This paper develops a multivariate filter based on an unobserved component model to estimate the financial cycle. Our model features: (1) a dynamic relationship between the financial cycle and key variables; (2) time-varying shock volatility for trend and cycle components. We demonstrate that our approach not only exhibits superior early warning properties for banking crises but also outperforms commonly used indicators in terms of data fit for decomposition exercises, as evidenced by the higher marginal likelihood. We document three important properties of the financial cycle. First, the sensitivity of the financial cycle to changes in real estate valuations increased during the post-90s period. Second, the sensitivity of the cycle to changes in financial conditions displays volatility and country specificities. Finally, our reduced form estimates suggest that the banking crisis of 1988 was preceded by positive contributions from the risk appetite shock, while the primary source of vulnerabilities emanated from the housing market in the run-up to the Global Financial Crisis.

The tails of macroeconomic outcomes can respond differently from the centre of their distribution: shocks with modest effects on median growth or inflation can shift downside growth or upside inflation risk. We develop a threshold stochastic-volatility-in-mean VAR with regime-dependent leverage to study their structural drivers. The model allows endogenous interactions between outcomes and volatility, contemporaneous level-volatility dependence, and regime-specific propagation. In nearly 150 years of U.S. data, predictive model selection supports three inflation-defined regimes. We identify business-cycle, financial, macroeconomic-uncertainty, and financial-uncertainty shocks and decompose their contributions to growth- and inflation-at-risk. The structural composition of tail risk differs from that of the predictive median. Business-cycle shocks dominate the median response of GNP growth but account for a substantially smaller share of growth-at-risk. Macroeconomic uncertainty makes a material contribution to both growth- and inflation-at-risk, with its share of growth-at-risk increasing with the magnitude of a positive macroeconomic-uncertainty impulse, despite its limited role at the median. In high-inflation states, the contribution of financial uncertainty to inflation-at-risk rises with the magnitude of positive financial-uncertainty impulses.

This paper studies the financial-market transmission of the April 2025 U.S. tariff announcements, with a particular focus on the unusual combination of dollar depreciation and rising long-term U.S. Treasury yields. We ask whether the market reaction can be understood as the propagation of a single tariff-announcement shock or instead reflects the interaction of distinct macro-financial disturbances. To address this question, we exploit time variation in the volatility and excess kurtosis of structural shocks to recover macro-financial forces without imposing event-based restrictions or exclusion assumptions. Our results show that the initial market reaction is consistent with a conventional safe-haven shock, but this mechanism cannot account for the subsequent joint behaviour of exchange rates and long-term U.S. Treasury yields. Instead, different segments of financial markets are dominated by distinct orthogonal shocks associated with safe-haven demand, confidence in U.S. institutions, Treasury-market intermediation, and changes in the convenience value of dollar-denominated safe assets.

This paper develops a dynamic factor model in which common level and volatility factors evolve jointly, allowing conditional means and variances to interact endogenously within a large-information setting. The joint evolution of these factors provides a tractable framework for modeling risk, as fluctuations in volatility affect both the dispersion and the location of outcomes, generating state-dependent and asymmetric tail risks in predictive distributions. Volatility is captured by latent common factors that drive co-movement in second moments across a large panel, while heavy-tailed idiosyncratic shocks absorb transitory outliers and isolate persistent uncertainty dynamics. The framework embeds these interactions directly within a factor structure, allowing tail risk to emerge endogenously from the joint dynamics of the system rather than being modeled directly through individual conditional quantiles. Empirically, the model delivers improvements in density forecast accuracy, particularly in the tails of the predictive distribution. An application to international inflation highlights a dominant global level component in advanced economies and stronger regional and volatility contributions in emerging and developing economies, pointing to substantial heterogeneity in the role of uncertainty across countries.

This paper introduces the Factor Bayesian Additive Regression Tree (FABART) model, a nonparametric dynamic factor model for structural analysis in large information sets. FABART combines a linear vector autoregressive transition equation for the latent factors with outcome-specific Bayesian Additive Regression Tree (BART) functions in the measurement equations, allowing the relationship between latent factors and observables to be learned nonparametrically rather than imposed through a functional form. A linear approximation permits efficient latent-state sampling, while structural shocks are identified in the factor transition system and propagated through the full nonlinear measurement functions using generalized impulse responses. We study the transmission of externally identified oil supply news shocks in a large U.S. macro-financial and regional dataset. The estimated responses reveal pronounced sign asymmetries and substantial cross-state heterogeneity, with economically meaningful responses emerging only for sufficiently large shocks.

The role of monetary analysis at the ECB has evolved over the past several decades, expanding from a narrow focus on the quantity of money towards a comprehensive assessment of monetary policy transmission and the credit creation process. This natural evolution was driven by the vulnerabilities of the transmission mechanism unveiled by the global financial crisis and the sovereign debt crisis, along with the need to better understand the new transmission channels set in motion by unconventional monetary policy. The move has also been facilitated by the increased availability of granular data and a parallel expansion in computation capacity. For these reasons, monetary analysis plays a central role at a central bank like the ECB which, as part of a data-dependent approach, has monetary policy transmission as a key element of its reaction function. We illustrate this role by discussing how monetary analysis contributed to the assessment of financing conditions during the pandemic, how it informed the diagnosis of the 2021-2022 surges in inflation, and how it contributed to the calibration of the tightening cycle. Finally, we explore some of the challenges that monetary analysis may face in the years to come.

This paper introduces a Bayesian Quantile Factor Augmented VAR (BQFAVAR) to examine the asymmetric effects of monetary policy throughout the business cycle. Monte Carlo experiments demonstrate that the model effectively captures non-linearities in impulse responses. Analysis of aggregate responses to a contractionary monetary policy shock reveals that financial variables and industrial production exhibit more pronounced impacts during recessions compared to expansions, aligning with predictions from the 'financial accelerator' propagation mechanism literature. Additionally, inflation displays a higher level of symmetry across economic conditions, consistent with households' loss aversion in the context of reference-dependent preferences and central banks' commitment to maintaining price stability. The examination of price rigidities at a granular level, employing sectoral prices and quantities, demonstrates that during recessions, the contractionary policy shock results in a more pronounced negative impact on quantities compared to expansions. This finding provides support for the notion of stronger downward than upward price rigidity, as suggested by 'menu-costs models'.

The Banking Euro Area Stress Test (BEAST) is a large-scale semi-structural model developed to analyse the euro area banking system from a macroprudential perspective. The model combines the dynamics of approximately 90 of the largest euro area banks with those of individual euro area economies. It reflects the heterogeneity of banks by replicating the structure of their balance sheets and profit and loss accounts. Additionally, it allows banks to adjust their assets, funding mix, pricing decisions, management buffers, and profit distribution along with individual bank conditions, including their capital and liquidity requirements, and other supervisory limits. The responses of banks impact credit supply conditions and have feedback effects on the macroeconomic environment. Stochastic solutions of the model provide a solid foundation for investigating multiple scenarios, deriving at-risk measures, and estimating model uncertainty.

The paper proposes a framework for assessing the impact of system-wide and bank-level capital buffers. The assessment rests on a factor-augmented vector autoregression (FAVAR) model that relates individual bank adjustments to macroeconomic dynamics. We estimate FAVAR models individually for eleven euro area economies and identify structural shocks, which allow us to diagnose key vulnerabilities of national banking systems and estimate short-run economic costs of increasing banks’ capitalisation.