Testing for jumps and cojumps in financial markets

In this thesis, we introduce a new testing methodology to detect cojumps in multi-asset returns. We define a cojump as a jump in at least one dimension of the return processes. For a multivariate process that follows a semimartingale, and with no other specific assumptions on the process, we form a test statistic which can easily disentangle jumps from continuous paths of the process. We prove that the test statistics are chi-square distributed in the absence of jumps in any dimensions. We propose a hypothesis testing based on the extreme distribution of the test statistics. If the test statistic observed is beyond the extreme level, then most likely, a cojump occurs. Monte Carlo simulation is performed to access the effectiveness of the test by examining the size and power of the test. We apply the test to a pair of empirical asset returns data and the findings of jump timing are consistent with existing literature.

Department dc:contributor.department

Massachusetts Institute of Technology. Computation for Design and Optimization Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Ju, Cheng, S.M. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Scott Joslin.

• Computation for Design and Optimization Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/58390

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/58390