arXiv research

Finite-time extinction and smoothing effects in fractional fast diffusion on manifolds.

We introduce a fractional Yamabe flow involving nonlocal conformally invariant operators on the conformal infinity of asymptotically hyperbolic manifolds, and show that on the conformal spheres $(\Sn, [g_{\Sn}])$, it converges to the standard sphere up to a Möbius diffeomorphism. This result allows us to obtain extinct…

Generative Fractional Diffusion Models improve image diversity and quality.

FDBM models use fractional Brownian motion to model complex stochastic processes.

Enhances option pricing with fractional order Black-Scholes-Merton model.

In this paper, we focus on option pricing models based on space-time fractional diffusion. We briefly revise recent results which show that the option price can be represented in the terms of rapidly converging double-series and apply these results to the data from real markets. We focus on estimation of model paramete…

In this paper, we show that the price of an European call option, whose underlying asset price is driven by the space-time fractional diffusion, can be expressed in terms of rapidly convergent double-series. The series formula can be obtained from the Mellin-Barnes representation of the option price with help of residu…

Data-driven discovery of "hidden physics" -- i.e., machine learning of differential equation models underlying observed data -- has recently been approached by embedding the discovery problem into a Gaussian Process regression of spatial data, treating and discovering unknown equation parameters as hyperparameters of a…

Formula for option pricing in a stochastic volatility model with jumps.

FSD-CAP improves graph feature imputation under high missing rates.

Study finds conditions for global minimizers on curved manifolds with fast diffusion and nonlocal interactions.

We suggest that the broad distribution of time scales in financial markets could be a crucial ingredient to reproduce realistic price dynamics in stylised Agent-Based Models. We propose a fractional reaction-diffusion model for the dynamics of latent liquidity in financial markets, where agents are very heterogeneous i…

Fractional porous media equations yield q-Gaussian solutions for stock price returns.

A new method speeds up sampling in diffusion models.

Fractional stochastic volatility models have been widely used to capture the non-Markovian structure revealed from financial time series of realized volatility. On the other hand, empirical studies have identified scales in stock price volatility: both fast-time scale on the order of days and slow-scale on the order of…

Efficiently handles contextual bandits with diffusion models.

We study markets with no riskless (safe) asset. We derive the corresponding Black-Scholes-Merton option pricing equations for markets where there are only risky assets which have the following price dynamics: (i) continuous diffusions; (ii) jump-diffusions; (iii) diffusions with stochastic volatilities, and; (iv) geome…

The mixed-fractional CEV model improves CDS pricing by accounting for default risk.

We show how the prices of options can be determined with the help of double-fractional differential equation in such a way that their inclusion in a portfolio of stocks provides a more reliable hedge against dramatic price drops that the use of options whose prices were fixed by the Black-Scholes formula.

Improved diffusion sampling for inverse problems with faster and more robust inference.

Analyzed a generalized voter model with power-law herding intensity, revealing anomalous diffusion and long-range memory.

SiD distills pretrained diffusion models into a fast one-step generator.

SA-Solver improves stochastic sampling from DPMs.

Researchers study fractional porous medium equation on hyperbolic space.

Improved diffusion model generation speed with speculative sampling.

Smooth convergence shown for curve diffusion flows.

We consider gradient estimates to positive solutions of porous medium equations and fast diffusion equations: $u_t=Δ_φ(u^p)$ associated with the Witten Laplacian on Riemannian manifolds. Under the assumption that the $m$-dimensional Bakry-Emery Ricci curvature is bounded from below, we obtain gradient estimates which…

New method for fast inference in diffusion models.

We solve a family of fractional Riccati differential equations with constant (possibly complex) coefficients. These equations arise, e.g., in fractional Heston stochastic volatility models, that have received great attention in the recent financial literature thanks to their ability to reproduce a rough volatility beha…

The paper derives gradient estimates for porous medium and fast diffusion equations on metric measure spaces.

Study approximates rough stochastic volatility models using diffusion processes.

A fast method approximates likelihood scores for noisy linear inverse problems.

This overview article concerns the notion of fractional smoothness of random variables of the form $g(X_T)$, where $X=(X_t)_{t\in [0,T]}$ is a certain diffusion process. We review the connection to the real interpolation theory, give examples and applications of this concept. The applications in stochastic finance main…

ELM combines machine learning and feature engineering for anomalous diffusion detection.

The fractional Poisson process (FPP) is a counting process with independent and identically distributed inter-event times following the Mittag-Leffler distribution. This process is very useful in several fields of applied and theoretical physics including models for anomalous diffusion. Contrary to the well-known Poiss…

Algorithm minimizes risk for multiclass classification of stochastic diffusion paths.

In this work we derive local gradient and Laplacian estimates of the Aronson-Bénilan and Li-Yau type for positive solutions of porous medium equations posed on Riemannian manifolds with a lower Ricci curvature bound. We also prove similar results for some fast diffusion equations. Inspired by Perelman's work we discove…

Predicts solar dynamics with diffusion models, improving long-range dependencies.

In this paper we apply Markovian approximation of the fractional Brownian motion (BM), known as the Dobric-Ojeda (DO) process, to the fractional stochastic volatility model where the instantaneous variance is modelled by a lognormal process with drift and fractional diffusion. Since the DO process is a semi-martingale,…

Local graph clustering improves with noisy labels, enhancing accuracy and performance.

Study finds roughness in volatility despite diffusive instantaneous volatility.

A fast voice conversion method using diffusion models.

We show by explicit closed form calculations that a Hurst exponent H that is not 1/2 does not necessarily imply long time correlations like those found in fractional Brownian motion. We construct a large set of scaling solutions of Fokker-Planck partial differential equations where H is not 1/2. Thus Markov processes, …

In this paper we present a rather general phenomenological theory of tick-by-tick dynamics in financial markets. Many well-known aspects, such as the Lévy scaling form, follow as particular cases of the theory. The theory fully takes into account the non-Markovian and non-local character of financial time series. Predi…

WSD uses a deterministic model to accelerate diffusion-based sampling.

A new method for fast graph embedding using diffusion graphs.

PRISMA uses PDE residuals for fast, robust, and accurate inference.

Diffusion maps are an emerging data-driven technique for non-linear dimensionality reduction, which are especially useful for the analysis of coherent structures and nonlinear embeddings of dynamical systems. However, the computational complexity of the diffusion maps algorithm scales with the number of observations. T…