smets and Wouters [2003, 2007]) and requires habits or wedges to match the hump-shaped consumption response to monetary shocks, the Euler equation with the zero-beta rate is satisfied without additional mechanisms. A stylized three-period New Keynesian model in Appendix Section F shows a monetary tightening can simultaneously raise the safe rate and lower the zero-beta rate through endogenous changes in the convenience spread.

Q6. Can the zero-beta rate explain valuation ratio variation without time-varying risk premia?

The paper shows that the zero-beta rate is sufficiently high, volatile, and persistent to generate the observed variation in the price-dividend ratio of a consumption claim under the assumption of a constant and small equity risk premium, reproducing the finding of Campbell (1991) that discount rates must vary — but attributing the variation to the zero-beta rate rather than to time-varying excess returns. The Campbell-Shiller decomposition of the log price-dividend ratio implies that the expected price-dividend ratio must predict either future real zero-beta rates, future excess returns, or a combination. Under the hypothesis of constant expected excess returns, the price-dividend ratio variation is driven entirely by zero-beta rate variation. In a VAR that includes the five macro instruments plus the CAPE ratio, the implied standard deviation of expected log price-dividend ratio of a consumption claim is approximately 28% — comparable to the 27% standard deviation in the Campbell-Cochrane (1999) model calibration, which achieves this variation through habit formation and time-varying risk premia. The equivalent calculation using the Treasury bill yield rather than the zero-beta rate produces a standard deviation of only 9%, consistent with Campbell’s (1991) original finding that the risk-free rate variation is insufficient.

The paper interprets this as a resolution of both the equity premium puzzle (the average zero-beta return is approximately equal to the average market return, implying a roughly zero equity premium over the zero-beta rate) and the excess volatility puzzle (the zero-beta rate generates sufficient variation in the discount rate). The trade-off is that the unexplained spread between the zero-beta rate and the Treasury bill yield — averaging 7.6% annually — is instead left as a “convenience puzzle.” The paper argues this reframing is progress because the type of models required to explain a large convenience spread on safe assets (frictions, segmented markets, money-like demand for liquid assets) are quite different from those designed to explain large time-varying risk premia.

Key Concepts

zero-beta rate: the expected return on the minimum-variance portfolio of stocks that has zero covariance with each of the included asset-pricing factors; in a model where the SDF innovations are spanned by those factors, equals the conditional expectation of the intertemporal marginal rate of substitution, making it the correct intertemporal price of consumption; measured in this paper at 8.3% annually on average using GMM on 130 CRSP stock portfolios, January 1973–December 2020.
convenience spread: the gap between the zero-beta rate and the expected real Treasury bill yield, averaging approximately 7.6% per year in this paper’s estimates; interpreted as the non-pecuniary value that holders of safe assets (Treasury bills and equivalents) receive from liquidity, collateral, and money-like services — not an expected excess return relative to consumption risk but a departure from the risk-return tradeoff for investors who value safety and liquidity independently of consumption hedging.
SDF-orthogonal equity portfolio: the minimum-variance portfolio constructed by the paper to have zero covariance with all seven asset-pricing factors (Fama-French 5 plus bond and default factors); the portfolio whose expected return equals the zero-beta rate because, by construction, no factor risk premium enters its expected return; estimated using the Ledoit-Wolf (2017) shrinkage estimator applied to 130 stock portfolios to address the rank problem in beta estimation.
Euler equation failure with safe rates: the empirical finding that the expected real Treasury bill return does not co-move with expected real consumption growth — the standard failure documented by Hansen-Singleton (1983), Dunn-Singleton (1986), and Yogo (2004) — which the paper reinterprets not as a structural failure of the representative agent model but as a consequence of using the wrong interest rate; the same Euler equation holds when applied to the zero-beta rate, which the paper argues is the correct intertemporal price.

US Public Debt and Safe Asset Market Power

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

Layer 1: Overview

This paper asks whether the U.S. government exploits its market power as the dominant global supplier of safe assets when setting the quantity of public debt, and quantifies the macroeconomic consequences of this strategic behavior. The paper develops a two-country general equilibrium model in which U.S. public debt provides a non-pecuniary benefit to foreign holders (capturing liquidity, collateral, and safety value) and the U.S. is the monopoly provider of this asset — facing a downward-sloping demand curve for Treasuries, so that issuing more debt reduces the convenience yield. The paper then tests empirically whether the data favor this monopoly model over a price-taking benchmark, exploiting the industrial organization insight that rotations in the demand curve (changes in elasticities during high- versus low-volatility regimes) can distinguish strategic from competitive behavior. Using quarterly data from 1935 to 2020, the paper finds that the data reject price-taking behavior in favor of the monopoly model across a wide range of specifications. Quantitatively, the monopoly calibration implies that U.S. market power generates approximately 45% of the observed convenience yield as a markup (about 30 basis points out of 68 basis points on average), causes safe asset supply to be roughly half what it would be under price-taking, and generates welfare gains to the U.S. of 0.21% in permanent consumption equivalents — almost half of which is attributable to market power rather than to the non-pecuniary value itself.

In depth

Q1. What is the theoretical framework for U.S. market power in safe assets?

The paper develops a deterministic, infinite-horizon, two-country model in which the U.S. is the sole provider of an asset with a non-pecuniary benefit to foreign (Rest of World) households, so the U.S. faces a downward-sloping demand curve for its public debt and acts as a monopolist in equilibrium. In the model, purchasing U.S. public debt yields a non-pecuniary flow benefit captured by an increasing, concave function f(b*). Because of this benefit, the equilibrium return on U.S. debt is lower than the return on capital — the gap being the convenience yield, defined as the spread between the U.S. capital return and the return on U.S. public debt. The U.S. Ramsey government internalizes the inverse demand function for its debt when solving its optimal fiscal problem, creating a standard monopoly markup: the equilibrium markup equals the inverse of the demand elasticity, µ = 1/ε_D, where ε_D is the price elasticity of foreign demand for U.S. Treasuries. Under price-taking, the markup is zero and the convenience yield reflects only the non-pecuniary value; under the monopoly model, the convenience yield is inflated by the markup, reducing debt supply below the competitive level.

Q2. How does the paper test monopoly versus price-taking behavior empirically?

The paper applies the conduct-testing approach of Bresnahan (1982) and the model selection test of Rivers and Vuong (2002): since rotations in the demand curve (changes in the elasticity, holding the level fixed) shift prices only if the firm exploits market power, a finding that convenience yields increase in high-elasticity regimes while quantities decrease is evidence of strategic behavior. The paper uses a regime indicator for periods of high global volatility (measured by the rolling standard deviation of MSCI UK Index returns over 1935–2020) as the demand rotator: during high-volatility periods, investors’ demand for safe assets is more inelastic (flight-to-safety), causing the demand curve to both shift outward and rotate (become steeper). The monopoly model predicts that the U.S. responds to the more inelastic demand by raising the convenience yield through higher markups and restricting supply, whereas the price-taking model attributes any convenience yield increase purely to shifts in marginal cost.

Empirically, the data show that convenience yields are higher and debt-to-GDP ratios lower during high-volatility periods — inconsistent with the price-taking model’s prediction that both prices and quantities should rise in a demand shift, and consistent with the monopoly model’s prediction of reduced supply. The estimated demand semi-elasticities are −0.20% per log-point in low volatility and −0.59% per log-point in high volatility (OLS), implying demand elasticities of 1.07 in low volatility and 3.18 in high volatility. The Rivers-Vuong test statistics reject price-taking in favor of the monopoly model at the 1% significance level under both OLS and IV specifications and across a wide range of assumed cost elasticities.

Q3. What is the quantitative magnitude of safe asset underprovision?

Using a demand elasticity of 2.2 (the average of the OLS and IV estimates from specifications without the demand rotator, consistent with the prior literature), the paper’s calibrated monopoly model implies that the U.S. safe asset supply is approximately half as large as it would be if the U.S. acted as a price taker: the steady-state total safe assets-to-GDP ratio is 0.39 in the monopoly equilibrium versus 0.59 in the competitive equilibrium. The markup accounts for approximately 45% of the average convenience yield of 68 basis points, implying a markup of about 30 basis points. The interest rate on U.S. public debt is 0.97% in the monopoly equilibrium versus 1.09% in the competitive equilibrium — a difference of 12 basis points — reflecting both the lower debt level and the higher convenience yield that the monopoly generates. These results hold across alternative parameterizations of the cost and demand elasticities.

Q4. What are the welfare implications of safe asset market power?

Market power generates significant welfare gains to the U.S. and welfare losses to the Rest of World: transitioning from the monopoly steady state to an economy with no special role for U.S. assets costs the U.S. 0.21% in permanent consumption equivalents and benefits the Rest of World by 0.34%; transitioning to a competitive equilibrium (price-taking but maintaining the special role) costs the U.S. 0.08% and benefits the Rest of World by 0.10%. This decomposition implies that roughly 60% of the U.S. welfare gain from its safe asset status is attributable to the non-pecuniary value (the benefit function f), and approximately 40% is attributable to market power per se. The interpretation is that the U.S. captures surplus from global safe asset demand through both the intrinsic value of its debt and through monopoly rents from restricting supply. The paper interprets these welfare gains as a quantification of “exorbitant privilege” arising from the supply side rather than from risk premium considerations.

Q5. What happens when safe asset competition increases?

The paper analyzes the effects of introducing Cournot competition among multiple sovereign safe asset suppliers, finding that while the aggregate supply of global safe assets increases substantially with more competitors, the U.S. public debt level itself is fairly stable, borrowing costs for the U.S. increase, and the Rest of World welfare improves. With N=2 symmetric Cournot competitors, the aggregate safe asset supply approximately doubles relative to the monopoly baseline, but each supplier’s equilibrium quantity is roughly unchanged. As N increases further, aggregate supply continues to grow, convenience yields fall, and interest rates on U.S. debt rise. A domestic financial fringe competing with U.S. government debt is modeled differently: because the U.S. government internalizes domestic fringe profits, domestic competition results in less competitive pressure, higher markups, and smaller welfare losses for the U.S. than the same amount of competition from foreign suppliers. These results quantify the macroeconomic stakes of initiatives to create alternative safe assets, such as euro area supranational safe bonds or Chinese reserve currency aspirations.

Q6. What identifies strategic versus competitive behavior using debt holder composition?

As a complementary identification strategy, the paper exploits time variation in the composition of U.S. Treasury holders: foreign investors (primarily official sector) have more inelastic demand than domestic investors (primarily financial institutions and mutual funds), and the increasing share of foreign investors since the 1970s implies a secular decline in the average demand elasticity. The paper estimates demand elasticities separately for the two groups, finds the foreign investor curve is more inelastic, and uses the implied time-varying average elasticity as a second demand rotator. The conduct test under this alternative approach also rejects price-taking in favor of the monopoly model. The monopoly model explains the observed increase in long-term convenience yields since the 1970s through rising markups driven by the shift toward less elastic foreign investors, rather than through rising marginal costs of debt issuance.

Key Concepts

safe asset market power: the U.S. government’s ability to internalize the downward-sloping foreign demand curve for U.S. Treasuries and restrict supply to maintain a high convenience yield; the paper provides the first formal empirical test and quantification of this strategic behavior in the global safe asset market.
convenience yield markup: the component of the observed convenience yield on U.S. Treasuries attributable to monopoly pricing rather than to the intrinsic non-pecuniary value of the assets; estimated at approximately 45% of the total convenience yield (about 30 out of 68 basis points) under the paper’s baseline demand elasticity of 2.2.
demand rotator (Bresnahan identification): a variable that changes the elasticity of demand without shifting its level, enabling identification of strategic conduct: observing that prices rise and quantities fall when demand becomes more inelastic (as during high-volatility regimes) is evidence of monopoly pricing, since a price taker would not respond to an elasticity change alone.
safe asset underprovision: the quantity distortion from monopoly pricing in the global safe asset market; the paper estimates the steady-state safe-asset-to-GDP ratio is approximately 50% lower in the monopoly equilibrium than in the competitive benchmark, reflecting the standard monopoly restriction of output to exploit the downward-sloping demand curve.