Testing for jumps and cojumps in financial markets
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/58390 |
| _version_ | 1811080417128742912 |
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| author | Ju, Cheng, S.M. Massachusetts Institute of Technology |
| author2 | Scott Joslin. |
| author_facet | Scott Joslin. Ju, Cheng, S.M. Massachusetts Institute of Technology |
| author_sort | Ju, Cheng, S.M. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010. |
| first_indexed | 2024-09-23T11:31:19Z |
| format | Thesis |
| id | mit-1721.1/58390 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:31:19Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/583902019-04-11T07:26:18Z Testing for jumps and cojumps in financial markets Ju, Cheng, S.M. Massachusetts Institute of Technology Scott Joslin. Massachusetts Institute of Technology. Computation for Design and Optimization Program. Massachusetts Institute of Technology. Computation for Design and Optimization Program. Computation for Design and Optimization Program. 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). 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. by Cheng Ju. S.M. 2010-09-03T18:33:41Z 2010-09-03T18:33:41Z 2010 2010 Thesis http://hdl.handle.net/1721.1/58390 640128441 eng 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. http://dspace.mit.edu/handle/1721.1/7582 64 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Computation for Design and Optimization Program. Ju, Cheng, S.M. Massachusetts Institute of Technology Testing for jumps and cojumps in financial markets |
| title | Testing for jumps and cojumps in financial markets |
| title_full | Testing for jumps and cojumps in financial markets |
| title_fullStr | Testing for jumps and cojumps in financial markets |
| title_full_unstemmed | Testing for jumps and cojumps in financial markets |
| title_short | Testing for jumps and cojumps in financial markets |
| title_sort | testing for jumps and cojumps in financial markets |
| topic | Computation for Design and Optimization Program. |
| url | http://hdl.handle.net/1721.1/58390 |
| work_keys_str_mv | AT juchengsmmassachusettsinstituteoftechnology testingforjumpsandcojumpsinfinancialmarkets |