4 citations · 11 across the 15 of their papers we have counts for
16 papers
Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection
Mathis Fourreau, Matthieu Garcin
The composite likelihood method reduces the computational cost of parameter estimation in time series by considering several subsets of observations instead of all observations at…
Directional Dependence of Extreme Events
Matthieu Garcin, Maxime L. D. Nicolas
This paper introduces a novel measure to quantify the directional dependence of extreme events between two variables. The proposed approach is designed to capture asymmetric tail d…
Asymptotic and finite-sample distributions of one- and two-sample empirical relative entropy
Matthieu Garcin, Louis Perot
In the perspective of building statistical tests of divergence between two probability distributions, we study the distribution of empirical relative entropy and derive several typ…
Prediction of linear fractional stable motions using codifference, with application to non-Gaussian rough volatility
Matthieu Garcin, Karl Sawaya, Thomas Valade
The linear fractional stable motion (LFSM) extends the fractional Brownian motion (fBm) by considering -stable increments. We propose a method to forecast future increments of t…
Market information of the fractional stochastic regularity model
Daniele Angelini, Matthieu Garcin
The Fractional Stochastic Regularity Model (FSRM) is an extension of Black-Scholes model describing the multifractal nature of prices. It is based on a multifractional process with…
Estimation of bid-ask spreads in the presence of serial dependence
Xavier Brouty, Matthieu Garcin, Hugo Roccaro
Starting from a basic model in which the dynamic of the transaction prices is a geometric Brownian motion disrupted by a microstructure white noise, corresponding to the random alt…
Fractal properties, information theory, and market efficiency
Xavier Brouty, Matthieu Garcin
Considering that both the entropy-based market information and the Hurst exponent are useful tools for determining whether the efficient market hypothesis holds for a given asset,…
Complexity measure, kernel density estimation, bandwidth selection, and the efficient market hypothesis
Matthieu Garcin
We are interested in the nonparametric estimation of the probability density of price returns, using the kernel approach. The output of the method heavily relies on the selection o…
A statistical test of market efficiency based on information theory
Xavier Brouty, Matthieu Garcin
We determine the amount of information contained in a time series of price returns at a given time scale, by using a widespread tool of the information theory, namely the Shannon e…
Nonparametric estimator of the tail dependence coefficient: balancing bias and variance
Matthieu Garcin, Maxime L. D. Nicolas
A theoretical expression is derived for the mean squared error of a nonparametric estimator of the tail dependence coefficient, depending on a threshold that defines which rank del…
Credit scoring using neural networks and SURE posterior probability calibration
Matthieu Garcin, Samuel Stephan
In this article we compare the performances of a logistic regression and a feed forward neural network for credit scoring purposes. Our results show that the logistic regression gi…
Forecasting with fractional Brownian motion: a financial perspective
Matthieu Garcin
The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for exam…
Long vs Short Time Scales: the Rough Dilemma and Beyond
Matthieu Garcin, Martino Grasselli
Using a large dataset on major FX rates, we test the robustness of the rough fractional volatility model over different time scales, by including smoothing and measurement errors i…
Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics
Ayoub Ammy-Driss, Matthieu Garcin
This paper investigates the impact of COVID-19 on financial markets. It focuses on the evolution of the market efficiency, using two efficiency indicators: the Hurst exponent and t…
Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets
Matthieu Garcin, Jules Klein, Sana Laaribi
The time-varying kernel density estimation relies on two free parameters: the bandwidth and the discount factor. We propose to select these parameters so as to minimize a criterion…
A comparison of maximum likelihood and absolute moments for the estimation of Hurst exponents in a stationary framework
Matthieu Garcin
The absolute-moment method is widespread for estimating the Hurst exponent of a fractional Brownian motion . But this method is biased when applied to a stationary version of $X…