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Found 18 papers, 5 papers with code
Liquidity takers behavior representation through a contrastive learning approach
Thanks to the access to the labeled orders on the CAC40 data from Euronext, we are able to analyze agents' behaviors in the market based on their placed orders.
The self-exciting nature of the bid-ask spread dynamics
The bid-ask spread, which is defined by the difference between the best selling price and the best buying price in a Limit Order Book at a given time, is a crucial factor in the analysis of financial securities.
From Rough to Multifractal volatility: the log S-fBM model
We introduce a family of random measures $M_{H, T} (d t)$, namely log S-fBM, such that, for $H>0$, $M_{H, T}(d t) = e^{\omega_{H, T}(t)} d t$ where $\omega_{H, T}(t)$ is a Gaussian process that can be considered as a stationary version of an $H$-fractional Brownian motion.
Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data
Estimating the amount of electricity that can be produced by rooftop photovoltaic systems is a time-consuming process that requires on-site measurements, a difficult task to achieve on a large scale.
About contrastive unsupervised representation learning for classification and its convergence
Contrastive representation learning has been recently proved to be very efficient for self-supervised training.
ZiMM: a deep learning model for long term and blurry relapses with non-clinical claims data
We consider a long-term (18 months) relapse (urination problems still occur despite surgery), which is blurry since it is observed only through the reimbursement of a specific set of drugs for urination problems.
SCALPEL3: a scalable open-source library for healthcare claims databases
SCALPEL-Extraction provides fast concept extraction from a big table such as the one produced by SCALPEL-Flattening.
Distributed, Parallel, and Cluster Computing Computers and Society
Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption
The minimization of convex objectives coming from linear supervised learning problems, such as penalized generalized linear models, can be formulated as finite sums of convex functions.
ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection
With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening.
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.
Uncovering Causality from Multivariate Hawkes Integrated Cumulants
We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process.
Mean-field inference of Hawkes point processes
We propose a fast and efficient estimation method that is able to accurately recover the parameters of a d-dimensional Hawkes point-process from a set of observations.
SGD with Variance Reduction beyond Empirical Risk Minimization
We introduce a doubly stochastic proximal gradient algorithm for optimizing a finite average of smooth convex functions, whose gradients depend on numerically expensive expectations.
Hawkes processes in finance
In this paper we propose an overview of the recent academic literature devoted to the applications of Hawkes processes in finance.
Trading and Market Microstructure
Sparse and low-rank multivariate Hawkes processes
We consider the problem of unveiling the implicit network structure of node interactions (such as user interactions in a social network), based only on high-frequency timestamps.
Concentration for matrix martingales in continuous time and microscopic activity of social networks
This paper gives new concentration inequalities for the spectral norm of a wide class of matrix martingales in continuous time.
Estimation of slowly decreasing Hawkes kernels: Application to high frequency order book modelling
We present a modified version of the non parametric Hawkes kernel estimation procedure studied in arXiv:1401. 0903 that is adapted to slowly decreasing kernels.
Statistical Finance Trading and Market Microstructure Methodology
Second order statistics characterization of Hawkes processes and non-parametric estimation
In this paper, we perform a systematic study of this non-parametric estimation procedure in the general framework of marked Hawkes processes.
Methodology Statistics Theory Geophysics Statistical Finance Trading and Market Microstructure Statistics Theory
