Selfsimilar processes / Paul Embrechts and Makoto Maejima.
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...
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Full Text (via ProQuest) |
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| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Princeton, N.J. :
Princeton University Press,
©2002.
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| Series: | Princeton series in applied mathematics.
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| Subjects: |
| 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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| Physical Description: | 1 online resource (x, 111 pages) : illustrations. |
| Bibliography: | Includes bibliographical references (pages 101-108) and index. |
| ISBN: | 1400814243 9781400814244 9781400825103 1400825105 |
| Language: | In English. |
| Source of Description, Etc. Note: | Print version record. |