In applications, and especially in mathematical finance, random
time-dependent events are often modeled as stochastic processes.
Assumptions are made about the structure of such processes, and
serious researchers will want to justify those assumptions through
the use of data. As statisticians are wont to say, "In God we
trust; all others must bring data."
This book establishes the theory of how to go about estimating not
just scalar parameters about a proposed model, but also the
underlying structure of the model itself. Classic statistical tools
are used: the law of large numbers, and the central limit
theorem.Researchers have recently developed creative and original
methods to use these tools in sophisticated (but highly technical)
ways to reveal new details about the underlying structure. For the
first time in book form, the authors present these latest
techniques, based on research from the last 10 years. They include
new findings.
This book will be of special interest to researchers, combining the
theory of mathematical finance with its investigation using market
data, and it will also prove to be useful in a broad range of
applications, such as to mathematical biology, chemical
engineering, and physics. "
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