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.
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On the Correlation Structure of Microstructure Noise: A Financial Economic Approach
TL;DR: In this paper, the authors introduce the financial economics of market microstructure into the financial econometrics of asset return volatility estimation, and derive model-based volatility estimators, which they apply to stock and oil prices.
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Have postwar economic fluctuations been stabilized
TL;DR: This article examined the stabilization of the postwar economy by focusing on the duration of business cycles, rather than their amplitude, and found clear evidence of postwar duration stabilization in terms of a shift toward longer expansions and shorter contractions.
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Globalization, the Business Cycle, and Macroeconomic Monitoring
TL;DR: In this paper, a framework for characterizing and monitoring the global business cycle is proposed and implemented, which utilizes high-frequency data, allows to account for a potentially large amount of missing observations, and is designed to facilitate the updating of global activity estimates as data are released and revisions become available.
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On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms
Francis X. Diebold,Kamil Yilmaz +1 more
TL;DR: In this paper, the authors propose several connectedness measures built from pieces of variance decompositions, and argue that they provide natural and insightful measures of connectedness among financial asset returns and volatilities.
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Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models
TL;DR: In this article, an exact maximum likelihood estimation of many observation-driven models remains an open question, and only approximate maximum likelihood estimators are attempted, because the unconditional density needed for exact estimation is not known in closed form.