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A Cholesky-MIDAS model for predicting stock portfolio volatility

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  • Ralf Becker
  • Adam Clements
  • Robert O'Neill

Abstract

This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is flexible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.

Suggested Citation

  • Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Cholesky-MIDAS model for predicting stock portfolio volatility," Centre for Growth and Business Cycle Research Discussion Paper Series 149, Economics, The University of Manchester.
    • Handle: RePEc:man:cgbcrp:149
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    References listed on IDEAS

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    Cited by:

    1. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    2. Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
    3. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. E. C. Brechmann & M. Heiden & Y. Okhrin, 2018. "A multivariate volatility vine copula model," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 281-308, April.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G00 - Financial Economics - - General - - - General

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