The Role of Realized Ex-Post Covariance Measures and Dynamic Model Choice and the Quality of Covariance Forecast

Rasmus T. Varneskov, Valeri Voev

Research output: Contribution to journalJournal articleResearchpeer-review


Recently, consistent measures of the ex-post covariation of financial assets based on noisy high-frequency data have been proposed. A related strand of literature focuses on dynamic models and covariance forecasting for high-frequency data based covariance measures. The aim of this paper is to investigate whether more sophisticated estimation approaches lead to more precise covariance forecasts, both in a statistical precision sense and in terms of economic value. A further issue, we address, is the relative importance of the quality of the realized measure as an input in a given forecasting model vs. the model's dynamic specification. The main finding is that the largest gains result from switching from daily to high-frequency data. Further gains are achieved if a simple sparse sampling covariance measure is replaced with a more efficient and noise-robust estimator.
Original languageEnglish
JournalJournal of Empirical Finance
Pages (from-to)83-95
Number of pages13
Publication statusPublished - 2013
Externally publishedYes


  • Forecast evaluation
  • Volatility forecasting
  • Portfolio optimization
  • Mean-variance analysis

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