Optimal sparse volatility matrix estimation for high-dimensional It\^{o} processes with measurement errors
Abstract
Stochastic processes are often used to model complex scientific problems in fields ranging from biology and finance to engineering and physical science. This paper investigates rate-optimal estimation of the volatility matrix of a high-dimensional It\^{o} process observed with measurement errors at discrete time points. The minimax rate of convergence is established for estimating sparse volatility matrices. By combining the multi-scale and threshold approaches we construct a volatility matrix estimator to achieve the optimal convergence rate. The minimax lower bound is derived by considering a subclass of It\^{o} processes for which the minimax lower bound is obtained through a novel equivalent model of covariance matrix estimation for independent but nonidentically distributed observations and through a delicate construction of the least favorable parameters. In addition, a simulation study was conducted to test the finite sample performance of the optimal estimator, and the simulation results were found to support the established asymptotic theory.
Cite
@article{arxiv.1309.4889,
title = {Optimal sparse volatility matrix estimation for high-dimensional It\^{o} processes with measurement errors},
author = {Minjing Tao and Yazhen Wang and Harrison H. Zhou},
journal= {arXiv preprint arXiv:1309.4889},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.1214/13-AOS1128 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)