Browse > Article
http://dx.doi.org/10.5351/CKSS.2008.15.6.825

Volatility Analysis for Multivariate Time Series via Dimension Reduction  

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)
Publication Information
Communications for Statistical Applications and Methods / v.15, no.6, 2008 , pp. 825-835 More about this Journal
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.
Keywords
MGARCH; factor model; MSE; MAD;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Campbell, J. Y., Lo, A. W. and MacKinlay, A. C. (1997). The Econometrics of Financial Markets, Princeton University Press, Princeton, New Jersey
2 Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom Inflation, Econometrica, 50, 987-1007   DOI   ScienceOn
3 Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327   DOI   ScienceOn
4 Engle, R. F. and Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH, Econometric Theory, 11, 122-150   DOI   ScienceOn
5 Grinold, R. C. and Kahn, R. N. (2000). Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk, 2nd edition., McGraw-Hill, New York
6 RiskMetrics (1996). RiskMetrics, Technical Document, 4th ed., J. P. Morgan, New York
7 Tsay, R. S. (2005). Analysis of Financial Time Series, John Wiley & Sons, New York
8 Zivot, E. and Wang, J. (2006). Modeling Financial Time Series with S-PLUS, 2nd ed., Springer, New York
9 Bauwens, L., Laurent, S. and Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey, Journal of Applied Econometrics, 21, 79-109   DOI   ScienceOn
10 성웅현 (2002). <응용 다변량분석>. 탐진
11 Bollerslev, T. (1990). Modeling the conditional in short-run nominal exchange rates: A multivariate generalized ARCH model, Review of Economics and Statistics, 72, 498-505   DOI   ScienceOn
12 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   DOI   ScienceOn
13 Connor, G. (1995). The three types of factor models: A comparison of their explanatory power, Financial Analysts Journal, 51, 42-46