Testing for Jumps When Asset Prices are Observed with Noise - A Swap Variance Approach

41 Pages Posted: 21 Nov 2005 Last revised: 8 Jul 2008

See all articles by George J. Jiang

George J. Jiang

Washington State University

Roel C. A. Oomen

Deutsche Bank AG (London); London School of Economics & Political Science (LSE) - Department of Statistics

Date Written: September 2007

Abstract

This paper proposes a new test for jumps in asset prices that is motivated by the literature on variance swaps. Formally, the test follows by a direct application of Ito's lemma to the semi-Martingale process of asset prices and derives its power from the impact of jumps on the third and higher order return moments. Intuitively, the test statistic reflects the cumulative gain of a variance swap replication strategy which is known to be minimal in the absence of jumps but substantial in the presence of jumps. Simulations show that the jump test has nice properties and is generally more powerful than the widely used bi-power variation test. An important feature of our test is that it can be applied - in analytically modified form - to noisy high frequency data and still retains power. As a by-product of our analysis, we obtain novel analytical results regarding the impact of noise on bi-power variation. An empirical illustration using IBM trade data is also included.

Keywords: swap variance, jumps, bi-power variation, market microstructure noise

JEL Classification: C14, C22, G12

Suggested Citation

Jiang, George and Oomen, Roel C.A., Testing for Jumps When Asset Prices are Observed with Noise - A Swap Variance Approach (September 2007). Journal of Econometrics, Vol. 144, No. 2, pp. 352-370, 2008. Available at SSRN: https://ssrn.com/abstract=850324

George Jiang

Washington State University ( email )

Department of Finance and Management Science
Carson College of Business
Pullman, WA 99-4746164
United States
509-3354474 (Phone)

HOME PAGE: http://directory.business.wsu.edu/bio.html?username=george.jiang

Roel C.A. Oomen (Contact Author)

Deutsche Bank AG (London) ( email )

Winchester House
1 Great Winchester Street
London, EC2N 2DB
United Kingdom

London School of Economics & Political Science (LSE) - Department of Statistics ( email )

Houghton Street
London, England WC2A 2AE
United Kingdom

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