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Pricing Financial Instruments: The Finite Difference Method

TLDR
The Pricing Equations. as mentioned in this paper and the Finite-difference method are the most commonly used methods for finite difference methods in the literature, and they can be found in:
Abstract
The Pricing Equations. Analysis of Finite Difference Methods. Special Issues. Coordinate Transformations. Numerical Examples. Index.

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Citations
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Journal ArticleDOI

Unbiased and efficient Greeks of financial options

TL;DR: A new but simple mathematical formulation is built so that formulas of Greeks for a broad class of derivative securities can be derived systematically and these formulas are the first in the literature.

Some Numerical Methods for Options Valuation

C. R. Nwozo
TL;DR: In this article, three numerical methods for options valuation namely binomial model, Finite difference methods and Monte Carlo simulation method are discussed and compared to the analytic Black-Scholes price of the options.
Proceedings ArticleDOI

Flexible design of commercial systems under market uncertainty: framework and application

TL;DR: In this paper, a procedural framework for the design optimization of modular commercial systems that are required to respond to changing market conditions is presented, where standard options valuation theory is used to model and valuate the flexibility embedded in a design solution and external optimization techniques are used to determine the design of greatest value.
Dissertation

Numerical methods for nonlinear equations in option pricing

TL;DR: In this paper, numerical methods for solving nonlinear partial differential equations (PDEs) that arise in option pricing problems are explored, and conditions under which the one factor uncertain volatility equations are guaranteed to converge to the viscosity solution are derived.
Posted Content

Fast Solutions of Complementarity Formulations in American Put Pricing

TL;DR: This paper shows that the finite-difference value function of an American put option can be computed by solving a sequence of linear complementarity problems (LCPs) in a computer time which grows linearly with the number of spatial grid points.