Safe-Assets-Convenience-Yield | Macro Paper Warehouse

Bank Opacity and Safe Asset Moneyness

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

This paper studies when a bank is more effective as a supplier of privately produced money-like safe assets (repo, commercial paper), finding that a bank produces safer, more liquid assets when (1) its return on equity (ROE) is relatively lower, and (2) it is relatively more opaque about its balance sheet. A three-period model is presented in which safe asset investors focus on the left tail of the bank asset value distribution that ultimately determines the debt’s moneyness: a higher ROE signals riskier investment activities with higher return volatility, exposing investors to greater left-tail risk and lowering the moneyness of the bank’s debt. Bank opacity mitigates the strength of the ROE-moneyness relationship because opacity limits investors’ ability to infer asset risk, making it optimal for the banking system to maintain a certain level of opacity. Empirical tests on dealer banks and money market mutual funds’ (MMFs) funding relationships confirm that higher ROE leads to MMF withdrawal due to lower moneyness of safe assets.

Summary based on a working paper version, AI-assisted and human-reviewed. See the linked published article for the authoritative version.

In depth

Q1. Why does higher ROE lower the moneyness of a bank’s safe assets?

Higher ROE signals that a bank is more likely to be engaging in riskier investment activities with higher return volatility, which exposes safe asset investors—who care almost entirely about the left tail of the bank asset value distribution—to a higher likelihood of complete insolvency, lowering the moneyness of the bank’s debt. The intuition is asymmetric: for a debt holder, the upside is limited to the contracted interest rate, while the downside involves potential total loss if the bank becomes insolvent. A higher ROE thus signals higher left-tail risk rather than higher credit quality from the safe asset investor’s perspective, contradicting the positive signal that higher ROE sends to equity investors.

Q2. How does the model formalize the moneyness concept?

In the three-period model, the bank issues a money-like safe asset (deposit) to finance itself, and the household holds it both to transfer wealth intertemporally and to use it as a medium of exchange; moneyness captures both the safety and the liquidity of the asset as experienced by the holder. The model embeds the Gorton-Pennacchi (1990) and Dang-Gorton-Holmström (2012) notion that money-like assets are purposefully designed to be information-insensitive, so that investors have little incentive to acquire private information about them. The model shows how ROE—a piece of public information—nonetheless predicts moneyness and triggers withdrawal.

Q3. Why is bank opacity an equilibrium feature that improves moneyness?

Bank opacity mitigates the predictive power of ROE for the moneyness of safe assets because if investors cannot observe detailed information about the bank’s asset side, they cannot fully infer the riskiness of the investments backing the bank’s debt from the ROE signal, making it optimal for the banking system to maintain a certain level of opacity to preserve the information-insensitive character of its safe assets. This result is consistent with Dang et al. (2017)’s argument that banks are intentionally opaque: opacity is not merely a byproduct of complexity but a deliberate design feature that preserves the moneyness of privately produced safe assets.

Q4. What is the empirical evidence using MMF and dealer bank data?

Empirical tests using data on MMF funding of dealer banks confirm that higher bank ROE leads to MMF withdrawal from the bank, consistent with the model’s prediction that higher ROE reduces the moneyness of the bank’s safe assets for institutional investors; the relationship is attenuated for more opaque banks, consistent with the model’s opacity mechanism. The wholesale banking sector (dealer banks and institutional investors like MMFs) is the natural testing ground because its participants are more informed than retail depositors and therefore more sensitive to signals about the riskiness of the assets backing the bank’s debt.

Key concepts

moneyness of safe assets : the degree to which a financial asset is safe and liquid—traded at par with no questions asked; determined in this paper by how well a bank’s debt protects investors against the left tail of the bank asset value distribution. return on equity (ROE) as a risk signal : the paper’s key insight that, for safe asset investors (debt holders), higher bank ROE signals riskier investments with higher return volatility rather than lower credit risk; this contrasts with the positive signal ROE sends to equity investors. information-insensitive safe asset : a financial asset purposefully designed to be immune to private information acquisition by investors (Gorton-Pennacchi 1990; Dang et al. 2012); bank opacity preserves this property by limiting investors’ ability to infer asset-side risk from public signals.

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.

Hedge funds and the Treasury cash-futures basis trade

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

The U.S. Treasury market is the deepest and most liquid fixed-income market in the world, yet in March 2020 it experienced unprecedented dysfunction—widening bid-ask spreads, skyrocketing repo rates, and diverging arbitrage spreads that prompted massive Federal Reserve intervention. This paper documents the rise and near-collapse of the Treasury cash-futures basis trade—an arbitrage strategy among hedge funds exploiting a persistent disconnect between cash Treasury prices and futures prices—as a central feature of that episode. Using regulatory datasets on hedge fund exposures and repo transactions, the authors show that at its peak the basis trade accounted for an estimated $400–$500 billion in positions, constituting more than 60% of total hedge fund Treasury exposure, more than 70% of hedge fund repo borrowing, and more than 25% of primary dealers’ repo lending. A model and empirical evidence link the trade’s growth after 2016 to broader Treasury market developments, and show how the trade’s reliance on short-term repo financing creates both margin risk and rollover risk. In March 2020 many of these risks materialized, though the unwinding of basis positions was likely a consequence rather than the primary cause of the stress; prompt Federal Reserve intervention may have prevented a liquidity spiral.

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 Treasury cash-futures basis trade and why did it become popular?

The basis trade exploits the arbitrage relationship Pₜ,τ = ΣBₜ,ₛcₛ + Bₜ,T Fₜ,τ,T: when futures prices are too high relative to the present value of the deliverable bond, traders go “long the basis” by buying the cash bond and shorting the futures, financing the long position in the overnight repo market. The trade became popular following 2016 as demand for long Treasury futures positions grew (from institutional investors seeking leveraged duration exposure) while the supply of warehousing capacity from dealers contracted under post-crisis regulatory constraints. Hedge funds stepped in as the marginal warehouser, exploiting the resulting premium embedded in futures prices. The trade is nearly zero net-cash but requires continuous repo rollover.

Q2. How large did the trade become and how was its size estimated?

Using regulatory data—specifically CFTC Form 40 (hedge fund futures positions), SEC Form PF (AUM and derivatives exposures), and FR 2004 (primary dealer repo data)—the authors estimate basis trade positions peaked at $400–$500 billion, comprising more than 60% of hedge fund Treasury exposure, more than 70% of hedge fund repo borrowing, and more than 25% of primary dealer repo lending to hedge funds. The data allow the authors to identify basis positions directly, distinguishing them from outright long Treasury positions, by matching the simultaneous long cash / short futures pattern that defines the trade. The estimates underscore that hedge funds had become systemically important participants in Treasury market intermediation.

Q3. What financial stability risks does the basis trade create?

The basis trade creates two interrelated risks: margin risk (variation margin calls on futures positions can force immediate liquidation) and rollover risk (if repo lenders withdraw funding, the cash Treasury position must be sold). The paper’s model formalizes how limits to arbitrage—specifically repo market illiquidity and margin requirements—impair risk-sharing between dealers and holders of long futures positions. These constraints mean that even a moderate adverse price move can trigger a self-reinforcing cycle: higher basis volatility → margin calls → forced sales → further basis widening → further margin calls.

Q4. What happened in March 2020 and what was the Federal Reserve’s role?

Beginning in early March 2020, the COVID-19 pandemic triggered a “dash for cash” that disrupted Treasury market functioning: bid-ask spreads widened dramatically, repo rates spiked, and the cash-futures basis moved sharply against basis traders, generating large margin calls. The authors find that while Treasury market disruptions spurred hedge funds to sell Treasuries, the unwinding of the basis trade was likely a consequence rather than a primary cause of the stress. The Federal Reserve intervened by dramatically expanding Treasury purchases from dealers and offering unlimited repo and reverse repo facilities, which likely prevented a liquidity spiral by removing the constraint on dealer intermediation capacity. The paper argues this episode highlights structural vulnerabilities in Treasury market intermediation arising from the shift of warehousing capacity to hedge funds.

Key concepts

Treasury cash-futures basis trade : an arbitrage strategy in which a trader simultaneously holds a long position in cash Treasury bonds (funded via repo) and a short position in Treasury futures, profiting from the convergence of cash and futures prices at delivery.

warehousing role of hedge funds : the function of holding Treasury bonds on behalf of institutional investors who want long futures exposure, financed in the repo market; this creates a link between Treasury, futures, and repo markets and exposes the system to repo rollover and margin risk.

rollover risk : the risk that short-term repo lenders decline to roll over funding at maturity, forcing the borrower to sell the collateral asset (Treasury bonds) at potentially distressed prices.

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.