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Chao Wang

Chao Wang

PhD Candidate

The University of Sydney
NSW 2006 Australia

Thesis Topic:

Range-Based GARCH Models for Value-at-Risk Estimation with Bayesian Approach

Thesis Description:

Now we are in a world saturated with data and information, and numerous quantitative methods for financial risk management are proposed and used by many financial research institutions and organizations within recent years. As a commonly used financial risk measurement, Vaule-at-Risk (VaR) summarizes risk through a single number. GARCH-type models are employed to capture the volatility clustering and the leverage effect of financial return series in my research. In order to capture the potential skewness and heavy tails in the conditional return distributions, the GARCH-type VaR forecast models are proposed with the assumption of different distributions, such as mixture of Student-t and mixture of Gaussian. A MCMC algorithm will be designed and implemented for inference and parameter estimation in a Bayesian framework, which is expected to show improved estimation properties compared to the frequentist. The proposed methods and models can be applied to forecast VaR for different market indices, exchange rates, and individual stocks. In addition, the proposed models will be modified and combined with range-based time series. The experimental results are expected to illustrate that the proposed models outperform, or are at least highly competitive with, several popular alternatives.

Professor Richard Gerlach is the principle supervisor and Dr Boris Choy is the associate supervisor.

Research Interests

  • Data Mining
  • Financial Time Series Modelling (GARCH, MGARCH)
  • Range-Based Time Series Models
  • Bayesian Econometrics
  • Markov Chain Monte Carlo
  • Value-at-Risk Forecasting