法兰西银行

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

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.

I consider a neoclassical growth model with a constant absolute risk aversion (CARA) utility function and derive a global closed form approximation that is arbitrarily precise as the discount rate ρ is close to the population growth rate n. I use it to show that the consumption function is strictly concave and that countries can have two different paths converging to the steady-state: front-loading and back-loading.

Recent fiscal slippages in EU economies highlighted the urgency of restoring sound public finance to ensure fiscal sustainability. Economic literature on fiscal discipline has grown substantially and developed that effective fiscal rules must be simple, flexible and credible. Difficult to reconcile, these conditions can be approximated if statistics provide accurate measures of indicators targeted by fiscal rules. Any lacks in this balancing might affect the performance of fiscal rules in achieving fiscal discipline. Focusing on the Golden Rule (GR) of public finance and using United Kingdom data, this paper shows that the GR is neither simple nor clear since its target measurement is sensitive to national accounts methodologies. We also raise concerns on GR credibility as its forecast is accompanied by uncertainty depicted in a stochastic Vector Autoregression (VAR) analysis. The main results suggest that these sensitivities of the GR affect its performance as measured as the compliance with the GR.