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April 2010 Vast volatility matrix estimation for high-frequency financial data
Yazhen Wang, Jian Zou
Ann. Statist. 38(2): 943-978 (April 2010). DOI: 10.1214/09-AOS730

Abstract

High-frequency data observed on the prices of financial assets are commonly modeled by diffusion processes with micro-structure noise, and realized volatility-based methods are often used to estimate integrated volatility. For problems involving a large number of assets, the estimation objects we face are volatility matrices of large size. The existing volatility estimators work well for a small number of assets but perform poorly when the number of assets is very large. In fact, they are inconsistent when both the number, p, of the assets and the average sample size, n, of the price data on the p assets go to infinity. This paper proposes a new type of estimators for the integrated volatility matrix and establishes asymptotic theory for the proposed estimators in the framework that allows both n and p to approach to infinity. The theory shows that the proposed estimators achieve high convergence rates under a sparsity assumption on the integrated volatility matrix. The numerical studies demonstrate that the proposed estimators perform well for large p and complex price and volatility models. The proposed method is applied to real high-frequency financial data.

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Yazhen Wang. Jian Zou. "Vast volatility matrix estimation for high-frequency financial data." Ann. Statist. 38 (2) 943 - 978, April 2010. https://doi.org/10.1214/09-AOS730

Information

Published: April 2010
First available in Project Euclid: 19 February 2010

zbMATH: 1183.62184
MathSciNet: MR2604708
Digital Object Identifier: 10.1214/09-AOS730

Subjects:
Primary: 62H12
Secondary: 62G05 , 62M05 , 62P20

Keywords: convergence rate , diffusion , integrated volatility , matrix norm , micro-structure noise , realized volatility , regularization , Sparsity , threshold

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.38 • No. 2 • April 2010
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