Tomas Carrera de Souza

Central bank lending operations, depending on their design, can create carry trade opportunities for banks, enabling them to leverage central bank funding to invest in higher-yielding assets. I use granular bank securities holdings data to analyze whether the European Central Bank’s recalibration of the TLTRO III programme during 2020–2022, a quasi-experiment providing a positive shock to central bank borrowing, prompted banks to reallocate borrowed funds from reserves to higher-yielding sovereign debt. The effect varies across bond types, determined by the bonds’ asset swap spread and time to maturity; and across banks, primarily influenced by domestic bond holdings, highlighting the role of home bias in these trades and the heterogeneity of risk preferences among banks. The findings have implications for the design of central bank lending operations, which can have spillovers into the demand for sovereign debt and into bank HQLA portfolio allocations.

This paper develops a tractable model of liquidity demand and funding choice under heterogeneous bank risk and market segmentation for demand-driven systems. Banks choose their level of reserve holdings by weighing liquidity benefits against funding costs and regulatory penalties for insufficient high-quality liquid assets (HQLA). Funding is sourced from either market-based debt (commercial paper, deposits, repo), which can be priced with a risk-sensitive spread, or central bank credit. The model endogenizes market funding rates and allocates total liquidity through a market-clearing mechanism, capturing the central bank’s trade-off between the size of its balance sheet and its ability to steer short-term interest rates.

In this study, we analyze the impact of the European Central Bank’s (ECB) sovereign bond purchases on bond demand among euro area investors from 2015 to 2022. By employing a novel demand setup, using ownership shares of individual bonds, we separately estimate investor reactions to (i) ECB bond purchases and (ii) new bond issuances. Utilizing bond level data on securities holdings of euro area investors and the ECB, we show that insurance companies and pension funds act as preferred habitat investors and are reluctant to sell the bonds the ECB is buying. Conversely, non-euro area investors from the private sector primarily serve as counterparties for ECB purchases. Our findings indicate significant differences across bond maturities and credit ratings, but minimal differences across the different stages of the quantitative easing (QE) implementation periods and between domestic and non-domestic euro area bonds.

This paper examines the impact of the Eurosystem’s increased footprint in financial markets, resulting from the response to the Covid-19 crisis, on repo rates. We exploit transaction-level data on the repo market and the Eurosystem’s purchase programmes and find that both marginal purchases (flow effect) and aggregate holdings (stock effect) have a significant downward impact on repo rates. The stock effect is nonlinear, and amplified when the central bank’s holdings are larger. Finally, we find that the Eurosystem’s Securities Lending Facility alleviates this collateral scarcity, but it does not fully compensate for the downward pressure on rates created by purchases.

What micro facts of price changes should be considered in the incorporation of price rigidities into macro models? To answer this, I exploit a novel micro data set obtained with web scraping techniques, containing daily prices of eight retailers from six countries with heterogeneous macroeconomic conditions. I find that: (1) There is a relation between the main statistics (related to the size and frequency of price adjustment) and the inflation rate of a country; (2) The distribution of the size of price changes has a relatively small, yet nontrivial mass around zero; (3) Familiar products from the same manufacturer have greater similarity in the timing and magnitude of price adjustment than heterogeneous products. I show that incorporating a three-dimensional cost – composed by a general cost, a product-specific cost, and a cost curtail for price changes in familiar products– makes an otherwise standard menu cost model reproduce these facts.