Volatility Estimation and Jump Testing via Realized Information Variation

31 Pages Posted: 14 Jul 2016

See all articles by Weiyi Liu

Weiyi Liu

Capital University of Economics and Business - School of Finance

Mingjin Wang

PKU

Date Written: May 29, 2016

Abstract

We put forward two jump-robust estimators of integrated volatility, namely realized information variation (RIV) and realized information power variation (RIPV). The "information" here refers to the difference between two-grid of ranges in high-frequency intervals, which preserves continuous variation and eliminates jump variation asymptotically. We give several probabilistic laws to show that RIV is much more efficient than most of the other estimators, e.g. 8.87 times more efficient than bi-power variation, and RIPV has a fast jump convergence rate at Op(1/n), while the others are usually Op(1/sqrt(n)) in the literature. We also extend our results to integrated quarticity and higher-order variation estimation, and then propose a new jump testing method. Simulation studies provide extensive evidence on the finite sample properties of our estimators and tests, comparing with alternative methods. The simulations support our theoretical results on volatility estimation and demonstrate that our jump testing method has much lower type I error for smaller sample frequencies, or in the presence of microstructure noise.

Keywords: Volatility, Jump, Realized Information Variation, Realized Information Power Variation, Microstructure noise

JEL Classification: C14, C15, C22, C80, G10

Suggested Citation

Liu, Weiyi and Wang, Mingjin, Volatility Estimation and Jump Testing via Realized Information Variation (May 29, 2016). Available at SSRN: https://ssrn.com/abstract=2809466 or http://dx.doi.org/10.2139/ssrn.2809466

Weiyi Liu (Contact Author)

Capital University of Economics and Business - School of Finance ( email )

Capital University of Economics and Business
Beijing, Beijng 100070
China

Mingjin Wang

PKU ( email )

Peking University
Beijing, Beijing 100871
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
99
Abstract Views
706
Rank
422,949
PlumX Metrics