Semi-parametric estimation of Autoregressive Conditional Duration (ACD) models

Summary

Method of Maximum Likelihood (ML) is used to estimate ACD model parameters. Estimating Function (EF) is a popular semi-parametric approach in time series modelling. This project develops the EF approach for ACD modelling.

Supervisor(s)

Associate Professor Shelton Peiris

Research Location

School of Mathematics and Statistics

Program Type

Masters/PHD

Synopsis

The class of ACD models is very popular in financial econometrics. An efficient method of parameter estimation is necessary.

Additional Information

HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

Time Series, Estimating Functions, Semi-parametric, Efficiency, mathematics, Statistics

Opportunity ID

The opportunity ID for this research opportunity is: 1011

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