RANK-BASED INFERENCE FOR MULTIVARIATE NONLINEAR AND LONG-MEMORY TIME SERIES MODELS

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Author(s)

Abstract

The portfolio of the Japanese Government Pension Investment Fund (GPIF) consists of a linear combination of five benchmarks of financial assets. Some of these exhibit long-memory and nonlinear behavior. Their analysis therefore requires multivariate nonlinear and long-memory time series models. Moreover, the assumption that the innovation densities underlying those models are known seems quite unrealistic. If those densities remain unspecified, the model becomes a semiparametric one, and rank-based inference methods naturally come into the picture. Rank-based inference methods under very general conditions are known to achieve the semiparametric efficiency bounds. % through the maximum invariant property of ranks. Defining ranks in the context of multivariate time series models, however, is not obvious. We propose two distinct definitions. The first one relies on the assumption that the innovation density is some unspecified elliptical density. The second one relies on the assumption that the innovation process is described by some unspecified independent component analysis model. Applications to portfolio management problems are discussed.

Journal

  • JOURNAL OF THE JAPAN STATISTICAL SOCIETY

    JOURNAL OF THE JAPAN STATISTICAL SOCIETY 40(1), 167-187, 2010-06-01

    THE JAPAN STATISTICAL SOCIETY

References:  24

Codes

  • NII Article ID (NAID)
    10026983676
  • NII NACSIS-CAT ID (NCID)
    AA1105098X
  • Text Lang
    ENG
  • Article Type
    ART
  • ISSN
    18822754
  • NDL Article ID
    10885847
  • NDL Source Classification
    ZD43(経済--統計)
  • NDL Call No.
    Z76-A259
  • Data Source
    CJP  NDL  J-STAGE 
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