Stochastic Calculus For Fractional Brownian Motion And Related Processes


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Stochastic Calculus for Fractional Brownian Motion and Related Processes


Stochastic Calculus for Fractional Brownian Motion and Related Processes

Author: I︠U︡lii︠a︡ S. Mishura

language: en

Publisher: Springer Science & Business Media

Release Date: 2008-01-02


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This volume examines the theory of fractional Brownian motion and other long-memory processes. Interesting topics for PhD students and specialists in probability theory, stochastic analysis and financial mathematics demonstrate the modern level of this field. It proves that the market with stock guided by the mixed model is arbitrage-free without any restriction on the dependence of the components and deduces different forms of the Black-Scholes equation for fractional market.

Stochastic Calculus for Fractional Brownian Motion and Related Processes


Stochastic Calculus for Fractional Brownian Motion and Related Processes

Author: Yuliya S. Mishura

language: en

Publisher:

Release Date: 2008


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The theory of fractional Brownian motion and other long-memory processes are addressed in this volume. Interesting topics for PhD students and specialists in probability theory, stochastic analysis and financial mathematics demonstrate the modern level of this field. Among these are results about Levy characterization of fractional Brownian motion, maximal moment inequalities for Wiener integrals including the values 0

Stochastic Calculus for Fractional Brownian Motion and Applications


Stochastic Calculus for Fractional Brownian Motion and Applications

Author: Francesca Biagini

language: en

Publisher: Springer Science & Business Media

Release Date: 2008-02-17


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Fractional Brownian motion (fBm) has been widely used to model a number of phenomena in diverse fields from biology to finance. This huge range of potential applications makes fBm an interesting object of study. Several approaches have been used to develop the concept of stochastic calculus for fBm. The purpose of this book is to present a comprehensive account of the different definitions of stochastic integration for fBm, and to give applications of the resulting theory. Particular emphasis is placed on studying the relations between the different approaches. Readers are assumed to be familiar with probability theory and stochastic analysis, although the mathematical techniques used in the book are thoroughly exposed and some of the necessary prerequisites, such as classical white noise theory and fractional calculus, are recalled in the appendices. This book will be a valuable reference for graduate students and researchers in mathematics, biology, meteorology, physics, engineering and finance.