Complex Principle Component Analysis on Dynamic Correlation Structure in Price Index Data | Zendy

Yuichi Kichikawa | Zendy; Yuta Arai | Zendy; Hiroshi Iyetomi | Zendy

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Complex Principle Component Analysis on Dynamic Correlation Structure in Price Index Data

Author(s) -

Yuichi Kichikawa,

Yuta Arai,

Hiroshi Iyetomi

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.08.294

Subject(s) - principal component analysis , computer science , series (stratigraphy) , matrix (chemical analysis) , component (thermodynamics) , autocorrelation , correlation , time series , econometrics , lag , multivariate statistics , transformation (genetics) , covariance matrix , statistics , algorithm , mathematics , artificial intelligence , machine learning , physics , geometry , thermodynamics , paleontology , computer network , biochemistry , materials science , chemistry , gene , composite material , biology

We carry out multivariate time series analysis on price indices of individual goods and services collected over the last 35 years in Japan. Adoption of the complex principal component analysis (CPCA) enables us to have a new insight into dynamic correlation structure involved in the price data. The CPCA is based on complexification of real data using the Hilbert transformation; lead-lag relations between individual prices manifest in a form of instantaneous phases of the complex time series. The correlation matrix in the CPCA is purified by adopting the random matrix theory as a null hypothesis for removal of statistical noises. We identify four significant eigenmodes for price movement which are free from seasonal variations. Each of them has different characteristics of dynamical correlations and is shown to be responsive to different economic events

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