{ "id": "2008.01385", "version": "v1", "published": "2020-08-04T07:49:14.000Z", "updated": "2020-08-04T07:49:14.000Z", "title": "The Multiplicative Chaos of $H=0$ Fractional Brownian Fields", "authors": [ "Paul Hager", "Eyal Neuman" ], "comment": "48 pages, 1 figure", "categories": [ "math.PR", "q-fin.MF" ], "abstract": "We consider a family of fractional Brownian fields $\\{B^{H}\\}_{H\\in (0,1)}$ on $\\mathbb{R}^{d}$, where $H$ denotes their Hurst parameter. We first define a rich class of normalizing kernels $\\psi$ such that the covariance of $$ X^{H}(x) = \\Gamma(H)^{\\frac{1}{2}} \\left( B^{H}(x) - \\int_{\\mathbb{R}^{d}} B^{H}(u) \\psi(u, x)du\\right), $$ converges to the covariance of a log-correlated Gaussian field when $H \\downarrow 0$. We then use Berestycki's ``good points'' approach in order to derive the limiting measure of the so-called multiplicative chaos of the fractional Brownian field $$ M^{H}_\\gamma(dx) = e^{\\gamma X^{H}(x) - \\frac{\\gamma^{2}}{2} E[X^{H}(x)^{2}] }dx, $$ as $H\\downarrow 0$ for all $\\gamma \\in (0,\\gamma^{*}(d)]$, where $\\gamma^{*}(d)>\\sqrt{\\frac{7}{4}d}$. As a corollary we establish the $L^{2}$ convergence of $M^{H}_\\gamma$ over the sets of ``good points'', where the field $X^H$ has a typical behaviour. As a by-product of the convergence result, we prove that for log-normal rough volatility models with small Hurst parameter, the volatility process is supported on the sets of ``good points'' with probability close to $1$. Moreover, on these sets the volatility converges in $L^2$ to the volatility of multifractal random walks.", "revisions": [ { "version": "v1", "updated": "2020-08-04T07:49:14.000Z" } ], "analyses": { "subjects": [ "60G15", "60G57", "60G60", "60G18" ], "keywords": [ "fractional brownian field", "multiplicative chaos", "log-normal rough volatility models", "small hurst parameter", "multifractal random walks" ], "note": { "typesetting": "TeX", "pages": 48, "language": "en", "license": "arXiv", "status": "editable" } } }