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In the paper written by Klibanov et al, it proposes a novel method to calculate implied volatility of a European stock options as a solution to ill-posed inverse problem for the Black-Scholes equation. In addition, it proposes a trading…

Numerical Analysis · Mathematics 2025-01-29 Wanchaloem Wunkaew , Yuqing Liu , Kirill V. Golubnichiy

In financial mathematics, it is a typical approach to approximate financial markets operating in discrete time by continuous-time models such as the Black Scholes model. Fitting this model gives rise to difficulties due to the discrete…

Mathematical Finance · Quantitative Finance 2024-01-11 Kathrin Hellmuth , Christian Klingenberg

We consider the problem of calculating risk-neutral implied volatilities of European options without relying on option mid prices but solely on bid and ask prices. We provide an approach, based on the conic finance paradigm, that allows to…

Mathematical Finance · Quantitative Finance 2021-10-25 Matteo Michielon , Asma Khedher , Peter Spreij

We present a theory of option pricing and hedging, designed to address non-perfect arbitrage, market friction and the presence of `fat' tails. An implied volatility `smile' is predicted. We give precise estimates of the residual risk…

Condensed Matter · Physics 2016-08-31 Jean-Philippe Bouchaud , Giulia Iori , Didier Sornette

Implied volatility IV is a key metric in financial markets, reflecting market expectations of future price fluctuations. Research has explored IV's relationship with moneyness, focusing on its connection to the implied Hurst exponent H. Our…

Computational Finance · Quantitative Finance 2025-02-12 Daniele Angelini , Fabrizio Di Sciorio

We study shortfall risk minimization for American options with path dependent payoffs under proportional transaction costs in the Black--Scholes (BS) model. We show that for this case the shortfall risk is a limit of similar terms in an…

Computational Finance · Quantitative Finance 2010-04-12 Yan Dolinsky

Surface roughness becomes relevant if typical length scales of the system are comparable to the scale of the variations as it is the case in microfluidic setups. Here, an apparent boundary slip is often detected which can have its origin in…

Soft Condensed Matter · Physics 2008-06-24 Christian Kunert , Jens Harting

In this paper we derive an effective equation for derivative pricing which accounts for the presence of virtual arbitrage opportunities and their elimination by the market. We model the arbitrage return by a stochastic process and find an…

Statistical Mechanics · Physics 2008-12-02 Kirill Ilinski , Alexander Stepanenko

Minimizing the Mean Squared Error (MSE) is a key objective in machine learning and is commonly used for imputing missing values. While this approach provides accurate point estimates, it introduces systematic biases in downstream analyses.…

Machine Learning · Statistics 2026-05-06 Stef van Buuren

In the Black-Scholes context we consider the probability distribution function (PDF) of financial returns implied by volatility smile and we study the relation between the decay of its tails and the fitting parameters of the smile. We show…

Pricing of Securities · Quantitative Finance 2010-10-12 L. Spadafora , G. P. Berman , F. Borgonovi

We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories. When $K$ is indeed infinite, or finite but very large, the relevant summation is intractable. Accordingly,…

Machine Learning · Statistics 2019-05-14 Runjing Liu , Jeffrey Regier , Nilesh Tripuraneni , Michael I. Jordan , Jon McAuliffe

The fate of scientific hypotheses often relies on the ability of a computational model to explain the data, quantified in modern statistical approaches by the likelihood function. The log-likelihood is the key element for parameter…

Machine Learning · Computer Science 2021-01-27 Bas van Opheusden , Luigi Acerbi , Wei Ji Ma

Since the introduction of the Black-Scholes model stochastic processes have played an increasingly important role in mathematical finance. In many cases prices, volatility and other quantities can be modeled using stochastic ordinary…

Data Analysis, Statistics and Probability · Physics 2007-05-23 Yin Mei Wong , Joshua Wilkie

We introduce a new probabilistic method for solving a class of impulse control problems based on their representations as Backward Stochastic Differential Equations (BSDEs for short) with constrained jumps. As an example, our method is used…

Computational Finance · Quantitative Finance 2015-03-17 Marie Bernhart , Huyên Pham , Peter Tankov , Xavier Warin

Options are financial instruments that depend on the underlying stock. We explain their non-Gaussian fluctuations using the nonextensive thermodynamics parameter $q$. A generalized form of the Black-Scholes (B-S) partial differential…

Statistical Mechanics · Physics 2009-11-07 Lisa Borland

We consider a model of linear market impact, and address the problem of replicating a contingent claim in this framework. We derive a non-linear Black-Scholes Equation that provides an exact replication strategy. This equation is fully…

Pricing of Securities · Quantitative Finance 2016-08-15 Gregoire Loeper

In this report it is shown that the implicit Euler time-discretization of some classes of switching systems with sliding modes, yields a very good stabilization of the trajectory and of its derivative on the sliding surface. Therefore the…

Numerical Analysis · Mathematics 2009-04-13 Vincent Acary , Bernard Brogliato

We consider the problem of pricing basket options in a multivariate Black Scholes or Variance Gamma model. From a numerical point of view, pricing such options corresponds to moderate and high dimensional numerical integration problems with…

Computational Finance · Quantitative Finance 2017-02-27 Christian Bayer , Markus Siebenmorgen , Raul Tempone

We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples. We show that our estimator can be derived as the…

Machine Learning · Computer Science 2020-02-17 Wouter Kool , Herke van Hoof , Max Welling

We analyze algorithms for solving stochastic variational inequalities (VI) without the bounded variance or bounded domain assumptions, where our main focus is min-max optimization with possibly unbounded constraint sets. We focus on two…

Optimization and Control · Mathematics 2026-02-06 Ahmet Alacaoglu , Jun-Hyun Kim
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