Money-Markets-Liquidity | 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.

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.

How Banks Create Gridlock in Payment Systems to Save Liquidity: The Case of Canada

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

This paper uses detailed transaction-level data from Canada’s new high-value payment system (HVPS) to show how participants save liquidity by strategically exploiting the gridlock resolution arrangement built into the system. Observed behaviors are found to be consistent with the equilibrium of a “gridlock game” that captures the key incentives participants face: by withholding outgoing payments to induce gridlock events, participants trigger the system’s bilateral netting algorithm, which settles stuck payment queues at lower liquidity cost than bilateral sequential settlement would require. The findings have implications for the design of high-value payment systems and shed light on financial institutions’ liquidity preference in payment system environments.

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

In depth

Q1. What is the gridlock resolution arrangement and why do banks exploit it?

Modern high-value payment systems (HVPSs) include a gridlock resolution mechanism that activates when a set of payments are mutually stuck in queues—each waiting for an incoming payment before it can be sent—and resolves them simultaneously via bilateral netting, which requires less settlement liquidity than sequential settlement; banks strategically withhold outgoing payments to trigger these events and thereby save liquidity. The HVPS studied is Canada’s new large-value transfer system, which replaced the older LVTS. The gridlock game captures the incentive structure: if a bank expects counterparties to send payments that would be netted against its own obligations in a gridlock, it is optimal to withhold and wait rather than settle bilaterally at higher liquidity cost.

Q2. How is the gridlock game formalized?

The “gridlock game” is a formal game-theoretic model that captures the key incentives participants face in the HVPS: players choose whether and when to send payments, and the equilibrium characterizes the strategic withholding behavior as a rational response to the liquidity-saving opportunities created by the gridlock resolution mechanism. The equilibrium of this game is shown to be consistent with the actual patterns observed in the HVPS data: the timing, magnitude, and counterparty structure of strategic withholding are aligned with the game’s equilibrium predictions.

Q3. What are the implications for HVPS design?

The finding that participants strategically exploit the gridlock resolution mechanism has implications for HVPS design: while gridlock resolution was intended as an exception-handling mechanism for unintended payment queue build-ups, participants have adapted to use it as a routine liquidity management tool, changing the system’s effective operation in ways the designers may not have anticipated. System designers must account for the strategic response of sophisticated participants when evaluating the performance of gridlock resolution mechanisms, since the equilibrium behavior changes the frequency, timing, and magnitude of gridlock events relative to the non-strategic benchmark.

Q4. What does the evidence reveal about banks’ liquidity preferences?

The strategic gridlock behavior reveals that financial institutions place significant value on conserving payment system liquidity—enough to coordinate timing of payment submissions in ways that exploit system-level netting opportunities—consistent with liquidity being a scarce and valuable resource in modern payment systems. This preference for liquidity conservation is amplified in environments where central bank reserves are costly and where payment system participants face collateral or reserve constraints.

Key concepts

gridlock in high-value payment systems : a situation in which a set of payments are mutually stuck in queues—each waiting for incoming funds before outgoing payment can be made—requiring the system’s bilateral netting algorithm to simultaneously settle them; exploited strategically by banks to save settlement liquidity. gridlock game : the paper’s game-theoretic model of strategic payment submission timing in an HVPS; captures the incentive to withhold outgoing payments to trigger gridlock resolution events that settle payment queues at lower net liquidity cost. bilateral netting in HVPS : the gridlock resolution mechanism that settles multiple mutually stuck payments by computing net obligations among participants and settling only the differences; requires less total settlement liquidity than sequential bilateral settlement and is the mechanism banks exploit in the gridlock game.

The Liquidity of the Government Bond Market — What Impact Does Quantitative Easing Have? Evidence from Sweden

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

This paper studies the effect of quantitative easing on government bond market liquidity using Swedish data. The central finding is that QE is associated with a deterioration in market liquidity levels, driven by a scarcity effect: as the central bank removes bonds from the market, available supply thins, widening bid-ask spreads and reducing depth. This scarcity effect is nonlinear — it dominates only when QE reaches a sufficient volume, after which the reduction in tradeable bond supply outweighs the demand-boosting effect of QE on prices. Market liquidity does not respond to QE announcements; the liquidity effect materializes only when actual purchases occur, distinguishing the liquidity channel from the pricing channel.

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

In depth

Q1. Why does QE reduce bond market liquidity?

QE removes government bonds from the secondary market into the central bank’s balance sheet, reducing the free float available for trading and thinning the order book; once the volume of purchases crosses a threshold level, this scarcity of tradeable bonds outweighs the positive liquidity effect from increased market demand, causing a net deterioration in liquidity measures. The nonlinearity implies there is a regime change: small QE programs may improve liquidity by boosting demand, but large programs that substantially reduce the available bond supply tip the balance toward scarcity-driven deterioration.

Q2. What explains the announcement-purchase difference?

Bond prices respond to QE announcements because forward-looking pricing incorporates expected changes in future supply and demand immediately; bond market liquidity, however, depends on the actual quantity of bonds available for trading, which only changes when purchases occur rather than when they are announced. This split behavior — prices react to news, liquidity reacts to quantities — suggests the liquidity channel is driven by the physical supply of tradeable bonds rather than by forward-looking asset price dynamics, and implies that liquidity costs of QE materialize gradually over the purchase period rather than at announcement.

Key concepts

scarcity effect : the reduction in government bond market liquidity caused by the central bank withdrawing bonds from the secondary market through QE purchases; the dominant mechanism when QE volume is large.

announcement vs. purchase channel : the distinction between QE effects on asset prices (which materialize at announcement) and QE effects on market liquidity (which materialize only at actual purchase); reflects the difference between forward-looking pricing and quantity-driven liquidity.