Realized Volatility When Sampling Times are Possibly Endogenous

45 Pages Posted: 21 Dec 2009 Last revised: 27 Apr 2013

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance; Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management; Hong Kong University of Science & Technology (HKUST) - Department of Finance

Per A. Mykland

University of Chicago - Department of Statistics

Eric Renault

University of North Carolina (UNC) at Chapel Hill - Department of Economics

Lan Zhang

University of Illinois at Chicago - Department of Finance

Xinghua Zheng

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management

Date Written: April 24, 2013

Abstract

When estimating integrated volatilities based on high-frequency data, simplifying assumptions are usually imposed on the relationship between the observation times and the price process. In this paper, we establish a central limit theorem for the Realized Volatility in a general endogenous time setting. We also establish a central limit theorem for the tricity under the hypothesis that there is no endogeneity, based on which we propose a test and document that this endogeneity is present in financial data.

Keywords: bias-correction, continuous semimartingale, discrete observation, efficiency, endogeneity, It{\^o} process, realized volatility, stable convergence

JEL Classification: C02, C12, C13, C14, C15, C22

Suggested Citation

Li, Yingying and Mykland, Per A. and Renault, Eric and Zhang, Lan and Zheng, Xinghua, Realized Volatility When Sampling Times are Possibly Endogenous (April 24, 2013). Econometric Theory, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1525410 or http://dx.doi.org/10.2139/ssrn.1525410

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Per A. Mykland

University of Chicago - Department of Statistics ( email )

Chicago, IL 60637-1514
United States
773-702-8044 (Phone)

Eric Renault

University of North Carolina (UNC) at Chapel Hill - Department of Economics ( email )

Chapel Hill, NC 27599
United States

Lan Zhang

University of Illinois at Chicago - Department of Finance ( email )

601 South Morgan Street
Chicago, IL 60607
United States

Xinghua Zheng (Contact Author)

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Register to save articles to
your library

Register

Paper statistics

Downloads
284
rank
97,632
Abstract Views
2,457
PlumX