|
Activity Number:
|
541
|
|
Type:
|
Invited
|
|
Date/Time:
|
Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
WNAR
|
| Abstract - #302764 |
|
Title:
|
Vast Volatility Matrix Estimation for High-Frequency Financial Data
|
|
Author(s):
|
Yazhen Wang*+
|
|
Companies:
|
National Science Foundation
|
|
Address:
|
4201 Wilson Blvd, Arlington, VA, 22203,
|
|
Keywords:
|
diffusion ; integrated volatility ; matrix norm ; micro-structure noise ; realized volatility ; sparsity
|
|
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 with 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. This paper proposes a new type of estimators for the integrated volatility matrix and establishes asymptotic theory for the proposed estimators when both the number of the assets and the sample size of the price data on the assets go to infinity. The numerical studies demonstrate that the proposed estimators perform well for large p and complex price and volatility models.
|
