Gianluca De Nard
NYU Stern School of Business
44 West 4th Street
+1 (212) 998-0081
University of Zurich
Department of Economics
+41 (0)44 634 56 95
My research involves the development of new machine learning and econometric methodologies for empirical finance, climate finance, asset pricing, as well as asset and risk management applications. More specifically, my research interests are threefold: a) I focus on hedging climate risk via an efficient factor mimicking portfolio approach for various climate (change) indices; b) I develop multivariate GARCH models and shrinkage estimation techniques of large-dimensional covariance matrices for portfolio and asset management applications; and c) I present machine learning methods for empirical asset pricing models based on big data.
Work in Progress:
Job Market Paper:
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
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 the International Journal of Forecasting
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.
Submitted to the Journal of Empirical Finance
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 International Review of Economics and Finance