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Gianluca De Nard


ZURICH

University of Zurich

Department of Economics

Zürichbergstrasse14

8032 Zürich

Switzerland


Office:

ZUH-G05

 

E-Mail:

gianluca.denard@econ.uzh.ch


NEW YORK

NYU Stern School of Business

Volatility Institute

44 West 4th Street

10025 NY

United States


Office:

Suite 9-66


E-Mail:

denard@stern.nyu.edu


 

 

 

 
 
 

About ME 

 

I am a Senior Research Associate at the Department of Economics at the University of Zurich as well as a Research Fellow at the NYU Stern Volatility and Risk Institute. I am also a Lecturer in Empirical Finance at the Department of Banking and Finance at the University of Zurich and Head of Quantitative Research at OLZ AG. Previously, I have been working as a Postdoctoral Researcher at Yale University. My primary research interests are Financial Econometrics and Machine Learning with applications in Empirical Asset Pricing and Sustainability/Climate Risk. In New York and Yale I am working together with Nobel laureate Robert F. Engle and Bryan Kelly. More specifically, we publish in real- time risk signals and international financial market data on volatility, correlations, systemic and climate risk: https://vlab.stern.nyu.edu


I have several publications (8) and promising working papers on (i) more powerful tests for cross-sectional anomalies; (ii) new machine learning methods for stock return prediction; (iii) a double-shrinkage estimator for taming the factor zoo; and (iv) improved inference methods for financial factor models, all with applications in Finance and Economics.

 

A main focus is on the estimation of robust large-dimensional covariance matrices. I have already published two papers in the Journal of Financial Econometrics and one paper in the Journal of Empirical Finance: two co-authored papers on factor models for portfolio selection as well as a single-author paper on new shrinkage techniques for multi-asset investing. A related paper is on large dynamic covariance matrix estimation with high-frequency data, co-authored with Robert F. Engle, Olivier Ledoit and Michael Wolf. In this paper, we show how performance can be increased by using open/high/low/close (OHLC) price data. Note that I was invited to present my work at the special Society for Financial Econometrics Seminar Series. Additionally, the paper was nominated for the Swiss Risk Award and published in the Journal of Banking and Finance.


At NYU and Yale I am currently working on a new machine learning paradigm for climate finance applications and empirical asset pricing. For example, I have two ongoing projects together with Robert F. Engle and Bryan Kelly on how to hedge climate risk, by building a climate change index based on textual analysis of news papers and derive a suitable mimicking or hedging portfolio. Additionally, we are working on big data and shrinkage methods to improve asset pricing models. For example, I work on two promising working papers, together with Bryan Kelly and Markus Leippold, that try to use clusters or subsamples of large data sets to predict asset returns and estimate higher moments. Finally, I was invited to work on the international Finance Crowd Analysis Project where we published our findings in the Journal of Finance. 



For more information about research, teaching and consulting please take a closer look at my webpage or use the contact form.