Francesco Ravazzolo

Bianchi, Daniele; Guidolin, Massimo & Ravazzolo, Francesco (2018)

Dissecting the 2007-2009 real estate market bust: Systematic pricing correction or just a housing fad?

Journal of Financial Econometrics, 16(1), s. 34- 62. Doi: 10.1093/jjfinec/nbx023

Foroni, Claudia; Ravazzolo, Francesco & Sadaba, Barbara (2018)

Assessing the predictive ability of sovereign default risk on exchange rate returns

Journal of International Money and Finance, 81, s. 242- 264. Doi: 10.1016/j.jimonfin.2017.12.001

Bianchi, Daniele; Guidolin, Massimo & Ravazzolo, Francesco (2017)

Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section

Journal of business & economic statistics, 35(1), s. 110- 129. Doi: 10.1080/07350015.2015.1061436

Krüger, F; Clark, Todd E & Ravazzolo, Francesco (2017)

Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts

Journal of business & economic statistics, 35(3), s. 470- 485. Doi: 10.1080/07350015.2015.1087856

Lerch, Sebastian; Thorarinsdottir, Thordis Linda, Ravazzolo, Francesco & Gneiting, Tilmann (2017)

Forecaster's dilemma: Extreme events and forecast evaluation

Statistical Science, 32(1), s. 106- 127. Doi: 10.1214/16-STS588

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.