معرفی
Richard Gerlach is a Professor at the University of Sydney, specializing in financial econometrics and time series analysis. His research focuses on developing Bayesian and semi-parametric models for forecasting and managing financial risk, with applications in volatility modeling, Value-at-Risk, and Expected Shortfall. He has published extensively in top-tier journals including the Journal of Financial Econometrics and Quantitative Finance.
His research interests span:
- Financial econometrics and time series forecasting
- Bayesian methods for risk management
- Nonlinear heteroskedastic models
- High-frequency financial data analysis
- Volatility and tail risk modeling
Gerlach's recent publications (2020-2025) predominantly explore Bayesian approaches to financial risk forecasting, with recurring themes in semi-parametric volatility modeling, tail risk quantification, and machine learning applications in finance. His work frequently integrates realized measures from high-frequency data to enhance predictive accuracy.
He currently advises PhD students including Jacky LYU (working on semi-parametric financial risk forecasting) and Wen PENG (researching Bayesian neural networks for volatility dynamics).
Notable grants awarded include:
- ARC Discovery Project: 'Deep learning based time series modeling and financial forecasting' (2020)
- Parametric and Semi-Parametric Financial Tail Risk Forecasting (Sydney Business School Pilot Research Scheme, 2020)
- ARC Discovery Project: 'Bayesian Inference for Flexible Parametric Multivariate Econometric modelling' (2008)
