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Gianluca De Nard


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



ZURICH

University of Zurich

Department of Economics

Zürichbergstrasse14

8032 Zürich

Switzerland


Office:

ZUH-G05

 

Phone:

 +41 (0)44 634 56 95


E-Mail:

gianluca.denard@bf.uzh.ch


 

 

 

 
 
 

May I Introduce MySelf? 

 

I am a Postdoctoral Researcher at Yale University and at the University of Zurich as well as a Research Fellow at the NYU Stern Volatility and Risk Institute. My primary research interests are Empirical Asset Pricing, Climate Finance and Financial Econometrics. 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 latest research and international financial market data on volatility, correlations, systemic and climate risk: https://vlab.stern.nyu.edu


My job market paper, co-authored with Robert Engle, Olivier Ledoit and Michael Wolf and 'revised and resubmitted' at the Journal of Banking & Finance, is on Large Dynamic Covariance Matrices and how intraday data can be used to improve estimation performance for applications in Portfolio and Risk Management.


Note that I have already published a single-author paper in the Journal of Financial Econometrics on generalized shrinkage estimators of the covariance matrix for (large-dimensional) multi-asset class portfolios. Additionally, Icollaboration with Michael Wolf and Olivier Ledoit we published a paper about approximate factor models in conjunction with robust multivariate GARCH models for better asset and risk management  solutions, also forthcoming in the Journal of Financial Econometrics.


Furthermore, I have various brand new and promising working papers on (i) a new test for cross-sectional anomalies; (ii) a new machine learning method for stock return prediction (with Simon Hediger and Markus Leippold); (iii) a double-shrinkage estimator for taming the factor zoo; and (iv) improved inference methods for the CAPM and multi-factor models (with Michael Wolf), all with applications in 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



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