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This book provides a generalised approach to fractal dimension
theory from the standpoint of asymmetric topology by employing the
concept of a fractal structure. The fractal dimension is the main
invariant of a fractal set, and provides useful information
regarding the irregularities it presents when examined at a
suitable level of detail. New theoretical models for calculating
the fractal dimension of any subset with respect to a fractal
structure are posed to generalise both the Hausdorff and
box-counting dimensions. Some specific results for self-similar
sets are also proved. Unlike classical fractal dimensions, these
new models can be used with empirical applications of fractal
dimension including non-Euclidean contexts. In addition, the book
applies these fractal dimensions to explore long-memory in
financial markets. In particular, novel results linking both
fractal dimension and the Hurst exponent are provided. As such, the
book provides a number of algorithms for properly calculating the
self-similarity exponent of a wide range of processes, including
(fractional) Brownian motion and Levy stable processes. The
algorithms also make it possible to analyse long-memory in real
stocks and international indexes. This book is addressed to those
researchers interested in fractal geometry, self-similarity
patterns, and computational applications involving fractal
dimension and Hurst exponent.
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