The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts

Rasmus Varneskov and Valeri Voev ()

Journal of Empirical Finance, 2013, vol. 20, issue C, 83-95

Abstract: 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.

Keywords: Forecast evaluation; Volatility forecasting; Portfolio optimization; Mean-variance analysis (search for similar items in EconPapers)
JEL-codes: C32 C53 G11 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Related works:
Working Paper: The Role of Realized Ex-post Covariance Measures and Dynamic Model Choice on the Quality of Covariance Forecasts (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:20:y:2013:i:c:p:83-95

DOI: 10.1016/j.jempfin.2012.11.002

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