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Forecasting Covariance Matrices: A Mixed Approach

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  • Roxana Halbleib
  • Valeri Voev

Abstract

In this article, we introduce a new method of forecasting large-dimensional covariance matrices by exploiting the theoretical and empirical potential of mixing forecasts derived from different information sets. The main theoretical contribution of the article is to find the conditions under which a mixed approach (MA) provides a smaller mean squared forecast error than a standard one. The conditions are general and do not rely on distributional assumptions of the forecasting errors or on any particular model specification. The empirical contribution of the article regards a comprehensive comparative exercise of the new approach against standard ones when forecasting the covariance matrix of a portfolio of thirty stocks. The implemented MA uses volatility forecasts computed from high-frequency-based models and correlation forecasts using realized-volatility-adjusted dynamic conditional correlation models. The MA always outperforms the standard methods computed from daily returns and performs equally well to the ones using high-frequency-based specifications, however at a lower computational cost.

Suggested Citation

  • Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
  • Handle: RePEc:oup:jfinec:v:14:y:2016:i:2:p:383-417.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbu031
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    2. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    3. Kevin Sheppard & Wen Xu, 2019. "Factor High-Frequency-Based Volatility (HEAVY) Models," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 33-65.
    4. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.

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