Abstract: Off-balance sheet foreign exchange (FX) swaps are a major source of US dollar funding for non-US banks that provide over half of global dollar credit. However, the frictions that lead banks to rely on these instruments and their broader impact on the financial system are not well understood. This paper shows that FX swaps emerge as alternative ("synthetic") funding instruments when banks face negative funding shocks from cash-market investors, such as US money market funds. The resulting increase in swap demand, combined with limits to arbitrage, leads to substantial deviations from covered interest parity (CIP) – the breakdown of a fundamental no-arbitrage pricing condition. I show a causal impact of banks’ swap demand on CIP deviations using an instrumental variables strategy that exploits idiosyncratic variation in money market funds’ investment in bank-level debt. This shift in aggregate demand is absorbed by non-bank users of FX derivatives in the form of higher hedging costs: I estimate the elasticity of non-bank investors' hedging demand to swap prices and find only a partial adjustment in quantities traded. My results indicate that frictions in the global market for the US dollar can provide a demand-based explanation for CIP deviations.
The Market for Sharing Interest Rate Risks: Quantities and Asset Prices
(with Jian Li, Ioana Neamtu, Ishita Sen)
Abstract: We study interest rate risk sharing across the financial system using novel data on cross-sector interest rate swap positions. We show that pension funds and insurers (PF&I) are natural counterparties to banks and corporations: PF&I buy duration, whereas banks and corporations sell duration. However, demand is highly segmented across maturities, resulting in significant imbalances at various maturity points. We calibrate a preferred-habitat investors model with risk-averse arbitrageurs to study how demand imbalances interact with supply side constraints to impact swap spreads. Our framework helps quantify the spillover effects of demand shifts, which informs policy discussions on financial institutions’ hedging requirements.
Uninformed yet Consequential: Liquidity Shocks in FX Markets
(with Petra Sinagl)
Abstract: We study how retail liquidity shocks impact prices and volumes in the foreign exchange (FX) spot market. We model risk-averse dealers' accumulation of inventory under asymmetric information and incomplete offset across retail clients. Our model predicts that retail liquidity shocks result in inventory imbalances that are transmitted to the inter-dealer segment, increasing price volatility and trading volumes. Using month-end settlement breaks to instrument for uninformed order flow, we empirically validate these predictions: a one-standard-deviation rise in retail net volume increases volatility by 12-22% and inter-dealer volume by 10%, indicating that liquidity-driven demand interacts with intermediary constraints to determine asset prices.
Unemployment Insurance Fraud in the Debit Card Market
(with Jetson Leder-Luis, Jialan Wang, Yunrong Zhou. NBER working paper # 32527)
Abstract: We study fraud in the unemployment insurance (UI) system using a dataset of 35 million debit card transactions. We apply machine learning techniques to cluster cards corresponding to varying levels of suspicious or potentially fraudulent activity. We then conduct a difference-in-differences analysis based on the staggered adoption of state-level identity verification systems between 2020 and 2021 to assess the effectiveness of screening for reducing fraud. Our findings suggest that identity verification reduced payouts to suspicious cards by 27%, while non-suspicious cards were largely unaffected by these technologies. Our results indicate that identity screening may be an effective mechanism for mitigating fraud in the UI system and for benefits programs more broadly.
Process Innovation and the Corporate Control Market
(with Jon Garfinkel, Amrita Nain)
Abstract: We show that specificity of process innovation affects merger decisions. We measure the composition of a firm’s innovation portfolio by machine-reading 90 million patent claims and show that firms with a higher share of process innovation generate more firm-specific knowledge: they are more likely to cite their own past patents, employ inventors who have more within-firm patenting experience, and exploit technologies already known to them rather than explore new ones. While process innovation is value-enhancing for the stand-alone firm, its specificity reduces synergistic gains from an acquisition. We find robust evidence that process innovators are significantly less likely to be acquired. Consistent with the specificity explanation, the negative effect of process innovation on acquisition likelihood is dampened if the bidder manufactures similar products and, therefore, can apply the target’s innovative processes to its own product line. Our study provides the first large-sample evidence on the fungibility of innovation and its impact on mergers and acquisitions.