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A Thick Modeling Approach to Multivariate Volatility Prediction

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Advances in Latent Variables

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

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Abstract

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.

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Notes

  1. 1.

    The data are available online at www.tickdata.com.

  2. 2.

    The choice of a discretization interval equal to 5 min is a standard practice in the literature since it usually guarantees a reasonable compromise between bias and variability of the realized covariance estimator.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Economics and Statistics (DiSES) & Statlab, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, (SA), Italy

    Alessandra Amendola & Giuseppe Storti

Authors
  1. Alessandra Amendola
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  2. Giuseppe Storti
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Corresponding author

Correspondence to Alessandra Amendola .

Editor information

Editors and Affiliations

  1. Dept. of Economics and Management, University of Brescia, Brescia, Italy

    Maurizio Carpita

  2. Dept. of Economics and Management, University of Brescia, Brescia, Italy

    Eugenio Brentari

  3. Dept. of Chemometrics and Sensometrics, Oniris Nantes National College, Nantes, France

    El Mostafa Qannari

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Amendola, A., Storti, G. (2014). A Thick Modeling Approach to Multivariate Volatility Prediction. In: Carpita, M., Brentari, E., Qannari, E. (eds) Advances in Latent Variables. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/10104_2014_18

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