The theory and applications of random dynamical systems (RDS)
are at the cutting edge of research in mathematics and economics,
particularly in modeling the long-run evolution of economic systems
subject to exogenous random shocks. Despite this interest, there
are no books available that solely focus on RDS in finance and
economics. Exploring this emerging area, Random Dynamical Systems
in Finance shows how to model RDS in financial applications.
Through numerous examples, the book explains how the theory of
RDS can describe the asymptotic and qualitative behavior of systems
of random and stochastic differential/difference equations in terms
of stability, invariant manifolds, and attractors. The authors
present many models of RDS and develop techniques for implementing
RDS as approximations to financial models and option pricing
formulas. For example, they approximate geometric Markov renewal
processes in ergodic, merged, double-averaged, diffusion, normal
deviation, and Poisson cases and apply the obtained results to
option pricing formulas.
With references at the end of each chapter, this book provides a
variety of RDS for approximating financial models, presents
numerous option pricing formulas for these models, and studies the
stability and optimal control of RDS. The book is useful for
researchers, academics, and graduate students in RDS and
mathematical finance as well as practitioners working in the
financial industry.
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