A Thick Modeling Approach to Multivariate Volatility Prediction

  • Alessandra AmendolaEmail author
  • Giuseppe Storti
Part of the Studies in Theoretical and Applied Statistics book series (STAS)


This paper proposes a modified approach to the combination of forecasts from multivariate volatility models where the combination is performed over a restricted subset including only the best performing models. Such a subset is identified over a rolling window by means of the Model Confidence Set (MCS) approach. The analysis is performed using different combination schemes, both linear and non linear, and considering different loss functions for the evaluation of the forecasting performance. An application to a vast dimensional portfolio of 50 NYSE stocks shows that (a) in non-extreme volatility periods the use of forecast combinations allows to improve over the predictive accuracy of the single candidate models (b) performing the combination over the subset of most accurate models does not significantly reduce the accuracy of the combined predictor.


Forecast combination Multivariate volatility Thick modeling Weights estimation 



The authors gratefully acknowledge financial support from MIUR within the PRIN project 2010–2011 (prot. 2010J3LZEN): Forecasting economic and financial time series: understanding the complexity and modelling structural change.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Economics and Statistics (DiSES) & StatlabUniversity of SalernoFiscianoItaly

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