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Forecasting multivariate realized stock market volatility

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  • Bauer, Gregory H.
  • Vorkink, Keith

Abstract

We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of lagged volatility, lagged returns and other forecasting variables. The model has several advantages: it is parsimonious; it does not require imposing parameter restrictions; and, it results in a positive-definite estimated covariance matrix. We apply the model to the covariance matrix of size-sorted stock returns and find that two factors are sufficient to capture most of the dynamics.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 160 (2011)
Issue (Month): 1 (January)
Pages: 93-101

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Handle: RePEc:eee:econom:v:160:y:2011:i:1:p:93-101

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: HAR-RV model Realized volatility Covariance matrix Factor model;

References

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