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The Computational and Financial Econometrics (CFEnetwork) focuses on the interface of theoretical and applied econometrics, financial econometrics and computation in order to advance a powerful interdisciplinary research field with immediate applications. That is, the CFEnetwork aims first to consolidate the research in computational and financial econometrics that is scattered throughout Europe; second to provide researches with a network from which they can obtain an unrivalled sources of information about the most recent developments in computational and financial econometrics as well as its applications; third to edit quality publications of high impact and significance.

The CFEnetwork is currently publishing the Annals of Computational and Financial Econometrics as a supplement to the journal Computational Statistics and Data Analysis.

Click on the following link if you wish to become a member of CFEnetwork. For further information please contact Computational and Financial Econometrics (CFEnetwork) is an autonomous subgroup linked to CMStatistics.

Organization and Activities
The CFEnetwork comprises a number of specialized teams in various research areas of computational and financial econometrics. The teams contribute to the activities of the network by organizing sessions, tracks and tutorials during the annual CFEnetwork meetings, editing special issues (currently publish under the CSDA Annals of CFE) and submitting research proposals. The speacialized teams are listed here: Specialized Teams.
Computational and financial econometrics comprise a broad field that has clearly interested a wide variety of researchers in economics, finance, statistics, mathematics and computing. Examples include estimation and inference on econometric models, model selection, panel data, measurement error, Bayesian methods, time series analyses, portfolio allocation, option pricing, quantitative risk management, systemic risk and market microstructure, to name but a few. While such studies are often theoretical, they can also have a strong empirical element measuring risk and return and often have a significant computational aspect dealing with issues like high-dimensionality and large numbers of observations. Algorithmic developments are also of interest since existing algorithms often do not utilize the best computational techniques for efficiency, stability, or conditioning. So also are developments of environments for conducting econometrics, which are inherently computer based. Integrated econometrics packages have grown well over the years, but still have much room for development.