Practitioners and researchers who have handled financial market
data know that asset returns do not behave according to the
bell-shaped curve, associated with the Gaussian or normal
distribution. Indeed, the use of Gaussian models when the asset
return distributions are not normal could lead to a wrong choice of
portfolio, the underestimation of extreme losses or mispriced
derivative products. Consequently, non-Gaussian models and models
based on processes with jumps, are gaining popularity among
financial market practitioners.
Non-Gaussian distributions are the key theme of this book which
addresses the causes and consequences of non-normality and time
dependency in both asset returns and option prices. One of the main
aims is to bridge the gap between the theoretical developments and
the practical implementations of what many users and researchers
perceive as "sophisticated" models or black boxes. The book is
written for non-mathematicians who want to model financial market
prices so the emphasis throughout is on practice. There are
abundant empirical illustrations of the models and techniques
described, many of which could be equally applied to other
financial time series, such as exchange and interest rates.
The authors have taken care to make the material accessible to
anyone with a basic knowledge of statistics, calculus and
probability, while at the same time preserving the mathematical
rigor and complexity of the original models.
This book will be an essential reference for practitioners in
the finance industry, especially those responsible for managing
portfolios and monitoring financial risk, but it will also be
useful for mathematicians who want to knowmore about how their
mathematical tools are applied in finance, and as a text for
advanced courses in empirical finance; financial econometrics and
financial derivatives.
General
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