Working papers

Thinking Forward: FX Hedging, Currency Choice, and the International role of the US Dollar (2021), with M. Fraschini

Our paper studies how foreign-exchange (FX) hedging affects firms’ currency choice, and exchange-rate pass-through. We develop a theoretical model that features dynamic currency choice with incomplete pass-through and limited access to FX derivatives markets. Our model predicts that FX hedging favours foreign currency pricing when firms are risk-averse. We test and quantify these theoretical results by using French product-level data on exports to extra-EU countries and their FX derivatives positions. We find that firms with higher shares of imports in foreign currency are more likely to use local (destination) currency pricing (up to 10% more), when they belong to industries more reliant on FX hedging. This is especially the case for the US dollar as the probability increases up to 23%. Finally, we document that FX hedging firms using local currency pricing have a lower exchange-rate pass-through into export prices even two years after the shock.

Central Bank Digital Currency and Quantitative Easing (2021), with M. Fraschini and L. Somoza

Swiss Finance Institute Research Paper No. 21-25
ABSTRACT:This paper studies how the introduction of a central bank digital currency (CBDC) interacts with ongoing monetary policies. We distinguish two kind of policies: standard policy, where the central bank invests in treasuries, and quantitative easing, where the central bank invests in risky securities. In each scenario, we introduce an interest-bearing CBDC, and study the equilibrium allocations. Our analysis reaches three main conclusions. The first is that the equilibrium impact of a CBDC depends on the ongoing monetary policy. Second, when the central bank conducts quantitative easing, the introduction of a CBDC is neutral under two conditions: the cost of issuing a CBDC is equal to the interest on reserves, and the demand for CBDC deposits is smaller than the amount of excess reserves in the system. Third, the introduction of a CBDC might render quantitative easing a quasi-permanent policy, as commercial banks optimally use their excess reserves to accommodate retailers’ demand for switching from bank deposits to CBDC deposits.

OTC Markets and Corporate FX Hedging (2019), with H. Hau and S. Langfield


New evidence shows substantial price discrimination in FX derivative markets between sophisticated and less-sophisticated non-financial firms (Hau et al., 2020). This market distortion potentially discourages many firms from hedging FX risks. This paper seeks to investigate the link between firms’ hedging activities and their expected hedging costs. Furthermore, we are interested in quantifying the welfare impact of a more competitive FX forward market. Understanding this issue has relevant policy implications for designing a better functioning derivative market that better serves the real economy.

Policy Papers

Stabilizing Stablecoins: Proposal for a Pragmatic Regulatory Approach, with L. Somoza, The Journal of FinTech (forthcoming)

MEDIA COVERAGE: , Financial Times - Alphaville, Cointelegraph, Il Sole 24 Ore .

We propose a framework for regulating stablecoins as a new asset class. We define stablecoins as those digital currencies which are centrally managed and backed by other assets. We compare stablecoins and ETFs under the principle that similar risks should be treated in a similar fashion. Hence, we argue that locking stablecoins into an ETF-like structure, along with restrictions on the basket composition, would significantly reduce regulatory concerns. Stablecoin providers would be functionally similar to ETF sponsors, and stablecoins would be a new vehicle for traditional fiat currencies. Finally, we address common macroeconomic concerns in light of our proposed framework.

Work in progress

  • Safe Asset and Financial Inclusion: Lessons for CBDCs?

  • Talk is cheap: a textual analysis of firms’ earning calls

  • Does Protectionism Shield Individuals from International Shocks? Evidence from Credit Score Data