Find us on Facebook Find us on LinkedIn Follow us on Twitter Subscribe to our YouTube channel

Research Students

Some current Business Analytics PhD students

Research Student Thesis Topic Thesis Description
Wilson Chen Estimation and Forecast of Value-at-Risk using Bayesian Methods 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.

Cheng Qian Multidimensional supplier bidding and bargaining We consider a situation where a set of suppliers with asymmetric costs bid on price and non-price attributes (e.g., quality variables) in a scoring auction to win an indivisible contract from the buyer. Only one supplier will be selected. We first investigate the bidding process in the commitment case, i.e., the bidding outcome will be implemented immediately and there would be no negotiation after the bidding. We consider uncertainty on scores, uncertainty on attribute weights, combined uncertainty on scores and weights, and uncertainty on competitors' costs in the bidding process. By studying the equilibrium decisions of the buyer and suppliers, we will answer the following questions in the commitment case: How should a potential supplier choose the price and non-price attributes in his/her bid?
What is the effect of each type of uncertainty on the buyer and suppliers' equilibrium behavior? How would information revelation by each player affect the outcome and in particular the amount of pro t made by the suppliers?

Then we consider the no-commitment case where the buyer would initiate a negotiation with one or more suppliers after the bidding. We are interested in how a bilateral multidimensional bargaining or a multilateral multidimensional bargaining as the second stage would affect the bidding strategies at the first stage.

Professor Eddie Anderson is the principle supervisor.
Lusheng Shao Supply bidding under demand uncertainty Demand uncertainty increases the risks for both suppliers and buyer. To deal with this risk nowadays, suppliers may offer different types of supply contracts, such as supply options and buy back mechanisms. We are interested in the situations under which many suppliers compete with each other on the supply of homogeneous products to the buyer for a variety of types of supply contracts. Several questions promote us further to conduct research on the particular area: (1) Is there a equilibrium for the suppliers to bid under different cost structures for production? (2) What is the optimal contract design for the buyer? (3) What is the impact of contract types on the supply chain's performance? Our interdisciplinary research topics involve economics, marketing as well as operations management.

Professor Eddie Anderson is the principle supervisor and Dr Erick Li is the associate supervisor.

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

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

Christian Contino Estimation, Inference and Forecasting for Value-at-Risk and Conditional Value-at-Risk using Bayesian GARCH Methods and High Frequency Intra-Day Data Financial institutions are constantly looking for better ways to both manage and quantify risk following the regulatory disclosure of Value-at-Risk (VaR). An incorrect estimation could jeopardize the returns or risk profile of an institution, leading to loss of confidence from stakeholders. The proposed model introduces high frequency intra-day data into a traditionally daily GARCH specification via mixed data sampling, MIDAS. The goal is to increase accuracy, especially for short term forecasts of both VaR and Conditional VaR. Monte Carlo Markov Chain (MCMC) methods will be employed within a Bayesian framework for inference and parameter estimation. The resulting models should provide application in risk management, derivative pricing and portfolio optimization.
Supervisor: Associate Professor Richard Gerlach
Rasika Yatigammana Modelling of liquidity risks through the estimation and forecasting of an ACD model In financial econometrics, vital information regarding the current state of financial market activity is revealed by the dynamics of duration between two consecutive trades or durations. Information that could be gleaned from this series is most useful for risk management, portfolio optimisation, hedging and also for managing liquidity and investigation of market microstructure theories. Liquidity is an important factor for many stakeholders in the financial market and duration is an obvious indicator of financial market liquidity, with its variance implying liquidity risk. This research would focus on Autoregressive Conditional Duration (ACD) models for the estimation and forecasting of liquidity risks primarily through the application of Bayesian methodology. Thus far, limited attention has been paid to reliable estimation and inference on ACD parameters as well as on finite sample and asymptotic properties of estimators. Since it is well established that the financial data exhibit extensive changes in its properties over time, methods for identification and estimation of structural breaks in duration data too would be developed, employing Markov chain Monte Carlo (MCMC) techniques.
Supervisor: Associate Professor Richard Gerlach/ Associate Professor Shelton Peiris