High-dimensional Index Volatility Models via Stein's Identity
This work addresses volatility modeling in high-dimensional settings for statistical and financial applications, offering a novel approach that relaxes existing conditions.
The authors tackled the problem of estimating parametric components in high-dimensional single and multiple index volatility models, developing methods using Stein's identities that handle heavy-tailed data and achieve minimax optimal convergence rates.
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.