Fractional Gaussian fields: a survey
Asad Lodhia, Scott Sheffield, Xin Sun, Samuel S. Watson

TL;DR
This survey explores fractional Gaussian fields, a family of random fields characterized by a parameter, highlighting their properties, examples, applications, and connections to various stochastic processes and physical models.
Contribution
It provides a comprehensive overview of fractional Gaussian fields, including formulas, properties, and new interpretations, serving as a foundational reference for further research.
Findings
Includes covariance formulas and Gibbs properties.
Defines a discrete fractional Gaussian field.
Connects FGF_s with stable Lévy processes.
Abstract
We discuss a family of random fields indexed by a parameter which we call the fractional Gaussian fields, given by \[ \mathrm{FGF}_s(\mathbb{R}^d)=(-\Delta)^{-s/2} W, \] where is a white noise on and is the fractional Laplacian. These fields can also be parameterized by their Hurst parameter . In one dimension, examples of processes include Brownian motion () and fractional Brownian motion (). Examples in arbitrary dimension include white noise (), the Gaussian free field (), the bi-Laplacian Gaussian field (), the log-correlated Gaussian field (), L\'evy's Brownian motion (), and multidimensional fractional Brownian motion (). These fields have applications to statistical physics, early-universe cosmology,…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Stochastic processes and financial applications · Financial Risk and Volatility Modeling
