Testing for jumps and cojumps in financial markets
Name
640128441-MIT.pdf
Description
Full printable version
Size
2.29 MB
Format
Adobe PDF
Checksum (MD5)
f4a0ccd68a3fb86c88a71694649b123e
Author(s)
Ju, Cheng, S.M. Massachusetts Institute of Technology
Advisor(s)
Scott Joslin.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 63-64).
Subjects
Computation for Design and Optimization Program.
MIT Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
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