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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IRTG 1792 Discussion Paper ; No. 2020-025

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Mathematical and Quantitative Methods: General
• Thema: high-frequency data ; kernel estimation ; jump ; forecasting covariance matrix

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Mustafayeva, Konul ; Wang, Weining
• Ereignis: Veröffentlichung
• (wer): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (wo): Berlin
• (wann): 2020

Weitere Informationen

• Handle: http://hdl.handle.net/10419/230831
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ