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

Kim Christensen, Mikkel Slot Nielsen, Mark Podolskij*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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.

Original languageEnglish
JournalStatistical Inference for Stochastic Processes
Volume26
Issue2
Pages (from-to)331-359
Number of pages29
ISSN1387-0874
DOIs
Publication statusPublished - Jul 2023

Keywords

  • Bernstein’s inequality
  • LASSO estimation
  • Low rank estimation
  • Quadratic variation
  • Rank recovery
  • Realized variance
  • Shrinkage estimator

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