2 citations · 4 across the 6 of their papers we have counts for
17 papers
Noise-proofing Universal Portfolio Shrinkage
Paul Ruelloux, Christian Bongiorno, Damien Challet
We enhance the Universal Portfolio Shrinkage Approximator (UPSA) of Kelly et al. (2023) by making it more robust with respect to estimation noise and covariate shift. UPSA optimize…
Covariance matrix filtering and portfolio optimisation: the Average Oracle vs Non-Linear Shrinkage and all the variants of DCC-NLS
Christian Bongiorno, Damien Challet
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 Co…
Optimal Covariance Cleaning for Heavy-Tailed Distributions: Insights from Information Theory
Christian Bongiorno, Marco Berritta
In optimal covariance cleaning theory, minimizing the Frobenius norm between the true population covariance matrix and a rotational invariant estimator is a key step. This estimato…
Statistical inference of lead-lag at various timescales between asynchronous time series from p-values of transfer entropy
Christian Bongiorno, Damien Challet
Symbolic transfer entropy is a powerful non-parametric tool to detect lead-lag between time series. Because a closed expression of the distribution of Transfer Entropy is not known…
Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimisation
Christian Bongiorno, Damien Challet
Portfolio optimization requires sophisticated covariance estimators that are able to filter out estimation noise. Non-linear shrinkage is a popular estimator based on how the Oracl…
Cleaning the covariance matrix of strongly nonstationary systems with time-independent eigenvalues
Christian Bongiorno, Damien Challet, Grégoire Loeper
We propose a data-driven way to reduce the noise of covariance matrices of nonstationary systems. In the case of stationary systems, asymptotic approaches were proved to converge t…