Abstract
High-Frequency Financial Time Series Analysis
by
Nalini Ravishanker
This talk will discuss a broad range of statistical data analysis for high-frequency financial time series obtained from the Trade and Quotes (TAQ) database. Clustering and biclustering of the time series across a large number of stocks enables grouping based on their stochastic properties. A hierarchical dynamic Bayesian framework enables predictive modeling of vector transaction counts in regularly spaced short time intervals. These approaches may be combined by financial analysts in order to provide detailed analysis of these large financial databases.
Committee
Workshop
Key Dates
Communication
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