Browse > Article
http://dx.doi.org/10.7465/jkdi.2014.25.6.1333

On multivariate GARCH model selection based on risk management  

Park, SeRin (Department of Statistics, Sungkyunkwan University)
Baek, Changryong (Department of Statistics, Sungkyunkwan University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.6, 2014 , pp. 1333-1343 More about this Journal
Abstract
Hansen and Lund (2005) documented that a univariate GARCH(1,1) model is no worse than other sophisticated GARCH models in terms of prediction errors such as MSPE and MAE. Here, we extend Hansen and Lund (2005) by considering multivariate GARCH models and incorporating risk management measures such as VaR and fail percentage. Our Monte Carlo simulations study shows that multivariate GARCH(1,1) model also performs well compared to asymmetric GARCH models. However, we suggest that actual model selection should be done with care in light of risk management. It is applied to the realized volatilities of KOSPI, NASDAQ and HANG SENG index for recent 10 years.
Keywords
Dynamic conditional correlation; value at risk; volatility models;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Bauwens, L., Laurent, S. and Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics, 21, 79-109.   DOI   ScienceOn
2 Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, 307-327.   DOI   ScienceOn
3 Byun, B., Yoo, D. and Lim, J. (2013). Validity assessment of VaR with Laplacian distribution. Journal of the Korean Data & Information Science Society, 24, 1263-1274.   과학기술학회마을   DOI   ScienceOn
4 Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50, 987-1007.   DOI   ScienceOn
5 Engle, R. F. (1986). Modelling the persistence of conditional variances. Econometrics Reviews, 5, 1-50.   DOI
6 Engle, R. F. (2002). Dynamic conditional correlation a simple class of multivariate GARCH models. Journalof Business and Economic Statistics, 18, 931-955.
7 Engle, R. F. and Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory,11, 122-150.   DOI   ScienceOn
8 Hansen P. R. and Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20, 873-889.   DOI   ScienceOn
9 Hurlin, Christophe, and Sessi Tokpavi. (2006). Backtesting value-at-risk accuracy: A simple new test.Journal of Risk, 9, 19-37.
10 Nelson, B. (1991). Conditional heteroskedasticity in asset returns : A new approach. Econometrica, 59, 347-370.   DOI   ScienceOn
11 Tsay, R. S. (2006). Multivariate volatility models. Institute of Mathematical Statistics, 52, 210-222.
12 Tse, Y. K. and Tsui, A. K. C. (2002). A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics, 20, 351-362.   DOI   ScienceOn
13 Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18, 931-955.   DOI   ScienceOn