Selfsimilar processes / Paul Embrechts and Makoto Maejima.

By:

Embrechts, Paul, 1953-

Contributor(s):

Maejima, Makoto

Material type: Text

text

Media type:

computer

Carrier type:

online resource

ISBN:

1400814243
9781400814244
9781400825103
1400825105

Subject(s):

Genre/Form:

Additional physical formats: Print version:: Selfsimilar processes.DDC classification:

519.23 21

LOC classification:

QA274.9 .E43 2002eb

Other classification:

SK 820

Online resources:

Click here to access online

Contents:

Contents; Preface; Chapter 1. Introduction; Chapter 2. Some Historical Background; Chapter 3. Selfsimilar Processes with Stationary Increments; Chapter 4. Fractional Brownian Motion; Chapter 5. Selfsimilar Processes with Independent Increments; Chapter 6. Sample Path Properties of Selfsimilar Stable Processes with Stationary Increments; Chapter 7. Simulation of Selfsimilar Processes; Chapter 8. Statistical Estimation; Chapter 9. Extensions; References; Index.

Summary: The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. When measured through linear correlation, many of these systems exhibit a slow correlation decay--a phenomenon often referred to as long-memory or long-range dependence. An example of this is the absolute returns of equity data in finance. Selfsimilar stochastic processes (particularly fractional Brownian motion) have long been postulated as a means to model this behavior, and the concept of selfsimilarity for a stochastic process is now proving to be extraordinarily useful. Selfsimilarity t.

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Includes bibliographical references (pages 101-108) and index.

Print version record.

The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. When measured through linear correlation, many of these systems exhibit a slow correlation decay--a phenomenon often referred to as long-memory or long-range dependence. An example of this is the absolute returns of equity data in finance. Selfsimilar stochastic processes (particularly fractional Brownian motion) have long been postulated as a means to model this behavior, and the concept of selfsimilarity for a stochastic process is now proving to be extraordinarily useful. Selfsimilarity t.

In English.

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