Advanced SearchSearch Tips
Assessments for MGARCH Models Using Back-Testing: Case Study
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Assessments for MGARCH Models Using Back-Testing: Case Study
Hwang, S.Y.; Choi, M.S.; Do, J.D.;
  PDF(new window)
Current financial crisis triggered by shaky U.S. banking system adds to the emphasis on the importance of the volatility in controlling and understanding financial time series data. The ARCH and GARCH models have been useful in analyzing economic time series volatilities. In particular, multivariate GARCH(MGARCH, for short) provides both volatilities and conditional correlations between several time series and these are in turn applied to computations of hedge-ratio and VaR. In this short article, we try to assess various MGARCH models with respect to the back-testing performances in VaR study. To this end, 14 korean stock prices are analyzed and it is found that MGARCH outperforms rolling window, and BEKK and CCC are relatively conservative in back-testing performance.
Muitivariate GARCH;Value at Risk (VaR);back-testing;stock prices data;
 Cited by
Value at Risk의 사후검증을 통한 다변량 시계열자료의 차원축소 방법의 비교: 사례분석,이대수;송성주;

응용통계연구, 2011. vol.24. 4, pp.597-607 crossref(new window)
극단 손실값들을 이용한 VaR의 추정과 사후검정: 사례분석,서성효;김성곤;

응용통계연구, 2010. vol.23. 2, pp.219-234 crossref(new window)
DCC 모델링을 이용한 다변량-GARCH 모형의 분석 및 응용,최성미;홍선영;최문선;박진아;백지선;황선영;

응용통계연구, 2009. vol.22. 5, pp.995-1005 crossref(new window)
금융시계열 분석을 위한 다변량-GARCH 모형에서 비대칭-CCC의 도입 및 응용,박란희;최문선;황선;

응용통계연구, 2011. vol.24. 5, pp.821-831 crossref(new window)
다변량 GARCH 모형의 CCC 및 ECCC 비교분석,이승연;황선영;

응용통계연구, 2014. vol.27. 7, pp.1219-1228 crossref(new window)
Asymmetric CCC Modelling in Multivariate-GARCH with Illustrations of Multivariate Financial Data, Korean Journal of Applied Statistics, 2011, 24, 5, 821  crossref(new windwow)
Comparison of Dimension Reduction Methods for Time Series Factor Analysis: A Case Study, Korean Journal of Applied Statistics, 2011, 24, 4, 597  crossref(new windwow)
Performance Analysis of Volatility Models for Estimating Portfolio Value at Risk, Korean Journal of Applied Statistics, 2015, 28, 3, 541  crossref(new windwow)
송유진, 최문선, 황선영 (2008). 차원축소를 통한 다변량 시계열의 변동성 분석 및 응용, <한국통계학회 논문집>, 15, 825-835

황선영, 박진아 (2005). VaR(Value at Risk) for Korean financial time series, <한국데이터정보과학회지>, 16, 283-288

Bauwens, L., Laurent, S. and Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey, Journal of Applied Econometrics, 21, 79-109 crossref(new window)

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327 crossref(new window)

Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model, Review of Economics and Statistics, 72, 498-505 crossref(new window)

Bollerslev, T., Engle, R. F. and Wooldridge, J. M. (1998). A capital asset pricing model with time-varying covariances, Journal of Political Economy, 96, 116-131 crossref(new window)

Christoffersen, P. and Pellitier, D. (2004). Backtesting value-at-risk: A duration-based approach, Journal of Financial Econometrics, 2, 84-108 crossref(new window)

Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1008 crossref(new window)

Engle, R. F. and Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH, Econometric Theory, 11, 122-150 crossref(new window)

Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives, 3, 73-84 crossref(new window)

RiskMetrics (1996). RiskMetrics Technical Document, 4th ed., J. P. Morgan, New York

Tsay, R. S. (2005). Analysis of Financial Time Series, John Wiley & Sons, New York