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Automated model selection in finance: General-to-speci c modelling of the mean and volatility speci cations

  • Alvaro Escribano

    ()

    (Universidad Carlos III de Madrid)

  • Genaro Sucarrat

    ()

    (BI Norwegian School of Management)

General-to-Specific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multi-path GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multi-path GETS modelling in finance. Making use of a recent result on log-GARCH Models, 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 (AR) 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. The finite sample properties of the methods are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods are very useful in practice.

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Paper provided by Instituto Madrileño de Estudios Avanzados (IMDEA) Ciencias Sociales in its series Working Papers with number 2011-09.

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Date of creation: 23 Jun 2011
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Publication status: Published in Oxford Bulletin of Economics and Statistics
Handle: RePEc:imd:wpaper:wp2011-09
Note: This paper is included in the IMDEA Social Sciences Working Paper Series through the Bank of Spain Excellence Programme
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  1. Kevin Sheppard & Andrew J. Patton, 2008. "Evaluating Volatility and Correlation Forecasts," Economics Series Working Papers 2008fe22, University of Oxford, Department of Economics.
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  15. Luc, BAUWENS & Genaro, SUCARRAT, 2006. "General to Specific Modelling of Exchange Rate Volatility : a Forecast Evaluation," Discussion Papers (ECON - Département des Sciences Economiques) 2006013, Université catholique de Louvain, Département des Sciences Economiques.
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