98 citations · 189 across the 16 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…
Financial factors selection with knockoffs: fund replication, explanatory and prediction networks
Damien Challet, Christian Bongiorno, Guillaume Pelletier
We apply the knockoff procedure to factor selection in finance. By building fake but realistic factors, this procedure makes it possible to control the fraction of false discovery…
A multi-layer network model to assess school opening policies during the COVID-19 vaccination campaign
Christian Bongiorno, Lorenzo Zino
We propose a multi-layer network model for the spread of COVID-19 that accounts for interactions within the family, between schoolmates, and casual contacts in the population. We u…
Vector-based Pedestrian Navigation in Cities
Christian Bongiorno, Yulun Zhou, Marta Kryven +5
How do pedestrians choose their paths within city street networks? Researchers have tried to shed light on this matter through strictly controlled experiments, but an ultimate answ…
Reactive Global Minimum Variance Portfolios with BAHC covariance cleaning
Christian Bongiorno, Damien Challet
We introduce a -fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices. We then apply this method to…
Bootstraps Regularize Singular Correlation Matrices
Christian Bongiorno
I show analytically that the average of bootstrapped correlation matrices rapidly becomes positive-definite as increases, which provides a simple approach to regularize sin…
Covariance matrix filtering with bootstrapped hierarchies
Christian Bongiorno, Damien Challet
Statistical inference of the dependence between objects often relies on covariance matrices. Unless the number of features (e.g. data points) is much larger than the number of obje…
Nonparametric sign prediction of high-dimensional correlation matrix coefficients
Christian Bongiorno, Damien Challet
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 featur…
Nested partitions from hierarchical clustering statistical validation
Christian Bongiorno, Salvatore Miccichè, Rosario N. Mantegna
We develop a greedy algorithm that is fast and scalable in the detection of a nested partition extracted from a dendrogram obtained from hierarchical clustering of a multivariate s…
Core of communities in bipartite networks
Christian Bongiorno, András London, Salvatore Miccichè +1
We use the information present in a bipartite network to detect cores of communities of each set of the bipartite system. Cores of communities are found by investigating statistica…
An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in Air Traffic Management
C. Bongiorno, S. Micciche', Rosario N. Mantegna
We present an agent based model of the Air Traffic Management socio-technical complex system that aims at modeling the interactions between aircrafts and air traffic controllers at…
An Empirically grounded Agent Based simulator for the Air Traffic Management in the SESAR scenario
Gérald Gurtner, Christian Bongiorno, Marco Ducci +1
In this paper we present a simulator allowing to perform policy experiments relative to the air traffic management. Different SESAR solutions can be implemented in the model to see…