Marcel Scharth

Marcel Scharth is a Lecturer in Business Analytics at the University of Sydney Business School. His research focuses on business analytics, econometric and statistical methods. He is affiliated with the Centre for Translational Data Science at the University of Sydney, where he engages in cross-disciplinary research. Marcel's work includes Bayesian methods, computational statistics, statistical learning, time series, and causal inference. His research has been published in journals such as the Journal of Econometrics, The Review of Economics and Statistics, and the Journal of Business and Economic Statistics. He teaches courses in statistical learning and advanced statistics, with an emphasis on conceptual understanding and analytical thinking. Marcel also has an interest in the science of learning, online education, and educational innovation.

Marcel Scharth is a researcher in Econometrics and Statistics, focusing on advanced statistical methods for modeling and predicting financial volatility. His work explores time-varying parameters, stochastic volatility, and the impact of realized measures on volatility analysis. He develops and applies Monte Carlo methods, such as importance sampling and particle filters, to improve inference in state-space models. His research also addresses asymmetric effects, leverage, and heavy tails in financial data, with applications to high-dimensional factor models and risk assessment. Scharth's contributions span both theoretical and applied econometrics, emphasizing robust estimation and computational efficiency in complex financial systems.