Structural Drivers of Growth at Risk:
Insights from a VAR-Quantile Regression approach
ECB Working Paper (Podcast)
Together with Giacomo Carboni, Luìs Fonseca, and Fabio Fornari
We investigate the impact of structural shocks on the joint distribution of future real GDP growth and inflation in the euro area. We model the mean of their distribution, together with selected financial indicators, using a VAR and perform quantile regression on the VAR residuals to estimate the time-varying variance as a function of macroeconomic and financial variables. Through impulse response analysis, we find that demand and financial shocks reduce expected GDP growth and increase its conditional variance, leading to negatively skewed future growth distributions. By enabling this mean-volatility interaction, demand-type shocks drive a significant time variation in downside risk to euro area GDP growth. Conversely, supply-type shocks do not generate the same mean-volatility interaction, resulting in symmetric movements in GDP growth and inflation quantiles.
Barking up the Wrong Tree? Climate News and Financial Stability
Current working paper (PDF)
Podcast (short/long)
Uncertainty about climate change persists because key parameters in climate models remain poorly constrained. One of the most notable of these is equilibrium climate sensitivity (ECS) —the long-term increase in global temperature following a doubling of CO2. Using a New-Keynesian integrated assessment model with financial frictions, we investigate whether news shocks that update beliefs about ECS could cause the macro-financial system to become unstable. Due to their exceptional forward-looking nature, financial markets are especially vulnerable to information about the future. As such news arrives, agents revise their expectations, adjusting their behaviour, policy implementation and investment choices simultaneously. Therefore, we establish a direct link between the physical and transition risks of climate change and a single pivotal parameter. We find that bad news about ECS can trigger financial stress. It is transition risk — the attempt to limit climate change to 2°C — that causes this instability; physical risk itself does not generate significant financial stress in the short term. However, since those bad news lead to a decrease in the future real interest rate, supporting asset prices, especially the near term physical and transition risk to the financial market is limited.
Monetary Policy, Macroprudential Regulation, and Growth at Risk:
Insights from a Medium-Scale DSGE model
ECB Report with early results (PDF)
Together with Nikolay Hristov and Benedikt Kolb
The “Growth at Risk” (GaR) literature following Adrian et al. (2019) has documented the importance of financial conditions for downside risks to economic growth. drian et al. (2020) elicit GaR dynamics from a small model by adding a ‘vulnerability function’ to a New Keynesian model—the vulnerability function links time-varying uncertainty in the model to the state of the economy. Here, we introduce the vulnerability channel into an otherwise medium-scale DSGE model with a financial sector based on Angeloni und Faia (2013). In particular, financial vulnerability increases in a bank run probability variable, which is at the heart of financial frictions, and affects macroeconomic volatility. We choose this variable as it has a straightforward interpretation as a financial stress indicator whose empirical pendants are usually used to forecast time-varying uncertainty. We show that introducing a vulnerability channel for a version of the model estimated on euro area data can replicate several core asymmetries and nonlinearities in an otherwise standard DSGE model solved with higher order perturbation techniques. Therefore, we propose a novel two-step estimation procedure that utilizes Gaussian Process Regressions trained on the particle filter to reduce the computational estimation burden significantly.