Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice. Issue 4 (1st October 2016)

• Record Type: Journal Article
• Authors: Lunde, Asger; Shephard, Neil; Sheppard, Kevin
• Is Part Of: Journal of business & economic statistics. Volume 34:Issue 4(2016)
• Page Start: 504
• Page End: 518
• Publication Date: 2016-10-01
• Subjects: Disciplined convex optimization -- High-frequency data -- Market frictions -- Minimum variance portfolio -- Realized kernel; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2015.1064432
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract : We propose a composite realized kernel to estimate the ex-post covariation of asset prices. These measures can in turn be used to forecast the covariation of future asset returns. Composite realized kernels are a data-efficient method, where the covariance estimate is composed of univariate realized kernels to estimate variances and bivariate realized kernels to estimate correlations. We analyze the merits of our composite realized kernels in an ultra high-dimensional environment, making asset allocation decisions every day solely based on the previous day's data or a short moving average over very recent days. The application is a minimum variance portfolio exercise. The dataset is tick-by-tick data comprising 437 U.S. equities over the sample period 2006–2011. We show that our estimator is able to outperform its competitors, while the associated trading costs are competitive.