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Found 6 papers, 0 papers with code
Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints
This paper analyzes the statistical properties of constrained portfolio formation in a high dimensional portfolio with a large number of assets.
Deep Learning Based Residuals in Non-linear Factor Models: Precision Matrix Estimation of Returns with Low Signal-to-Noise Ratio
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 framework.
Generalized Linear Models with Structured Sparsity Estimators
Since it is not clear how a particular feasible-weighted nodewise regression may fit in an oracle inequality for penalized Generalized Linear Model, we need a second oracle inequality to get oracle bounds for the approximate inverse for the sample estimate of second-order partial derivative of Generalized Linear Model.
Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso
We consider situations where a user feeds her attributes to a machine learning method that tries to predict her best option based on a random sample of other users.
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models
Since the nodewise regression is not feasible due to the unknown nature of idiosyncratic errors, we provide a feasible-residual-based nodewise regression to estimate the precision matrix of errors which is consistent even when number of assets, p, exceeds the time span of the portfolio, n. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with an increasing number of factors and p>n.
Oracle Inequalities for Convex Loss Functions with Non-Linear Targets
We give two examples of loss functions covered by our framework and show how penalized nonparametric series estimation is contained as a special case and provide a finite sample upper bound on the mean square error of the elastic net series estimator.
