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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


 

 

 
 
 


Research 

 

My research involves the development of new statistical methods in the areas of Applied Financial Econometrics and Statistics. More specifically, I focus on multivariate GARCH models and shrinkage estimation techniques of large-dimensional covariance matrices for portfolio and asset management applications. Additionally, we develop a subsampling method for asset return prediction and a double-shrinkage estimator for taming the factor zoo.

 

Job Market Paper:


Large Dynamic Covariance Matrices: Enhancements Based on Intraday Data

(with Robert Engle, Olivier Ledoit and Michael Wolf, 2020)


Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic covariances, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign (function) of the observed return.


Journal of Banking and Finance (Revise and Resubmit)

Nominated for the Swiss Risk Award

Presented at the Society for Financial Econometrics (click here to see the presentation)



Journal Publications:


This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.

 


Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides the definition and derivation of a new asymptotically optimal linear shrinkage estimator we propose an adaptive Blockbuster algorithm that clusters the covariance matrix even if the (number of) asset classes are unknown and change over time. It displays superior all-around performance on historical data against a variety of state-of-the-art linear shrinkage competitors. Additionally, we find that for small and medium-sized investment universes the proposed estimator outperforms even recent nonlinear shrinkage techniques. Hence, this new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns. Furthermore, due to the general structure of the proposed Blockbuster shrinkage estimator the application is not restricted to financial problems.


Working Papers:

This paper injects factor structure into the estimation of time-varying, covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals and factors in the presence of conditional heteroskedasticity for a large number of stocks and factors. Therefore, we introduce a new double-shrinkage estimator that first shrinks the factor model coefficients and then applies (dynamic) nonlinear shrinkage to the residuals and factors. The proposed covariance matrix estimator blends a regularized factor structure with conditional heteroskedasticity of residuals and factors in large dimensions up to 1000 stocks and 99 factors. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including dynamic exact and approximate factor models based on observed as well as latent factors. We show that for the proposed double-shrinkage estimator it is ‘enough’ to use only the market factor, or the most important latent factor(s), and thus there is no need to laboriously taking into account the entire factor zoo. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.

Submitted to the Review of Financial Studies

 

 We propose a new method, VASA, based on variable subsample aggregation of model predictions for equity returns using a large-dimensional set of factors. To demonstrate the effectiveness, robustness and dimension reduction power of VASA, we perform a comparative analysis between state-of-the-art machine learning algorithms. As a performance measure, we explore not only the global predictive but also the stock-specific R2’s and their distribution. While the global R2 indicates the average forecasting accuracy, we find that high variability in the stock-specific R2’s can be detrimental for the portfolio performance, due to the higher prediction risk. Since VASA shows minimal variability, portfolios formed on this method outperform the portfolios based on more complicated methods like random forests and neural nets.


Submitted to Management Science


Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long-short portfolios based on their factor scores, with microcaps alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for the (mimicking) portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. Thus, we propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, ‘Student’ t-statistics more than double.


Submitted to the Journal of Applied Econometrics


Conditional heteroskedasticity of the error terms is a common occurrence in CAPM regressions and multi-factor models, which necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference. In this paper, we show that the weighted least squares and especially the adaptive least squares, generally lead to smaller HC standard errors compared to ordinary least squares, which translates into improved inference in the form of shorter confidence intervals and more powerful hypothesis tests. In an extensive empirical analysis based on historical stock returns and common factors, we find that especially during times of financial turmoil conditional heteroskedasticity is omnipresent and that for some periods the proposed methods almost cut in half confidence intervals.


Work in Progress:


  • A Climate Factor Mimicking Portfolio (with Robert Engle)
  • Large Subsampled Covariance Matrices (with Simon Hediger)
  • Applied Linear and Nonlinear Shrinkage Estimation of the Covariance Matrix: An Application for Benchmarked Managers
  • etc.