We are grateful to Luc Bauwens, Casper Christophersen, Jurgen Doornik, Neil R. Ericsson, David F. Hendry, Sebastien Laurent, André Alves Portela Santos, Enrique Sentana, the editor, an anonymous reviewer, and seminar participants at University of Oslo, BI Norwegian Business School, Statistics Norway, CORE and Universidad de Navarra, and conference participants at the XVII Foro de Finanzas, the XXXIV Simposio Economico, Forskermøtet 2010 and the 8th OxMetrics User Conference, for helpful comments and suggestions. This research was supported by a Marie Curie Intra-European Fellowship within the 6th European Community Framework Programme, and funding from the Bank of Spain Excellence Program is gratefully acknowledged.
Automated Model Selection in Finance: General-to-Specific Modelling of the Mean and Volatility Specifications*
Article first published online: 24 OCT 2011
© Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2011
Oxford Bulletin of Economics and Statistics
Volume 74, Issue 5, pages 716–735, October 2012
How to Cite
Sucarrat, G. and Escribano, A. (2012), Automated Model Selection in Finance: General-to-Specific Modelling of the Mean and Volatility Specifications. Oxford Bulletin of Economics and Statistics, 74: 716–735. doi: 10.1111/j.1468-0084.2011.00669.x
- Issue published online: 11 SEP 2012
- Article first published online: 24 OCT 2011
- Final Manuscript Received: July 2011
General-to-Specific (GETS) modelling has witnessed major advances thanks to the automation of multi-path GETS specification search. However, the estimation complexity associated with financial models constitutes an obstacle to automated multi-path GETS modelling in finance. Making use of a recent result we provide and study simple but general and flexible methods that automate financial multi-path GETS modelling. Starting from a general model where the mean specification can contain autoregressive terms and explanatory variables, and where the exponential volatility specification can include log-ARCH terms, asymmetry terms, volatility proxies and other explanatory variables, the algorithm we propose returns parsimonious mean and volatility specifications.