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Found 7 papers, 0 papers with code
Covariance matrix filtering and portfolio optimisation: the Average Oracle vs Non-Linear Shrinkage and all the variants of DCC-NLS
The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC+NLS).
Statistical inference of lead-lag at various timescales between asynchronous time series from p-values of transfer entropy
On the other hand, the asymptotic distribution of Transfer Entropy between two time series is known.
Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimisation
Portfolio optimization requires sophisticated covariance estimators that are able to filter out estimation noise.
Vector-based Pedestrian Navigation in Cities
How do pedestrians choose their paths within city street networks?
Financial factors selection with knockoffs: fund replication, explanatory and prediction networks
We apply the knockoff procedure to factor selection in finance.
Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning
We introduce a $k$-fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices.
Nonparametric sign prediction of high-dimensional correlation matrix coefficients
We introduce a method to predict which correlation matrix coefficients are likely to change their signs in the future in the high-dimensional regime, i. e. when the number of features is larger than the number of samples per feature.
