Hawkes Processes in Finance: A Review with Simulations

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dc.contributor.authorSimon, Graham
dc.date.accessioned2016-10-21T16:47:51Z
dc.date.available2016-10-21T16:47:51Z
dc.date.issued2016-06
dc.description57 pages. A thesis presented to the Department of Mathematics and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Science, Spring 2016.en_US
dc.description.abstractHawkes processes are flexible robust models for simulating many self-exciting features seen in empirical data. Using a Hawkes process creates clusters in modeled data that are frequently seen in different natural environments. Some frequent areas of use for Hawkes processes include the study of earthquakes, neural networks, social media sharing, and financial trading data. This work builds an accessible framework for the undergraduate study of Hawkes processes through building step-by-step from point processes to Poisson processes and eventually Hawkes models. A literature review of current research and utilizations for Hawkes processes is then done to demonstrate some of the dramatic growth seen in this field of research. Point clusters, kernel estimation, parameter estimation, and algorithms for implementation are also discussed with simple simulations performed in Excel.en_US
dc.identifier.urihttps://hdl.handle.net/1794/20369
dc.language.isoen_USen_US
dc.publisherUniversity of Oregonen_US
dc.relation.ispartofseriesUniversity of Oregon theses, Dept. of Mathematics, Honors College, B.S., 2016;
dc.rightsCreative Commons BY-NC-ND 4.0-USen_US
dc.subjectMathematicsen_US
dc.subjectApplied mathematicsen_US
dc.subjectFinancial mathematicsen_US
dc.subjectPoint processesen_US
dc.subjectHawkes modelsen_US
dc.subjectPoisson processesen_US
dc.subjectProbability theoryen_US
dc.titleHawkes Processes in Finance: A Review with Simulationsen_US
dc.typeThesis / Dissertationen_US

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