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Wilson Chen

BCompSc (Hons) CSU
PhD Student


Thesis Topic: Estimation and Forecast of Value-at-Risk using Bayesian Methods

Thesis Description: Value-at-Risk (VaR) was introduced as a measure of market risk in the Amendment to Basel-I in 1996. Banks have an incentive to obtain accurate VaR forecasts to minimize their capital charge. A model that either over- or under-forecasts VaR will ultimately result in a higher capital charge. The aim of this thesis is to improve the accuracy upon the state-of-the-art forecasting models. To accomplish this goal, this thesis proposes a semi-parametric GARCH-type model that is flexible in the specification of volatility dynamics. The proposed model is more robust to misspecification, and is able to provide more accurate forecasts when the true volatility process departs from the parametric specifications. A key challenge is that the proposed model typically requires the estimation of a large number of parameters. As such, it is important to account for the uncertainty in parameter estimation. In a Bayesian setting, parameter uncertainty can be handled naturally by integrating out the parameters with respect to the posterior distribution. In this thesis, the posterior distribution is estimated using Markov Chain Monte Carlo (MCMC) algorithms. The estimated predictive distribution is then the functional of the estimated posterior distribution

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

Research Interests

  • Time-series modelling
  • Volatility models
  • Value-at-Risk forecasting
  • Bayesian inference
  • Computationally intensive Bayesian methods
  • Markov Chain Monte Carlo simulation algorithms