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Combination of multivariate volatility forecasts

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Author Info
Alessandra Amendola
Giuseppe Storti
Abstract

This paper proposes a novel approach to the combination of conditional covariance matrix forecasts based on the use of the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional systems by means of a two-step strategy. The finite sample properties of the GMM estimator of the combination weights are investigated by Monte Carlo simulations. Finally, in order to give an appraisal of the economic implications of the combined volatility predictor, the results of an application to tactical asset allocation are presented.

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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2009-007.

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Length: 16 pages
Date of creation: Jan 2009
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Handle: RePEc:hum:wpaper:sfb649dp2009-007

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Related research
Keywords: Multivariate GARCH; Forecast Combination; GMM; Portfolio Optimization;

Find related papers by JEL classification:
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May. [Downloadable!] (restricted)
    Other versions:
  2. Jagannathan, Ravi & Skoulakis, Georgios & Wang, Zhenyu, 2002. "Generalized Method of Moments: Applications in Finance," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 470-81, October.
  3. Pesaran, M Hashem & Zaffaroni, Paolo, 2005. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi-Asset Volatility Models for Risk Management," CEPR Discussion Papers 5279, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  4. Storti, G., 2006. "Minimum distance estimation of GARCH(1,1) models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1803-1821, December. [Downloadable!] (restricted)
  5. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  6. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February. [Downloadable!] (restricted)
  7. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, 02. [Downloadable!] (restricted)
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