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arXiv · 2304.04372

Symmetric positive semi-definite Fourier estimator of instantaneous variance-covariance matrix

Jirô Akahori·Nien-Lin Liu·Maria Elvira Mancino·Tommaso Mariotti·Yukie Yasuda
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Abstract

In this paper we propose an estimator of spot covariance matrix which ensure symmetric positive semi-definite estimations. The proposed estimator relies on a suitable modification of the Fourier covariance estimator in Malliavin and Mancino (2009) and it is consistent for suitable choices of the weighting kernel. The accuracy and the ability of the estimator to produce positive semi-definite covariance matrices is evaluated with an extensive numerical study, in comparison with the competitors present in the literature. The results of the simulation study are confirmed under many scenarios, that consider the dimensionality of the problem, the asynchronicity of data and the presence of several specification of market microstructure noise.

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Jirô Akahori, Nien-Lin Liu, Maria Elvira Mancino, Tommaso Mariotti, Yukie Yasuda. 2023-04-10. Symmetric positive semi-definite Fourier estimator of instantaneous variance-covariance matrix. https://arxiv.org/abs/2304.04372

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