Gianluca De Nard
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Factor-Mimicking Portfolios for Climate Risk Open
We propose and implement a procedure to optimally hedge climate change risk. First, we construct climate risk indices through textual analysis of newspapers. Second, we present a new approach to compute factor-mimicking portfolios to build…
Improved inference in financial factor models Open
Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama–French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to…
Subsampled factor models for asset pricing: The rise of Vasa Open
We propose a new method, variable subsample aggregation (VASA), for equity return prediction using a large‐dimensional set of factors. To demonstrate the effectiveness, robustness, and dimension reduction power of VASA, we perform a compar…
Large dynamic covariance matrices: Enhancements based on intraday data Open
Multivariate GARCH models do 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 uncondi…
Large dynamic covariance matrices: enhancements based on intraday data Open
Multivariate GARCH models do 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 uncondi…
Factor models for portfolio selection in large dimensions: the good, the better and the ugly Open
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 conditional het…
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly Open
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 co…