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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


Phone:

 +1 (212) 998-0081


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


 

 

 
 
 

NYU Stern Volatility and Risk Institute


For the academic year 2019/2020 I have been working at the NYU Stern Volatility and Risk Institute as visiting PhD scholar supervised by Nobel prize winner Prof. Robert F. Engle. For my stay in New York the Swiss National Science Foundation (SNF) has awarded a fellowship. Due to my work and projects of joint interest, I became a member and research fellow of the NYU Stern Volatility and Risk Lab.


Volatility and Risk Institute:

Homepage


V-Lab:

https://vlab.stern.nyu.edu/about


Ongoing Projects:

There are two promising ongoing research projects together with Robert F. Engle and Bryan Kelly. The projects are related to climate finance and financial econometrics. The main idea is to derive a climate change index based on textual analysis of newspapers and compute a mimicking portfolio to hedge climate (change) risk.


Finished SNF Funded Projects:

We present the research plan for the Multivariate Volatility Modeling project at the NYU Volatility Institute with direct collaboration and supervision of Prof. Robert F. Engle. We develop a new generalized linear shrinkage target, which can be used for large-dimensional covariance matrix estimation. The proposed covariance matrix estimator can be applied for various fields of research (e.g. Statistics, Economics, Biology, Engineering, etc.). However, we focus on the accurate large-dimensional multivariate volatility estimation of multi-asset class returns and its application to portfolio selection, which has been neglected in the recent literature. Additionally, we plan to extend and combine the generalized shrinkage estimator with factor and dynamic conditional correlation models.

We expect that the proposed generalized constant-variance-covariance shrinkage estimator will outperform even latest shrinkage techniques, as it is based only on a statistical framework and therefore has less restrictive assumptions. Especially it should deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns and thus will contribute significantly towards the covariance matrix estimation literature. We plan to run an extensive empirical simulation to evaluate the out-of-sample performance of the proposed estimator.

In summary, the goal of the project is to contribute to the development of the next generation of multivariate volatility models that combine dynamic conditional correlation and factor models with a new generalized and well-conditioned shrinkage estimator.


Results:

The paper "Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage" forthcoming in the Journal of Financial Econometrics; and my Job Market paper "Large Dynamic Covariance Matrices: Enhancements Based on Intraday Data", co-authored with Robert Engle, Olivier Ledoit and Michael Wolf,