This paper explores monetary-macroprudential policy interactions in a simple, calibrated New Keynesian model incorporating the possibility of a credit boom precipitating a financial crisis and a loss function reflecting financial stability considerations. Deploying the countercyclical capital...

This paper explores monetary-macroprudential policy interactions in a simple, calibrated New Keynesian model incorporating the possibility of a credit boom precipitating a financial crisis and a loss function reflecting financial stability considerations. Deploying the countercyclical capital...

This paper proposes an operational approach to stress testing, allowing one to assess the banking sector's vulnerability in multiple plausible macro-financial scenarios. The approach helps identify macro-financial risk factors of particular relevance for the banking system and individual banks...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870-2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...

This paper explores how the need to transition to a low-carbon economy influences firm credit risk. It develops a novel dataset which augments data on firms' green-house gas emissions over time with information on climate disclosure practices and forward-looking emission reduction targets,...

Funds with an environmental, social and corporate governance (ESG) mandate have been growing rapidly in recent years and received inflows also during periods of market turmoil, such as March 2020, in contrast to their non-ESG peers. This paper investigates whether investors in ESG funds react...

Using evidence from the EU emissions trading system, we collect verified emissions of close to 4000 highly polluting and mostly non-listed firms responsible for 26% of EU's emissions. Over the period 2013-2019, we find a non-linear relationship between leverage and emissions. A firm with higher...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...

Funds with an environmental, social and corporate governance (ESG) mandate have been growing rapidly in recent years and received inflows also during periods of market turmoil, such as March 2020, in contrast to their non-ESG peers. This paper investigates whether investors in ESG funds react...

This paper explores how the need to transition to a low-carbon economy influences firm credit risk. It develops a novel dataset which augments data on firms’ green-house gas emissions over time with information on climate disclosure practices and forward-looking emission reduction targets,...