Marc Paolella
Professor of Empirical Finance at University of Zurich
SFI Faculty Member at Swiss Finance Institute
Biography
University of Zurich
Education
- Habilitation: 2002 - Christian Albrechts Universität zu Kiel
- Dr. sc.pol.: 1998 - Christian Albrechts Universität zu Kiel
- M.S. Statistics: 1993 - Colorado State University
- B.A. Economics and B.S. Applied Mathematics and Statistics 1990, State University of New York at Stony Brook
Research Interest
- Time Series Analysis, Computational Statistics, GARCH and Risk Prediction
Teaching
- Econometrics I & II
- Forschungsseminar "Finance"
- Internes Finanzwirtschaftliches Forschungsseminar
- Time series analysis
Publication
*Book *
- Fundamental Statistical Inference: A Computational Approach Paolella Marc John Wiley & Sons, New York, January 2018
- Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH Paolella Marc John Wiley & Sons, New York, January 2018
- Fundamental statistics: a computational approach Paolella Marc Epub ahead of print, January 2013
- Intermediate statistics: a computational approach Paolella Marc Epub ahead of print, January 2013
- Intermediate Probability: A Computational Approach Paolella Marc West Sussex, England, January 2007
- Fundamental Probability: A Computational Approach Paolella Marc West Sussex, England, January 2006
Swiss Finance Institute
Marc Paolella is Professor of Empirical Finance at the University of Zurich. Professor Paolella is the author of several books on graduate-level probability, statistics, and time series analysis. His research papers have been published in the top academic journals in his areas of expertise.
Expertise
Professor Paolella is studying different modeling techniques that claim to better forecast financial assets' returns and to better select an optimal portfolio. One of the key challenges in asset management is to walk the fine line between sensitivity to new data points and stability, thus avoiding excessive rebalancing. His newly developed model provides both increased risk stability and reduced transaction costs. An empirical analysis of the market crash caused by the COVID-19 pandemic shows that the model reacts in a timely manner to sudden market downturns and effectively minimizes financial losses. Overall, investors could benefit from these findings by improving their portfolio selection and updating their methodologies, as further financial crises are inevitable.
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