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Related papers: Option Pricing in Illiquid Markets with Jumps

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The shortcomings of the popular Black-Scholes-Merton (BSM) model have led to models which could more accurately model the behavior of the underlying assets in energy markets, particularly in electricity and future oil prices. In this paper…

Pricing of Securities · Quantitative Finance 2020-06-01 Konrad Gajewski , Sebastian Ferrando , Pablo Olivares

Pricing financial derivatives, in particular European-style options at different time-maturities and strikes, means a relevant problem in finance. The dynamics describing the price of vanilla options when constant volatilities and interest…

Quantum Physics · Physics 2024-01-22 Javier Gonzalez-Conde , Ángel Rodríguez-Rozas , Enrique Solano , Mikel Sanz

The LIBOR Market Model (LMM) is a widely used model for pricing interest rate derivatives. While the Black-Scholes model is well-known for pricing stock derivatives such as stock options, a larger portion of derivatives are based on…

Quantum Physics · Physics 2022-07-05 Hao Tang , Wenxun Wu , Xian-Min Jin

This paper examines the problem of pricing spread options under some models with jumps driven by Compound Poisson Processes and stochastic volatilities in the form of Cox-Ingersoll-Ross(CIR) processes. We derive the characteristic function…

Pricing of Securities · Quantitative Finance 2014-09-04 Pablo Olivares , Matthew Cane

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

An option market maker incurs funding costs when carrying and hedging inventory. To hedge a net long delta inventory, for example, she pays a fee to borrow stock from the securities lending market. Because of haircuts, she posts additional…

Pricing of Securities · Quantitative Finance 2020-05-05 Wujiang Lou

We model the price of a stock via a Lang\'{e}vin equation with multi-dimensional fluctuations coupled in the price and in time. We generalize previous models in that we assume that the fluctuations conditioned on the time step are compound…

Mathematical Physics · Physics 2008-12-10 Przemyslaw Repetowicz , Peter Richmond

In recent studies the truncated Levy process (TLP) has been shown to be very promising for the modeling of financial dynamics. In contrast to the Levy process, the TLP has finite moments and can account for both the previously observed…

Statistical Mechanics · Physics 2008-12-10 Andrew Matacz

Deep hedging is a framework for hedging derivatives in the presence of market frictions. In this study, we focus on the problem of hedging a given target option by using multiple options. To extend the deep hedging framework to this…

Computational Finance · Quantitative Finance 2023-05-23 Masanori Hirano , Kentaro Imajo , Kentaro Minami , Takuya Shimada

When the underlying asset displays oscillations, spikes or heavy-tailed distributions, the lognormal diffusion process (for which Black and Scholes developed their momentous option pricing formula) is inadequate: in order to overcome these…

Computational Finance · Quantitative Finance 2017-12-22 Marcellino Gaudenzi , Alice Spangaro , Patrizia Stucchi

An efficient computational algorithm to price financial derivatives is presented. It is based on a path integral formulation of the pricing problem. It is shown how the path integral approach can be worked out in order to obtain fast and…

Statistical Mechanics · Physics 2009-11-07 G. Montagna , O. Nicrosini , N. Moreni

A unified analytical pricing framework with involvement of the shot noise random process has been introduced and elaborated. Two exactly solvable new models have been developed. The first model has been designed to value options. It is…

Pricing of Securities · Quantitative Finance 2014-10-15 Nick Laskin

In this paper we propose a novel pricing-hedging framework for volatility derivatives which simultaneously takes into account rough volatility and volatility jumps. Our model directly targets the instantaneous variance of a risky asset and…

Pricing of Securities · Quantitative Finance 2021-11-30 Liang Wang , Weixuan Xia

It is a well known fact that local scale invariance plays a fundamental role in the theory of derivative pricing. Specific applications of this principle have been used quite often under the name of `change of numeraire', but in recent work…

Condensed Matter · Physics 2007-05-23 Jiri Hoogland , Dimitri Neumann , Michel Vellekoop

A bubble is characterized by the presence of an underlying asset whose discounted price process is a strict local martingale under the pricing measure. In such markets, many standard results from option pricing theory do not hold, and in…

Probability · Mathematics 2009-09-01 Erik Ekström , Johan Tysk

We consider a stochastic volatility model with jumps where the underlying asset price is driven by the process sum of a 2-dimensional Brownian motion and a 2-dimensional compensated Poisson process. The market is incomplete, resulting in…

Probability · Mathematics 2011-10-31 Youssef El-Khatib

We consider a financial market in which two securities are traded: a stock and an index. Their prices are assumed to satisfy the Black-Scholes model. Besides assuming that the index is a tradable security, we also assume that it is…

Portfolio Management · Quantitative Finance 2011-09-26 Vladimir Vovk

Pricing a multi-asset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differential equations to calculate the prices of certain types of…

Quantum Physics · Physics 2022-07-05 Kenji Kubo , Koichi Miyamoto , Kosuke Mitarai , Keisuke Fujii

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

We propose the deep parametric PDE method to solve high-dimensional parametric partial differential equations. A single neural network approximates the solution of a whole family of PDEs after being trained without the need of sample…

Computational Finance · Quantitative Finance 2020-12-14 Kathrin Glau , Linus Wunderlich