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Department of Finance Prof. Dr. Marc Paolella

Prof. Dr. Marc Paolella

Biography

Marc Paolella is a full professor of empirical finance in the Department of Finance at the University of Zurich. He teaches master's and PhD courses in financial econometrics (modeling and prediction of multivariate financial time series, advanced portfolio optimization methodologies, quantitative risk management, etc.); and master's courses in probability theory, modern computational statistical inference, advanced calculus, real analysis, and measure theory. He has single-authored four textbooks on the above topics (and one on martial arts).

Marc has published about 50 research papers on a variety of topics in statistics and financial econometrics, many of which appear in the leading scientific field journals, e.g., the Journal of Econometrics, the Journal of Financial Econometrics, the Journal of Banking and Finance, and the Journal of Multivariate Analysis, among others. Current research projects focus on developing computationally viable methods for financial portfolio construction that lead to outperformance (total return, tail risk, Sharpe ratios, max-drawdown, etc.) compared to not only the standard benchmarks (e.g., Markowitz, equal weighted, risk-parity, etc.), but also to popular academic constructs, such as the common multivariate GARCH models.

Marc is also a visiting professor at the University of Geneva, and has taught numerous master's and PhD courses in both Geneva and Lugano. As part of the steering committee of the specialized master's degree programme of Science ETH UZH in Quantitative Finance, he is instrumental in shaping this successful and highly competitive program.

Marc's personal web page https://www.marc-paolella.com/ contains a readable overview of his research agenda and output, books, and hobbies.

 

Research Interests

Continuous and Discrete Mixture Modeling; Distributional Testing; Financial Econometrics; Non-Elliptic Portfolio Optimization under Transaction Costs; Saddlepoint Approximation Theory; Time Series Analysis.