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Related papers: A note on essential smoothness in the Heston model

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The quadratic rough Heston model provides a natural way to encode Zumbach effect in the rough volatility paradigm. We apply multi-factor approximation and use deep learning methods to build an efficient calibration procedure for this model.…

Computational Finance · Quantitative Finance 2022-05-31 Mathieu Rosenbaum , Jianfei Zhang

Financial time series exhibit a number of interesting properties that are difficult to explain with simple models. These properties include fat-tails in the distribution of price fluctuations (or returns) that are slowly removed at longer…

Statistical Finance · Quantitative Finance 2013-11-19 Raoul Golan , Austin Gerig

We present a stochastic-local volatility model for derivative contracts on commodity futures able to describe forward-curve and smile dynamics with a fast calibration to liquid market quotes. A parsimonious parametrization is introduced to…

Pricing of Securities · Quantitative Finance 2020-01-27 Emanuele Nastasi , Andrea Pallavicini , Giulio Sartorelli

In this paper we consider the multispecies stirring process on the discrete torus. We prove a large deviation principle for the trajectory of the vector of densities of the different species. The technique of proof consists in extending the…

Probability · Mathematics 2024-10-29 Francesco Casini , Frank Redig , Hidde van Wiechen

We investigate the links between various no-arbitrage conditions and the existence of pricing functionals in general markets, and prove the Fundamental Theorem of Asset Pricing therein. No-arbitrage conditions, either in this abstract…

Mathematical Finance · Quantitative Finance 2021-05-25 Sergey Badikov , Mark H. A. Davis , Antoine Jacquier

In the last several years, the intimate connection between convex optimization and learning problems, in both statistical and sequential frameworks, has shifted the focus of algorithmic machine learning to examine this interplay. In…

Machine Learning · Computer Science 2014-07-23 Mehrdad Mahdavi

Statistical inference on the explained variation of an outcome by a set of covariates is of particular interest in practice. When the covariates are of moderate to high-dimension and the effects are not sparse, several approaches have been…

Methodology · Statistics 2022-01-24 Hua Yun Chen

In this paper, we use the framework of mod-$\phi$ convergence to prove precise large or moderate deviations for quite general sequences of real valued random variables $(X_{n})_{n \in \mathbb{N}}$, which can be lattice or non-lattice…

Probability · Mathematics 2017-02-14 Valentin Féray , Pierre-Loïc Méliot , Ashkan Nikeghbali

Accurately characterizing the implied volatility curves is a central challenge in option pricing and risk management. The classical SABR model by Hagan et al. has been widely adopted in practice due to its well-defined stochastic volatility…

Mathematical Finance · Quantitative Finance 2026-03-31 Wenxuan Zhang , Zhouchi Lin , Benzhuo Lu

This research addresses accurate option pricing by employing models beyond the traditional Black-Scholes framework. While Black-Scholes provides a closed-form solution, it is limited by assumptions of constant volatility, no dividends, and…

Computational Finance · Quantitative Finance 2026-04-08 Karmanpartap Singh Sidhu , Pranshi Saxena

Usually, in the Black-Scholes pricing theory the volatility is a positive real parameter. Here we explore what happens if it is allowed to be a complex number. The function for pricing a European option with a complex volatility has…

Mathematical Finance · Quantitative Finance 2016-12-07 Yiran Cui , Sebastian del Bano Rollin , Guido Germano

This note is devoted to a rigorous derivation of rigid-plasticity as the limit of elasto-plasticity when the elasticity tends to infinity.

Analysis of PDEs · Mathematics 2015-10-14 Jean-François Babadjian , Gilles A. Francfort

This study develops an integrated stochastic modeling framework for pricing short and medium-maturity equity options and assessing interest-rate risk using the Heston (1993), Bates (1996), and CIR (1985) models. We calibrate the Heston…

Portfolio Management · Quantitative Finance 2026-05-28 Nunik Srikandi Putri , Ajay Kumar Verma , Neo Paul Lesupi

In this paper, we study the statistical properties of the moneyness scaling transformation by Leung and Sircar (2015). This transformation adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the…

Statistical Finance · Quantitative Finance 2020-09-22 Sergey Nasekin , Wolfgang Karl Härdle

We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a nonnegative function of a Volterra type continuous Gaussian process that may have very rough sample paths. The main results obtained in…

Probability · Mathematics 2021-01-01 Archil Gulisashvili

Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance portfolio concerning different lines of business. One of the…

Probability · Mathematics 2021-05-12 Miriam Hägele , Jaakko Lehtomaa

Intermittency is one of central obstacles for understanding small-scale dynamics in the fully developed hydrodynamic turbulence. The modern approach is largely based on the multifractal theory of Parisi and Frisch which is, however,…

Fluid Dynamics · Physics 2023-05-12 Alexei A. Mailybaev

Nonsmooth formulations of physical models are common, particularly in climate modeling. However, in many of these models, there is little justification for this modeling choice, and no mathematical indication that the resulting behavior in…

Dynamical Systems · Mathematics 2016-02-01 Julie Leifeld

We address the information content of European option prices about volatility in terms of the Fisher information matrix. We assume that observed option prices are centred on the theoretical price provided by Heston's model disturbed by…

Statistical Finance · Quantitative Finance 2016-10-19 Oliver Pfante , Nils Bertschinger

We give a decomposition of the posterior predictive variance using the law of total variance and conditioning on a finite dimensional discrete random variable. This random variable summarizes various features of modeling that are used to…

Methodology · Statistics 2022-09-02 Dean Dustin , Bertrand Clarke