F
Francis X. Diebold
Researcher at University of Pennsylvania
Publications - 376
Citations - 82582
Francis X. Diebold is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Volatility (finance) & Exchange rate. The author has an hindex of 110, co-authored 368 publications receiving 74723 citations. Previous affiliations of Francis X. Diebold include International Monetary Fund & Duke University.
Papers
More filters
Posted Content
The Past, Present, and Future of Macroeconomic Forecasting
TL;DR: In recent years powerful new dynamic stochastic general equilibrium theory has been developed macroeconomic forecasting is poised for resurgence as mentioned in this paper, which aligns itself with economic theory and hence rises and falls with theory, receded following the decline of Keynesian theory.
Posted Content
Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach
Francis X. Diebold,Maximilian Göbel,Philippe Goulet Coulombe,Glenn D. Rudebusch,Boyuan Zhang +4 more
TL;DR: In this article, a dynamic factor model was proposed to combine four different measures of Arctic sea ice extent in an optimal way that accounts for their differing volatility and cross-correlations.
Posted Content
Forecasting output with the composite leading index: an ex ante analysis
Posted Content
Direction-of-Change Forecasts for Asian Equity Markets Based on Conditional Variance, Skewness and Kurtosis Dynamics: Evidence from Hong Kong and Singapore
TL;DR: In this paper, the authors show that the pervasive volatility forecastability in equity returns could, via induced sign forecastability, be used to produce direction-of-change forecasts useful for market timing.
Posted Content
Measuring predictability: theory and macroeconomic applications
Francis X. Diebold,Lutz Kilian +1 more
TL;DR: In this paper, a measure of predictability based on the ratio of the expected loss of a short-run forecast to the expected losses of a long-run prediction is proposed, which is tailored to the forecast horizons of interest.