Rate My Professor Richard Gerlach

Gerlach has published extensively in high-impact journals. Key publications include "Efficient Bayesian Inference for Dynamic Mixture Models" (Journal of the American Statistical Association, 2000), "Bayesian estimation of smoothly mixing time-varying parameter models" (Computational Statistics & Data Analysis, 2011), "Bayesian estimation and inference for log-ACD models" (Computational Statistics, 2016), "Bayesian Assessment of Dynamic Quantile Forecasts" (International Journal of Forecasting, 2016), "Variational Bayes for assessment of dynamic quantile forecasts" (International Journal of Forecasting, 2017), "Bayesian Semi-Parametric Realized Conditional Autoregressive Range Models for Tail Risk Forecasting" (Journal of Financial Econometrics, 2020), and "A Bayesian realized threshold measurement GARCH framework for daily volatility and realized variance forecasting" (Journal of Forecasting, 2024). His Google Scholar profile records 3678 citations, underscoring his influence in financial time series, Bayesian MCMC, financial risk and forecasting. Gerlach is associated with the Time Series and Forecasting Research Group at the University of Sydney Business School, contributing to advancements in quantitative finance, asset pricing, financial risk management, predictive analytics and business analytics.