Analysis of high frequency data using stochastic volatility model with long memory


High frequency data (HFD) become more and more common in finance and instudry.  Stochastic volatility (SV) model is propsed to model the mean and unobserved volatility.  This project investigates different dynamic modeling strategies for the features in the HFD including the long memory, heavy skewness, dynamic kurtosis and others within the SV model using a Bayesian approach.  We will investigate application to cryptocurrency research.


Associate Professor Jennifer Chan

Research Location

School of Mathematics and Statistics

Program Type



Financial time series modelling using Bayesian method

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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high frequency data, stochastic volatility model, Bayesian method

Opportunity ID

The opportunity ID for this research opportunity is: 1425

Other opportunities with Associate Professor Jennifer Chan