法兰西银行

The COVID-19 market turmoil triggered significant outflows from euro area money market funds (MMFs), reaching nearly 8% of assets under management. By analysing granular data on derivative contracts and MMF activity, we identify a novel shock transmission mechanism: as financial intermediaries use MMFs as cash management vehicles, derivative margin calls can generate MMF flows. Our findings suggest that margin calls faced by euro area non-bank financial intermediaries drove around 45% of the aggregate outflows from EUR-denominated euro area MMFs between February and April 2020, revealing a critical channel through which market shocks propagate.

This paper introduces the Euro Area Communication Event-Study Database (EA-CED), a new dataset tracking intraday financial market movements around 304 ECB Governing Council meetings (ECBGC) and 5,100 inter-meeting communication (IMC) events by GC members, primarily in the form of speeches and interviews. We document that IMC events are associated with significant market movements often comparable to, or larger than, those following ECB policy announcements, particularly for longer maturity yields. Importantly, these effects are not limited to communication from the ECB President but also from other Governing Council members. Like ECBGC announcements, IMC events convey multidimensional information: three structurally identified factors explain a large share of the yield curve movements around IMC surprises. Finally, we show that IMC events provide relevant information for identifying the effects of monetary policy shocks on euro area output and inflation in a Bayesian Vector Autoregression model.

We nowcast world trade using machine learning, distinguishing between tree -based methods (random forest, gradient boosting) and their regression -based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform not only the tree-based techniques, but also more “traditional” linear and non-linear techniques (OLS, Markov-switching, quantile regression). They do so significantly and consistently across different horizons and real-time datasets. To further improve performance when forecasting with machine learning, we propose a flexible three-step approach composed of ( step 1 ) pre-selection, ( step 2 ) factor extraction and ( step 3 ) machine learning regression. We find that both pre-selection and factor extraction significantly improve the accuracy of machine-learning-based predictions. This three-step approach also outperforms workhorse benchmarks, such as a PCA-OLS model, an elastic net

A puzzle is that price-to-rent ratios in the housing market of a given urban area vary a lot in time and space. In some contexts, accounting for differences in local discount rates or rent growth differences is not sufficient. We propose a variant of asset pricing equations for housing markets that include a price gradient in space. It is analogous to the transport equation in physics and generates a new range of solutions consistent with the facts. The main rationale for the price gradient in the asset pricing equation is the existence of spatial search frictions for housing, but other informational frictions are discussed. The main mechanism is supported by the data analysis of a large urban area, Paris region.

Does the Federal Reserve’s monetary policy influence the rates on USD-pegged stablecoins? While major stablecoin issuers do not pay interest, investors can earn returns by depositing stablecoins in Decentralized Finance (DeFi) protocols. We document unusually large and persistent spreads between traditional short-term interest rates and DeFi deposit rates, as well as a weak and unstable transmission of policy rate changes. We show that, in the short run, monetary policy shocks can move stablecoin rates in the opposite direction of policy rates, delaying a convergence that occurs only over the medium run. Both the sign of the short-run effect and the speed of convergence depend on the intensity of deleveraging induced by crypto-price reactions relative to the standard interest-rate arbitrage channel — an effect shaped by investors’ limited ability to bridge traditional and decentralized finance.