The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Multivariate Volatility Analysis via Canonical Correlations for Financial Time Series

정준상관분석을 통한 다변량 금융시계열의 변동성 분석

  • Lee, Seung Yeon (Department of Statistics, Sookmyung Women's University) ;
  • Hwang, S.Y. (Department of Statistics, Sookmyung Women's University)
  • 이승연 (숙명여자대학교 통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Received : 2014.11.16
  • Accepted : 2014.11.23
  • Published : 2014.12.31
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Abstract

Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.

다변량 금융시계열의 변동성분석을 다변량 기법인 정준상관분석(canonocal correaltion analysis)을 이용해 분석하였다. 변동성의 특성상 계수들이 비음(non-negative)인 정준상관분석, 즉, non-negative and sparse canonical correlation analysis (NSCCA)를 이용해 보았다. 본 논문은 다변량 시계열의 변동성 커브에 대해 연구하고 있으며 제시된 방법론을 이변량 주식자료분석을 통해 예시해 보았다.

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