High-dimensional estimation of quadratic variation based on penalized realized variance

TL;DR

This paper introduces a penalized realized variance estimator for high-dimensional quadratic variation, utilizing nuclear norm regularization to promote low-rank solutions, with proven optimality and practical effectiveness in financial data analysis.

Contribution

It develops a novel PRV estimator using nuclear norm penalization for high-dimensional covariance matrices, with theoretical guarantees and empirical validation.

Findings

PRV encourages low-rank solutions via eigenvalue soft-thresholding.

The estimator is minimax optimal up to a logarithmic factor.

Empirical application detects a limited number of factors in equity markets.

Abstract

In this paper, we develop a penalized realized variance (PRV) estimator of the quadratic variation (QV) of a high-dimensional continuous It\^{o} semimartingale. We adapt the principle idea of regularization from linear regression to covariance estimation in a continuous-time high-frequency setting. We show that under a nuclear norm penalization, the PRV is computed by soft-thresholding the eigenvalues of realized variance (RV). It therefore encourages sparsity of singular values or, equivalently, low rank of the solution. We prove our estimator is minimax optimal up to a logarithmic factor. We derive a concentration inequality, which reveals that the rank of PRV is -- with a high probability -- the number of non-negligible eigenvalues of the QV. Moreover, we also provide the associated non-asymptotic analysis for the spot variance. We suggest an intuitive data-driven subsampling procedure to select the shrinkage parameter. Our theory is supplemented by a simulation study and an empirical application. The PRV detects about three-five factors in the equity market, with a notable rank decrease during times of distress in financial markets. This is consistent with most standard asset pricing models, where a limited amount of systematic factors driving the cross-section of stock returns are perturbed by idiosyncratic errors, rendering the QV -- and also RV -- of full rank.