Book description
Financial Modelling - Theory, Implementation and Practice is a
unique combination of quantitative techniques, the application to
financial problems and programming using Matlab. The book enables the
reader to model, design and implement a wide range of financial models
for derivatives pricing and asset allocation, providing practitioners
with complete financial modelling workflow, from model choice,
deriving prices and Greeks using (semi-) analytic and simulation
techniques, and calibration even for exotic options.
The book is split into three parts. The first part considers
financial markets in general and looks at the complex models needed to
handle observed structures, reviewing models based on diffusions
including stochastic-local volatility models and (pure) jump
processes. It shows the possible risk neutral densities, implied
volatility surfaces, option pricing and typical paths for a variety of
models including SABR, Heston, Bates, Bates-Hull-White,
Displaced-Heston, or stochastic volatility versions of Variance Gamma,
respectively Normal Inverse Gaussian models and finally,
multi-dimensional models. The stochastic-local-volatility Libor market
model with time-dependent parameters is considered and as an
application how to price and risk-manage CMS spread products is demonstrated.
The second part of the book deals with numerical methods which
enables the reader to use the models of the first part for pricing and
risk management, covering methods based on direct integration and
Fourier transforms, and detailing the implementation of the COS, CONV,
Carr-Madan method or Fourier-Space-Time Stepping. This is applied to
pricing of European, Bermudan and exotic options as well as the
calculation of the Greeks. The Monte Carlo simulation technique is
outlined and bridge sampling is discussed in a Gaussian setting and
for Lévy processes. Computation of Greeks is covered using likelihood
ratio methods and adjoint techniques. A chapter on state-of-the-art
optimization algorithms rounds up the toolkit for applying advanced
mathematical models to financial problems and the last chapter in this
section of the book also serves as an introduction to model risk.
The third part is devoted to the usage of Matlab, introducing the
software package by describing the basic functions applied for
financial engineering. The programming is approached from an
object-oriented perspective with examples to propose a framework for
calibration, hedging and the adjoint method for calculating Greeks in
a Libor Market model.
Source code used for producing the results and analysing the models
is provided on the author's dedicated website, http://www. mathworks. de/matlabcentral/fileexchange/authors/246981
Jörg Kienitz is head of Quantitative Analytics at Deutsche
Postbank AG. He is primarily involved in developing and implementing
models for pricing complex derivatives structures and for asset
allocation. He also lectures at university level on advanced financial
modelling and implementation including the University of Oxford's
part-time Masters of Finance course. Jörg works as an independent
consultant for model development and validation as well as giving
seminars for finance professionals. He is a speaker at the major
financial conferences including Global Derivatives, WBS Fixed Income
or RISK. Jörg is the member of the editorial board of International
Review of Applied Financial Issues and Economics and holds a Ph. D. in
stochastic analysis from the University of Bielefeld.
Daniel Wetterau is senior specialist in the Quantitative
Analytics team of Deutsche Postbank AG. He is responsible for the
implementation of term structure models, advanced numerical methods,
optimization algorithms and methods for advanced quantitative asset
allocation. Further to his work he teaches finance courses for market
professionals. Daniel received a Masters in financial mathematics from
the University of Wuppertal and was awarded the Barmenia mathematics
award for his thesis.
