This paper tests how financial performance indicators and combined ESG score for large-
cap stocks impact on stock return. In particular, we examine how market capitalization, price …

This paper considers the specification of covariance structures with tail estimates. We focus
on two aspects:(i) the estimation of the VaR-CoVaR risk matrix in the case of larger number …

We develop and implement methods for determining whether relaxing sparsity constraints
on portfolios improves the investment opportunity set for risk-averse investors. We formulate …

These lecture notes provide an overview of existing methodologies and recent
developments for estimation and inference with high dimensional time series regression …

This paper addresses statistical inference for high-dimensional threshold regression
parameters. I establish oracle inequalities for the scaled LASSO estimator proposed by Lee …

This paper proposes a robust, shocks-adaptive portfolio in a large-dimensional assets
universe where the number of assets could be comparable to or even larger than the sample …

We develop uniform inference for high-dimensional threshold regression parameters and
valid inference for the threshold parameter in this paper. We first establish oracle …

This paper introduces a consistent estimator and rate of convergence for the precision matrix
of asset returns in large portfolios using a non-linear factor model within the deep learning …

In this paper, we connect the deep learning literature with non-linear factor models and
show that deep learning estimation leads to a substantial improvement in the non-linear …

We examine how many factors out of a wide range of 207 that have incremental information
in explaining cross-sectional stock returns. First, we find that the significance of each factor …