Risk estimation algorithms based on Barra US Equity Model (USE4).
Including:
- Newey-West Serial Correlation Adjustment
- Eigenfactor Risk Adjustment
- Volatility Regime Adjustment
Covariance Estimation Methods:
- Linear LW and Non-linear LW
- OAS
- Garch estimation
Future works:
- Bayesian Shrinkage on Specific Risk
Dependencies:
- Python
- NumPy
- SciPy
- Sklearn
- Cvxpy
Elementary:
Maciej J. Capinski. Portfolio Theory and Risk Management.
Patrick Duvaut and Emmanuelle Jay. Risk-Based and Factor Investing
Peter Buhlmann. Statistics for High-Dimensional Data
Trevor Hastie. Statistical Learning with Sparsity The Lasso and Generalizations (Classic!)
Martin J. Wainwright. High-dimensional statistics: A Non-asymptotic Viewpoint
Roman Vershynin. High-Dimensional Probability with Applications in Data Science
Advanced:
Evarist Giné and Richard Nickl. Mathematical Foundations of Infinite-Dimensional Statistical Models
Peter Bülmann and Sara van de Geer, Statistics for High-dimensional Data: Methods, Theory and Applications
Wolfgang Karl, Applied Quantitative Finance
Alexandre B. Tsybakov, Introduction to Nonparametric Estimation
Roman Vershynin, Introduction to the Non-asymptotic Analysis of Random matrices
Vladimir Koltchinskii, Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems
Stéphane Boucheron, Gábor Lugosi and Pascal Massart, Concentration Inequalities: A Non-Asymptotic Theory of Independence
Sara van de Geer, Empirical processes in M-estimation
Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux. Dictionary Learning for Massive Matrix Factorization. International Conference on Machine Learning, Jun 2016, New York, United States. 2016