Statistical Portfolio Estimation

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Statistical Portfolio Estimation by Masanobu Taniguchi, Hiroshi Shiraishi, Junichi Hirukawa, Hiroko Kato Solvang, Takashi Yamashita, CRC Press
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Author: Masanobu Taniguchi, Hiroshi Shiraishi, Junichi Hirukawa, Hiroko Kato Solvang, Takashi Yamashita ISBN: 9781351643627
Publisher: CRC Press Publication: September 1, 2017
Imprint: Chapman and Hall/CRC Language: English
Author: Masanobu Taniguchi, Hiroshi Shiraishi, Junichi Hirukawa, Hiroko Kato Solvang, Takashi Yamashita
ISBN: 9781351643627
Publisher: CRC Press
Publication: September 1, 2017
Imprint: Chapman and Hall/CRC
Language: English

The composition of portfolios is one of the most fundamental and important methods in financial engineering, used to control the risk of investments. This book provides a comprehensive overview of statistical inference for portfolios and their various applications. A variety of asset processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many important problems can be covered.

This book can primarily be used as a reference by researchers from statistics, mathematics, finance, econometrics, and genomics. It can also be used as a textbook by senior undergraduate and graduate students in these fields.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

The composition of portfolios is one of the most fundamental and important methods in financial engineering, used to control the risk of investments. This book provides a comprehensive overview of statistical inference for portfolios and their various applications. A variety of asset processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many important problems can be covered.

This book can primarily be used as a reference by researchers from statistics, mathematics, finance, econometrics, and genomics. It can also be used as a textbook by senior undergraduate and graduate students in these fields.

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