High Dimensional Covariance Matrix Estimations and Factor Models

Large covariance matrix estimation is crucial for high-dimensional statistical inferences, and has also played an central role in factor analysis. Applications are found in analyzing financial risks, climate data, genomic data and PCA, etc. Commonly used approaches to estimating large covariances include shrinkages and sparse modeling. This talk will present new theoretical results on estimating large (inverse) covariance matrices under large N large T asymptotics, with a focus on the roles it plays in statistical inferences for large panel data and factor models. The application examples will include efficient estimation for factor analysis and portfolio allocations under large assets.