Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Garcin, Matthieu"'
Autor:
Angelini, Daniele, Garcin, Matthieu
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 a random Hurst exponent $H_t$, driven by a fractional Ornstein-Uhlenb
Externí odkaz:
http://arxiv.org/abs/2409.07159
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 alternation of bids and asks, we propose moment-based estimators along wi
Externí odkaz:
http://arxiv.org/abs/2407.17401
Autor:
Brouty, Xavier, Garcin, Matthieu
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, we study the link between the two approaches. We thus provide a theore
Externí odkaz:
http://arxiv.org/abs/2306.13371
Autor:
Garcin, Matthieu
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 of a bandwidth parameter. Many selection methods have been proposed in
Externí odkaz:
http://arxiv.org/abs/2305.13123
Autor:
Brouty, Xavier, Garcin, Matthieu
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 entropy, applied to a symbolic representation of this time series. By d
Externí odkaz:
http://arxiv.org/abs/2208.11976
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 delimits the tails of a distribution. We propose a new method to optimall
Externí odkaz:
http://arxiv.org/abs/2111.11128
Autor:
Garcin, Matthieu, Stéphan, Samuel
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 gives quite good results on the dataset and the neural network can impro
Externí odkaz:
http://arxiv.org/abs/2107.07206
Autor:
Brouty, Xavier, Garcin, Matthieu
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena March 2024 180
Autor:
Garcin, Matthieu
The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for example that log-prices follow an fBm, one can exploit the non-Markovian n
Externí odkaz:
http://arxiv.org/abs/2105.09140
Autor:
Garcin, Matthieu, Grasselli, Martino
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 into the analysis. Our findings lead to new stylized facts in the log-l
Externí odkaz:
http://arxiv.org/abs/2008.07822