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: Professor Richard Gerlach / Associate Professor Shelton Peiris
- Time series modelling
- Duration models
- Bayesian inference
- Markov chain Monte Carlo simulations