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High Frequency Data: Modeling Durations via the ACD and Log ACD Models

By Lilian Cheung
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Abstract

This thesis proposes a method of finding initial parameter estimates in the Log ACD1 model for use in recursive estimation. The recursive estimating equations method is applied to the Log ACD1 model to find recursive estimates for the unknown parameters in the model. A literature review is provided on the ACD and Log ACD models, and on the theory of estimating equations. Monte Carlo simulations indicate that the proposed method of finding initial parameter estimates is viable. The parameter estimation process is demonstrated by fitting an ACD model and a Log ACD model to a set of IBM stock duration data

Topics: Time Series, High Frequency Data, ACD and Log ACD Duration Models, Initial Parameter Estimation, Recursive Estimating Equations, Monte Carlo Simulations, Applied Mathematics, Longitudinal Data Analysis and Time Series, Mathematics, Other Applied Mathematics, Other Mathematics, Statistics and Probability
Publisher: OpenCommons@UConn
Year: 2014
OAI identifier: oai:opencommons.uconn.edu:srhonors_theses-1395
Provided by: OpenCommons at University of Connecticut
Downloaded from https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=1395&context=srhonors_theses

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