Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

Laurent A.F. Callot, Anders B. Kock, Marcelo C. Medeiros

Research output: Contribution to journalArticlepeer-review


We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso-type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics as well as the performance of the proposed models for forecasting the realized covariance matrices of the 30 Dow Jones stocks. We find that the dynamics are not stable as the data are aggregated from the daily to lower frequencies. Furthermore, we are able beat our benchmark by a wide margin. Finally, we investigate the economic value of our forecasts in a portfolio selection exercise and find that in certain cases an investor is willing to pay a considerable amount in order get access to our forecasts.

Original languageEnglish (US)
Pages (from-to)140-158
Number of pages19
JournalJournal of Applied Econometrics
Issue number1
StatePublished - Jan 1 2017
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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