BSc UTS PhD AGSM A.Stat.
H04 - Merewether Building
The University of Sydney
NSW Australia 2006
|Telephone||+61 2 9351 3944|
|Fax||+61 2 9351 6409|
Richard Gerlach's research interests lie mainly in financial econometrics and time series. His work has concerned developing time series models for measuring, forecasting and managing risk in financial markets as well as computationally intensive Bayesian methods for inference, diagnosis, forecasting and model comparison for these models. Recent focus has been on nonlinear threshold heteroskedastic models for volatility, Value-at-Risk and Expected Shortfall forecasting. He has developed structural break and intervention detection tools for use in state space models; also has an interest in estimating logit models incorporating misclassification and variable selection. His applied work has involved forecasting risk levels during and after the Global Financial Crisis; assessing asymmetry in major international stock markets, in response to local and exogenous factors; co-integration analysis assessing the effect of the Asian financial crisis on long term relationships between international real estate investment markets; stock selection for financial investment using logit models; option pricing and hedging involving barriers; and factors influencing the 2004 Federal election.
His research papers have been published in Journal of the American Statistical Association, Journal of Business and Economic Statistics, Journal of Time Series Analysis and the International Journal of Forecasting. He has been an invited speaker and regular presenter at international conferences such as the International conference for Computational and Financial Econometrics, the International Symposium on Forecasting and the International Statistical Institute sessions.
- Computational and Financial econometrics
- Bayesian statistics
- Financial risk forecasting and management
- Computationally intensive statistical methods
- Data analysis
- Markov Chain Monte Carlo Estimation
- Time series econometrics