Principal component analysis (PCA)
Eigenportfolios of US equities for the exponential correlation model
In this paper, the eigendecomposition of a Toeplitz matrix populated by an exponential function in order to model empirical correlations of US equity returns is investigated.
Sometimes it’s fine to be boring
Diversification puts portfolios in the middle of the pack – where investors feel safe, writes Antonia Lim
How machine learning could aid interest rate modelling
Standard Chartered quant proposes machine-learning technique to better capture rate dynamics
Curve dynamics with artificial neural networks
Artificial neural networks can replace PCA for yield curves analysis
Flylets and fixed-income portfolio risk management
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Flylets and invariant risk metrics
Kharen Musaelian, Santhanam Nagarajan and Dario Villani show how to build robust risk metrics for bond returns
Stock selection with principal component analysis
The authors of this paper propose a stock selection method based on a variable selection method used with PCA in multivariate statistics.
Risk budgeting and diversification based on optimised uncorrelated factors
Meucci, Santangelo and Deguest introduce a risk decomposition method based on minimum-torsion bets