 Testing for non-correlation between price and volatility jumpsJean Jacod, Claudia Klüppelberg and Gernot Müller Journal of Econometrics, 2017, vol. 197, issue 2, 284-297 Abstract: We consider a log-price process Xt, which is observed at discrete times 0,Δn, 2Δn,…, and the process has a stochastic squared volatility σt2. Assuming that the price process as well as the volatility process have common jumps, we suggest tests for non-correlation between log-price and squared volatility jumps, or functions of such jumps. Our tests have a prescribed asymptotic level, as the mesh Δn tends to 0 and the observation time Tn tends to ∞. The finite sample performance of our test is studied using simulations. We finally apply our tests to real data, and the test rejects the non-correlation hypothesis for the combination of squared log-price jumps and the moduli of the jumps of the squared volatility. This sheds new light on economically motivated statements on causality between price and volatility jumps and on econometric modeling. Keywords: Common jumps; Discrete sampling; High-frequency data; Itô semimartingale; Statistical test; Stochastic volatility model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:197:y:2017:i:2:p:284-297 DOI: 10.1016/j.jeconom.2016.11.007 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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