Testing for jumps and cojumps in financial markets
Author(s)
Ju, Cheng, S.M. Massachusetts Institute of Technology
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Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Advisor
Scott Joslin.
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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).
Date issued
2010Department
Massachusetts Institute of Technology. Computation for Design and Optimization ProgramPublisher
Massachusetts Institute of Technology
Keywords
Computation for Design and Optimization Program.