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

Kim Christensen (), Mikkel Slot Nielsen () and Mark Podolskij ()
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Kim Christensen: Aarhus University
Mikkel Slot Nielsen: Aarhus University
Mark Podolskij: University of Luxembourg

Statistical Inference for Stochastic Processes, 2023, vol. 26, issue 2, No 4, 359 pages

Abstract: Abstract In this paper, we develop a penalized realized variance (PRV) estimator of the quadratic variation (QV) of a high-dimensional continuous Itô 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.

Keywords: Bernstein’s inequality; LASSO estimation; Low rank estimation; Quadratic variation; Rank recovery; Realized variance; Shrinkage estimator (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11203-022-09282-8

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