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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/12462
Title: Robust Estimation of Autoregressive Conditional Duration Models
Authors: El, Sebai S Rola
Advisor: Chen, Bei
Balakrishnan, Narayanaswamy
Viveros-Aguilera, Roman
Department: Mathematics and Statistics
Keywords: Robust Estimation of ACD Models;Applied Statistics;Longitudinal Data Analysis and Time Series;Applied Statistics
Publication Date: Oct-2012
Abstract: <p>In this thesis, we apply the Ordinary Least Squares (OLS) and the Generalized Least Squares (GLS) methods for the estimation of Autoregressive Conditional Duration (ACD) models, as opposed to the typical approach of using the Quasi Maximum Likelihood Estimation (QMLE).</p> <p>The advantages of OLS and GLS as the underlying methods of estimation lie in their theoretical ease and computational convenience. The latter property is crucial for high frequency trading, where a transaction decision needs to be made within a minute. We show that both OLS and GLS estimates are asymptotically consistent and normally distributed. The normal approximation does not seem to be satisfactory in small samples. We also apply Residual Bootstrap to construct the confidence intervals based on the OLS and GLS estimates. The properties of the proposed methods are illustrated with intensive numerical simulations as well as by a case study on the IBM transaction data.</p>
URI: http://hdl.handle.net/11375/12462
Identifier: opendissertations/7348
8403
3315406
Appears in Collections:Open Access Dissertations and Theses

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