Francesco Ravazzolo is a researcher at Economic Department, BI Norwegian Business School and senior researcher at the monetary policy department of Norges Bank.
Research areas
Bayesian econometrics
Publications
Caporin, Massimiliano; Natvik, Gisle James, Ravazzolo, Francesco & Santucci de Magistris, Paolo (2019)
The bank-sovereign nexus: Evidence from a non-bailout episode
We explore the interplay between sovereign and bank credit risk in a setting where Danish authorities first let two Danish banks default and then left the country’s largest bank, Danske Bank, to recapitalize privately. We find that the correlation between bank and sovereign credit default swap (CDS) rates changed with these events. Following the non-bailout events, the sensitivity to external shocks, proxied by CDS rates on the European banking sector, declined both for Danske Bank and for Danish sovereign debt. After Danske Bank’s recapitalization, its exposure to the European banking sector reappeared while that did not happen for Danish sovereign debt. The decoupling between CDS rates on sovereign and private bank debt indicates that the vicious feedback loop between bank and sovereign risk weakened after the non-bailout policies were introduced.
Furlanetto, Francesco; Ravazzolo, Francesco & Sarferaz, Samad (2019)
Identification of financial factors in economic fluctuations
In public discussions of the quality of forecasts, attention typically focuses on the predictive performance in cases of extreme events. However, the restriction of conventional forecast evaluation methods to subsets of extreme observations has unexpected and undesired effects, and is bound to discredit skillful forecasts when the signal-to-noise ratio in the data generating process is low. Conditioning on outcomes is incompatible with the theoretical assumptions of established forecast evaluation methods, thereby confronting forecasters with what we refer to as the forecaster’s dilemma. For probabilistic forecasts, proper weighted scoring rules have been proposed as decision-theoretically justifiable alternatives for forecast evaluation with an emphasis on extreme events. Using theoretical arguments, simulation experiments and a real data study on probabilistic forecasts of U.S. inflation and gross domestic product (GDP) growth, we illustrate and discuss the forecaster’s dilemma along with potential remedies.
Bjørnland, Hilde C; Ravazzolo, Francesco & Thorsrud, Leif Anders (2017)
Forecasting GDP with global components: This time is different