May 2021 High-dimensional index volatility models via Stein’s identity
Sen Na, Mladen Kolar
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Bernoulli 27(2): 794-817 (May 2021). DOI: 10.3150/20-BEJ1238


We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order Stein’s identities, we develop methods that are applicable for the estimation of the variance index in the high-dimensional setting requiring finite moment condition, which allows for heavy-tailed data. Our approach complements the existing literature in the low-dimensional setting, while relaxing the conditions on estimation, and provides a novel approach in the high-dimensional setting. We prove that the statistical rate of convergence of our variance index estimators consists of a parametric rate and a nonparametric rate, where the latter appears from the estimation of the mean link function. However, under standard assumptions, the parametric rate dominates the rate of convergence and our results match the minimax optimal rate for the mean index estimation. Simulation results illustrate finite sample properties of our methodology and back our theoretical conclusions.


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Sen Na. Mladen Kolar. "High-dimensional index volatility models via Stein’s identity." Bernoulli 27 (2) 794 - 817, May 2021.


Received: 1 March 2019; Revised: 1 January 2020; Published: May 2021
First available in Project Euclid: 24 March 2021

Digital Object Identifier: 10.3150/20-BEJ1238

Keywords: high-dimensional estimation , index volatility model , Stein’s identity

Rights: Copyright © 2021 ISI/BS


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Vol.27 • No. 2 • May 2021
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