I am a Lecturer in Business Analytics at the University of Sydney Business School, where I specialise in the fields of statistics, econometrics, machine learning, and data science. I am also affiliated with the Centre for Translational Data Science. I received my Ph.D. from the VU University Amsterdam and the Tinbergen Institute.

My areas of expertise and interest are:

  • Bayesian methods.
  • Monte Carlo methods and computational statistics.
  • Statistical machine learning.
  • Time series.
  • Causal analysis for business applications.

Marcel


Research

Working Papers

"Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model" (with Mengheng Li)

"Markov Interacting Importance Samplers" (with Eduardo Mendes and Robert Kohn)
>> [abstract] [paper]

"Importance sampling squared for Bayesian inference and model choice with estimated likelihoods" (with Minh-Ngoc Tran, Michael K. Pitt and Robert Kohn)
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Publications

"Predicting time-varying parameters with parameter-driven and observation-driven models" (with S.J. Koopman and André Lucas). Review of Economics and Statistics, Volume 98, Issue 1, March 2016.
>> [abstract] [working paper] [published version]

"Particle Efficient Importance Sampling" (with Robert Kohn). Journal of Econometrics, Volume 190, Issue 1, January 2016, Pages 133–147.
>> [abstract] [working paper] [published version] [code]

"Numerically accelerated importance sampling for nonlinear non-Gaussian state space models" (with S.J. Koopman and André Lucas). Journal of Business and Economic Statistics, Volume 33, Issue 1, pages 114-127.
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"Modeling and predicting the CBOE market volatility index" (with Marcelo Fernandes and Marcelo C. Medeiros). Journal of Banking and Finance, Volume 40, March 2014, pages 1-10.
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"The analysis of stochastic volatility in the presence of daily realised measures" (with S.J. Koopman). Journal of Financial Econometrics, 11 (1), Winter 2013, 76-115.
>> [abstract] [paper]

"Asymmetric Effects and Long Memory in the Volatility of Dow Jones Stocks" (with Marcelo Medeiros). International Journal of Forecasting, 25, 304-327, 2009.
>> [abstract] [paper]


Teaching

Course material

Statistical Learning and Data Mining (QBUS6810, GitHub page)

Predictive Analytics (QBUS2820, forecasting section, GitHub page)

Short course

Machine Learning Using Python (MEAFA workshop)

Student resources

Python for Business Analytics (for students getting started with Python)

Linear Algebra review


Contact

Marcel Scharth
Discipline of Business Analytics
The University of Sydney Business School
University of Sydney NSW 2006
Australia

Telephone: +61 2 9036 9120
email: marcel.scharth [at] sydney.edu.au