This track is concerned with methodological and computational aspects of econometrics, with special interest in the empirical analysis of financial time series. Computational and financial econometrics techniques are widely applied by academics and practitioners alike in a wide range of real-world problems that include risk management, portfolio management and forecasting financial markets. Financial time series analysis deals with theoretical and empirical issues in asset valuations, volatility and risk measurement, and market microstructure modeling inter alia. The computational aspects of such problems are of crucial importance because one typically deals with high-dimensional models and large samples. Existing algorithms can be improved computationally in terms of efficiency, stability, or conditioning. Relatedly, there is still much room for further development of integrated econometrics packages.
The track is strongly connected with the 4th International Conference on Computational and Financial Econometrics (CFE'10) and the Computational Statistics and Data Analysis. Papers containing strong computational statistical or econometric components or substantive data-analytic elements will be considered for publication in the CSDA Annals of Computational and Financial Econometrics.
Submissions to this track should be directed to the (CFE'10) which takes place jointly with the ERCIM10 conference.