Advertisement

A Thick Modeling Approach to Multivariate Volatility Prediction

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

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.

Keywords

Forecast combination Multivariate volatility Thick modeling Weights estimation 

Notes

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.

References

  1. 1.
    Amendola, A., Storti, G.: A GMM procedure for combining volatility forecasts. Comput. Stat. Data Anal. 52(6), 3047–3060 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Amendola, A., Storti, G.: Combination of multivariate volatility forecasts. SFB 649 Discussion Papers, DP2009-007. Humboldt University, Berlin, Germany (2009)Google Scholar
  3. 3.
    Amendola, A., Storti, G.: Model uncertainty and forecast combination in high dimensional multivariate volatility prediction. In: Proceedings of COMPSTAT 2012, ISI/IASC, 27–38 (2012)Google Scholar
  4. 4.
    Andersen, T.G., Bollerslev, T., Frederiksen, P., Nielsen, O.: Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns. J. Appl. Econometrics 25(2), 233–261 (2010)CrossRefMathSciNetGoogle Scholar
  5. 5.
    De Pooter, M., Ravazzolo, F., van Dijk, D.: Term structure forecasting using macro factors and forecast combination. Board of Governors of the Federal Reserve System, Discussion Paper 993 (2010)Google Scholar
  6. 6.
    Engle, R.F.: Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J. Bus. Econ. Stat. 20(3), 339–350 (2002)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Engle, R.F., Shephard, N., Sheppard, K.: Fitting vast dimensional time-varying covariance models. Economics Series Working Papers 403. University of Oxford, Oxford (2008)Google Scholar
  8. 8.
    Engle, R.F., Kroner, K.F.: Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model. Econ. Theor. 11(1), 122–150 (1995)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Golosnoy, V., Gribisch, B., Liesenfeld, R.: The conditional autoregressive Wishart model for multivariate stock market volatility. J. Econometrics 167, 211–223 (2011)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Granger, C.W.J., Jeon, Y.: Thick modeling. Econ. Model. 21, 323–343 (2004)CrossRefGoogle Scholar
  11. 11.
    Hansen, P.R., Lunde, A., Nason, J.M.: The model confidence set. Econometrica 79, 453–497 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    J.P. Morgan Guaranty Trust Company: RiskMetrics Technical Document, 4 edn. (1996)Google Scholar
  13. 13.
    Laurent, S., Rombouts, J.V.K., Violante, F.: On loss functions and ranking forecasting performances of multivariate volatility models. J. Econometrics 173(1), 1–10 (2013)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Patton, A.J., Sheppard, K.: Evaluating volatility and correlation forecasts. In: Andersen, T.G., Davis, R.A., Kreiss, J.P., Mikosch, T. (eds.) Handbook of Financial Time Series. Springer, Berlin (2009)Google Scholar
  15. 15.
    Patton, A.J.: Volatility forecast comparison using imperfect volatility proxies. J. Econometrics 160(1), 246–256 (2011)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Pesaran, M.H., Schleicher, C., Zaffaroni, P.: Model averaging in risk management with an application to futures markets. J. Empir. Finance 16(2), 280–305 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

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

Personalised recommendations