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The study of heavy-tailed distributions allows researchers to
represent phenomena that occasionally exhibit very large deviations
from the mean. The dynamics underlying these phenomena is an
interesting theoretical subject, but the study of their statistical
properties is in itself a very useful endeavor from the point of
view of managing assets and controlling risk. In this book, the
authors are primarily concerned with the statistical properties of
heavy-tailed distributions and with the processes that exhibit
jumps. A detailed overview with a Matlab implementation of
heavy-tailed models applied in asset management and risk
managements is presented. The book is not intended as a theoretical
treatise on probability or statistics, but as a tool to understand
the main concepts regarding heavy-tailed random variables and
processes as applied to real-world applications in finance.
Accordingly, the authors review approaches and methodologies whose
realization will be useful for developing new methods for
forecasting of financial variables where extreme events are not
treated as anomalies, but as intrinsic parts of the economic
process.
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