Multivariate volatility models for high frequency data

Summary

Volatility forecast is important in risk management. However since volatility is unobserved, most volatility models like the GARCH models are based on daily return and model volatility as a latent process. This unavoidably leads to the loss of intraday market information. In recent years, high frequency data in financial markets have been available and various volatility or realised volatility measures have been proposed. Fitting these volatility measure time series directly to the Conditional Autoregressive Range (CARR) model was shown to provide more efficient volatility forecast than the traditional GARCH model. Recently, this CARR model has been extended to multiple volatility measure time series enabling us to model covariance apart from variance. This project investigates various modelling strategies for modelling covariance and applies the models to stock markets and cryptocurrency markets.

Supervisor(s)

Associate Professor Jennifer Chan

Research Location

School of Mathematics and Statistics

Program Type

PHD

Synopsis

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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Keywords

high frequency data, Bayesian method, volatility measures, covariance regression, persistance

Opportunity ID

The opportunity ID for this research opportunity is: 1425

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