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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.
List of contents
Introduction
Preliminaries
Portfolio Theory for Dependent Return Processes
Multiperiod Problem for Portfolio Theory
Portfolio Estimation based on Rank Statistics
Portfolio Estimation Influence by Non-Gaussian Innovatin and Exogenous Variables
Numerical Examples
Theoretical Foundations and Technicalities
About the author
Masanobu Taniguchi is a research professor in the Department of Applied Mathematics at Waseda University, Japan.
Hiroshi Shiraishi is a lecturer in the Laboratory of Mathematics, Jikei University School of Medicine, Japan.
Junichi Hirukawa is an associate professor in the Faculty of Science at Niigata University, Japan.
Hiroko Solvang Kato is a researcher and project leader in the Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Norway.
Summary
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, nonstationary processes, and the book provides a framework for statistical inference using local asymptotic normality. <
