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Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data

Konul Mustafayeva and Weining Wang

No 2020-025, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: Estimating spot covariance is an important issue to study, especially with the increasing availability of high-frequency nancial data. We study the estimation of spot covariance using a kernel method for high-frequency data. In particular, we consider rst the kernel weighted version of realized covariance estimator for the price process governed by a continuous multivariate semimartingale. Next, we extend it to the threshold kernel estimator of the spot covariances when the underlying price process is a discontinuous multivariate semimartingale with nite activity jumps. We derive the asymptotic distribution of the estimators for both xed and shrinking bandwidth. The estimator in a setting with jumps has the same rate of convergence as the estimator for di usion processes without jumps. A simulation study examines the nite sample properties of the estimators. In addition, we study an application of the estimator in the context of covariance forecasting. We discover that the forecasting model with our estimator outperforms a benchmark model in the literature.

Keywords: high-frequency data; kernel estimation; jump; forecasting covariance matrix (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
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