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Indexation and Causation of Financial Markets

  • Book
  • © 2015

Overview

Authors:
  1. Yoko Tanokura

    1. Graduate School of Advanced Mathematical Sciences, Meiji University, Nakano-ku, Japan

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  2. Genshiro Kitagawa

    1. Research Organization of Information and Systems, Minato-ku, Japan

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    You can also search for this author in PubMed   Google Scholar

  • Provides a method of analysis for nonstationary non-Gaussian multivariate time series
  • Develops a means of constructing an index for financial time series
  • Explains a practical statistical technique for global investment management
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)

Part of the book sub series: JSS Research Series in Statistics (JSSRES)

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Table of contents (4 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Yoko Tanokura, Genshiro Kitagawa
    Pages 1-11

  3. Method for Constructing a Distribution-Free Index

    • Yoko Tanokura, Genshiro Kitagawa
    Pages 13-34

  4. Power Contribution Analysis of a Multivariate Feedback System

    • Yoko Tanokura, Genshiro Kitagawa
    Pages 35-47

  5. Application to Financial and Economic Time Series Data

    • Yoko Tanokura, Genshiro Kitagawa
    Pages 49-99

  6. Back Matter

    Pages 101-103
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Keywords

About this book

​This book presents a new statistical method of constructing a price index of a financial asset where the price distributions are skewed and heavy-tailed and investigates the effectiveness of the method. In order to fully reflect the movements of prices or returns on a financial asset, the index should reflect their distributions. However, they are often heavy-tailed and possibly skewed, and identifying them directly is not easy. This book first develops an index construction method depending on the price distributions, by using nonstationary time series analysis. Firstly, the long-term trend of the distributions of the optimal Box–Cox transformed prices is estimated by fitting a trend model with time-varying observation noises. By applying state space modeling, the estimation is performed and missing observations are automatically interpolated. Finally, the index is defined by taking the inverse Box–Cox transformation of the optimal long-term trend. This book applies the method to various financial data. For example, applying it to the sovereign credit default swap market where the number of observations varies over time due to the immaturity, the spillover effects of the financial crisis are detected by using the power contribution analysis measuring the information flows between indices. The investigations show that applying this method to the markets with insufficient information such as fast-growing or immature markets can be effective.

Reviews

“The book develops a new practical method for constructing an index of prices of a financial asset for which the distributions are skewed and heavy-tailed. … The book is valuable and concise reading for professionals in the area of finance and financial econometrics.” (Pavel Stoynov, zbMATH 1338.91009, 2016)

Authors and Affiliations

  • Graduate School of Advanced Mathematical Sciences, Meiji University, Nakano-ku, Japan

    Yoko Tanokura

  • Research Organization of Information and Systems, Minato-ku, Japan

    Genshiro Kitagawa

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