Random Correlation Matrix and De-Noising

Ken-ichi Mitsui () and Yoshio Tabata ()
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Ken-ichi Mitsui: Doctor Candidate of Osaka University
Yoshio Tabata: Graduate School of Business Administration, Nanzan Univeristy

No 06-26, Discussion Papers in Economics and Business from Osaka University, Graduate School of Economics

Abstract: In Finance, the modeling of a correlation matrix is one of the important problems. In particular, the correlation matrix obtained from market data has the noise. Here we apply the de-noising processing based on the wavelet analysis to the noisy correlation matrix, which is generated by a parametric function with random parameters. First of all, we show that two properties, i.e. symmetry and ones of all diagonal elements, of the correlation matrix preserve via the de-noising processing and the efficiency of the de-nosing processing by numerical experiments. We propose that the de-noising processing is one of the effective methods in order to reduce the noise in the noisy correlation matrix.

Keywords: correlation matrix; calibration; rank reduction; de-noising; wavelet analysis (search for similar items in EconPapers)
JEL-codes: C51 C61 C63 G32 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2006-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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