Analysis of Multivariate Financial Time Series using Cointegration : Case Study
- Authors
- 최문선; 황선영; 박진아
- Issue Date
- Mar-2007
- Publisher
- 한국데이터정보과학회
- Keywords
- Cointegration; Error Correction Model; Vector AR; Cointegration; Error Correction Model; Vector AR
- Citation
- 한국데이터정보과학회지, v.18, no.1, pp 73 - 80
- Pages
- 8
- Journal Title
- 한국데이터정보과학회지
- Volume
- 18
- Number
- 1
- Start Page
- 73
- End Page
- 80
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/8464
- ISSN
- 1598-9402
- Abstract
- Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.
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