Daniel J. Duffy - Numerical Methods in Computational Finance

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This book is a detailed and step-by-step introduction to the mathematical foundations of ordinary and partial differential equations, their approximation by the finite difference method and applications to computational finance. The book is structured so that it can be read by beginners, novices and expert users.
Part A Mathematical Foundation for One-Factor Problems
Chapters 1 to 7 introduce the mathematical and numerical analysis concepts that are needed to understand the finite difference method and its application to computational finance.
Part B Mathematical Foundation for Two-Factor Problems
Chapters 8 to 13 discuss a number of rigorous mathematical techniques relating to elliptic and parabolic partial differential equations in two space variables. In particular, we develop strategies to preprocess and modify a PDE before we approximate it by the finite difference method, thus avoiding ad-hoc and heuristic tricks.
Part C The Foundations of the Finite Difference Method (FDM)
Chapters 14 to 17 introduce the mathematical background to the finite difference method for initial boundary value problems for parabolic PDEs. It encapsulates all the background information to construct stable and accurate finite difference schemes.
Part D Advanced Finite Difference Schemes for Two-Factor Problems
Chapters 18 to 22 introduce a number of modern finite difference methods to approximate the solution of two factor partial differential equations. This is the only book we know of that discusses these methods in any detail.
Part E Test Cases in Computational Finance
Chapters 23 to 26 are concerned with applications based on previous chapters. We discuss finite difference schemes for a wide range of one-factor and two-factor problems.
This book is suitable as an entry-level introduction as well as a detailed treatment of modern methods as used by industry quants and MSc/MFE students in finance. The topics have applications to numerical analysis, science and engineering.
More on computational finance and the author’s online courses, see www.datasim.nl.

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(3.8) In the cases and and when P and Q are constants thi - фото 401

In the cases картинка 402and and when P and Q are constants this equation is linear and an exact solution - фото 403and when P and Q are constants, this equation is linear, and an exact solution can then be found. We reduce Equation (3.8)to a linear one by the substitution to produce the following linear ODE 39 332 Riccati ODE The Riccati - фото 404to produce the following linear ODE:

(3.9) 332 Riccati ODE The Riccati ODE is a nonlinear ODE of the form 310 - фото 405

3.3.2 Riccati ODE

The Riccati ODE is a non-linear ODE of the form:

(3.10) This ODE has many applications for example to interestrate models Duffie and - фото 406

This ODE has many applications, for example to interest-rate models (Duffie and Kan (1996)). In some cases a closed-form solution to Equation (3.10)is possible, but in this book our focus is on approximating it using the finite difference method.

We now discuss the relationship between the Riccati equation and the pricing of a zero-coupon bond P ( t , T ), which is a contract that offers one dollar at maturity T . By definition, an affine term structure model assumes that P ( t , T ) has the form:

Let us assume that the shortterm interest rate is described by the following - фото 407

Let us assume that the short-term interest rate is described by the following stochastic differential equation (SDE):

where is a standard Brownian motion under the riskneutral equivalent measure - фото 408

where картинка 409is a standard Brownian motion under the risk-neutral equivalent measure and картинка 410and картинка 411are given functions.

Duffie and Kan proved that P ( t , T ) is exponential-affine if and only if the drift and volatility have the form where - фото 412and volatility Numerical Methods in Computational Finance - изображение 413have the form:

Numerical Methods in Computational Finance - изображение 414

where Numerical Methods in Computational Finance - изображение 415and картинка 416are given functions of t .

The coefficients A ( t , T ) and B ( t , T ) in this case are determined by the following ordinary differential equations:

(3.11) and 312 The first Equation 311for B t T is the Riccati equation - фото 417

and:

(3.12) The first Equation 311for B t T is the Riccati equation and the second - фото 418

The first Equation (3.11)for B ( t , T ) is the Riccati equation and the second one (3.12)is solved easily from the first one by integration.

3.3.3 Predator-Prey Models

ODEs can be used as simple models of population growth, for example, by assuming that the rate of reproduction of a population of size P is proportional to the existing population and to the amount of available resources. The ODE is:

where r is the growth rate and K is the carrying capacity The initial - фото 419

where r is the growth rate and K is the carrying capacity . The initial population is It is easy to check the following identities Transformation of this - фото 420. It is easy to check the following identities:

Numerical Methods in Computational Finance - изображение 421

Transformation of this equation leads to the logistic ODE:

(3.13) Numerical Methods in Computational Finance - изображение 422

where n is the population in units of carrying capacity Numerical Methods in Computational Finance - изображение 423and measures time in units of 1 r For systems we can consider the predatorprey - фото 424measures time in units of 1/ r .

For systems, we can consider the predator-prey model in an environment consisting of foxes and rabbits:

(3.14) where number of rabbits at time t - фото 425

where:

картинка 426 = number of rabbits at time t
картинка 427 = number of foxes at time t
Numerical Methods in Computational Finance - изображение 428 = birth rate of rabbits
Numerical Methods in Computational Finance - изображение 429 = death rate of rabbits
картинка 430 = unit birth rate of rabbits
картинка 431 = death rate of foxes
картинка 432 = birth rate of foxes
картинка 433 = unit birth rate of foxes.

The ODE system (3.14)is a model of a closed ecological environment in which foxes and rabbits are the only kinds of animals. Rabbits eat grass (of which there is a constant supply), procreate and are eaten by foxes. All foxes eat rabbits, procreate and die of geriatric diseases.

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