DeepAI AI Chat
Log In Sign Up

Clustering of discretely observed diffusion processes

by   Alessandro De Gregorio, et al.
Università degli Studi di Milano
Sapienza University of Rome

In this paper a new dissimilarity measure to identify groups of assets dynamics is proposed. The underlying generating process is assumed to be a diffusion process solution of stochastic differential equations and observed at discrete time. The mesh of observations is not required to shrink to zero. As distance between two observed paths, the quadratic distance of the corresponding estimated Markov operators is considered. Analysis of both synthetic data and real financial data from NYSE/NASDAQ stocks, give evidence that this distance seems capable to catch differences in both the drift and diffusion coefficients contrary to other commonly used metrics.


page 1

page 2

page 3

page 4


Nonparametric estimation of the diffusion coefficient from S.D.E. paths

Consider a diffusion process X=(X_t), with t in [0,1], observed at discr...

Non-parametric Estimation of Stochastic Differential Equations with Sparse Gaussian Processes

The application of Stochastic Differential Equations (SDEs) to the analy...

Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations

We propose a new method for inferring the governing stochastic ordinary ...

Non-Parametric Learning of Stochastic Differential Equations with Fast Rates of Convergence

We propose a novel non-parametric learning paradigm for the identificati...

Convergent discretisation schemes for transition path theory for diffusion processes

In the analysis of metastable diffusion processes, Transition Path Theor...

Online Smoothing for Diffusion Processes Observed with Noise

We introduce a methodology for online estimation of smoothing expectatio...