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Activity Number:
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444
- Highlights from the Journal STAT
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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SSC (Statistical Society of Canada)
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Abstract #312617
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Title:
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Clustering and Bi-Clustering for High-Frequency Financial Time Series Based on Mutual Information
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Author(s):
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Jian Zou* and Haitao Liu and Nalini Ravishanker
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Companies:
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Worcester Polytechnic Institute and Worcester Polytechnic Institute and University of Connecticut
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Keywords:
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Biclustering;
High-Frequency Time Series;
Mutual Information
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Abstract:
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Exploring high frequency transaction level financial data is of considerable interest to researchers and investors. The extra amount of information contained in high-frequency data and keen interests in high-frequency finance motivate researchers to study dynamic patterns of co-movement over multiple trading days. In this paper, we have developed a series of clustering and biclustering algorithms based on mutual information for high frequency financial time series . We examine the co-movement probabilities of selected m-tuples of stocks over multiple trading days under different metrics. Additionally, we propose an unified framework to describe patterns and monitor the structure of high-dimensional daily or weekly time series that track linkages between any given m-tuple of stocks over a long time period.
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Authors who are presenting talks have a * after their name.
