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Volatility Analysis for Multivariate Time Series via Dimension Reduction

차원축소를 통한 다변량 시계열의 변동성 분석 및 응용

  • Song, Eu-Gine (Department of Statistics, Sookmyung Women's University) ;
  • Choi, Moon-Sun (Department of Statistics, Sookmyung Women's University) ;
  • Hwang, S.Y. (Department of Statistics, Sookmyung Women's University)
  • 송유진 (숙명여자대학교 통계학과) ;
  • 최문선 (숙명여자대학교 통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Published : 2008.11.30

Abstract

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

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Cited by

  1. Asymmetric CCC Modelling in Multivariate-GARCH with Illustrations of Multivariate Financial Data vol.24, pp.5, 2011, https://doi.org/10.5351/KJAS.2011.24.5.821
  2. A recent overview on financial and special time series models vol.29, pp.1, 2016, https://doi.org/10.5351/KJAS.2016.29.1.001