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http://dx.doi.org/10.5351/KJAS.2015.28.5.1013

Performance Comparison of Estimation Methods for Dynamic Conditional Correlation  

Lee, Jiho (Department of Applied Statistics, Chung-Ang University)
Seong, Byeongchan (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.5, 2015 , pp. 1013-1024 More about this Journal
Abstract
We compare the performance of two representative estimation methods for the dynamic conditional correlation (DCC) GARCH model. The first method is the pairwise estimation which exploits partial information from the paired series, irrespective to the time series dimension. The second is the multi-dimensional estimation that uses full information of the time series. As a simulation for the comparison, we generate a multivariate time series similar to those observed in real markets and construct a DCC GARCH model. As an empirical example, we constitute various portfolios using real KOSPI 200 sector indices and estimate volatility and VaR of the portfolios. Through the estimated dynamic correlations from the simulation and the estimated volatility and value at risk (VaR) of the portfolios, we evaluate the performance of the estimations. We observe that the multi-dimensional estimation tends to be superior to pairwise estimation; in addition, relatively-uncorrelated series can improve the performance of the multi-dimensional estimation.
Keywords
multivariate volatility model; DCC GARCH model; ARCH; conditional heteroscedasticity; pair-wise estimation; KOSPI 200;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Aielli, G. P. (2013). Dynamic conditional correlation: On properties and estimation, Journal of Business & Economic Statistics, 31, 171-194.
2 Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327.   DOI   ScienceOn
3 Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: A multivariate gener- alized ARCH model, The Review of Economics and Statistics, 72, 498-505.   DOI   ScienceOn
4 Bollerslev, T., Engle, R. F. and Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances, The Journal of Political Economy, 96, 116-131.   DOI   ScienceOn
5 Dajcman, S. and Festic, M. (2012). Interdependence between the Slovenian and European stock markets-a DCC-GARCH analysis, Ekonomska Istrazivanja, 25, 379.   DOI
6 Ding, Z., Granger, C. W. and Engle, R. F. (1993). A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1, 83-106.   DOI   ScienceOn
7 Sener, E., Baronyan, S. and Menguturk, L. A. (2012). Ranking the predictive performances of value-at-risk estimation methods, International Journal of Forecasting, 28, 849-873.   DOI   ScienceOn
8 Tsay, R. S. (2010). Analysis of Financial Time Series, Wiley.
9 Xu, C. and Chen, H. (2012). Measuring portfolio value at risk, Working paper, Department of Economics, School of Economics and Management, Lund University.
10 Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom in ation, Econometrica: Journal of the Econometric Society, 45, 987-1007.
11 Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregres- sive conditional heteroskedasticity models, Journal of Business & Economic Statistics, 20, 339-350.   DOI   ScienceOn
12 Engle, R. F. and Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH, Econometric Theory, 11, 122-150.   DOI   ScienceOn
13 Glosten, L. R., Jagannathan, R. and Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks, The Journal of Finance, 48, 1779-1801.   DOI   ScienceOn
14 Kim, W. H. (2014). Time-varying comovement of KOSPI 200 sector indices returns, CSAM (Communications for Statistical Applications and Methods), 21, 335-347.
15 Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models, Studies in Nonlinear Dynamics & Econometrics, 9, 1-55.
16 Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica: Journal of the Econometric Society, 59, 347-370.   DOI
17 Orskaug, E. (2009). Multivariate DCC-GARCH model with various error distributions, Working paper, Norwegian Computing Center, SAMBA/19/09.
18 Pagan, A. R. and Schwert, G. W. (1990). Alternative models for conditional stock volatility, Journal of Econometrics, 45, 267-290.   DOI   ScienceOn
19 Schwert, A. (2010). Crisis period forecast evaluation of the DCC-GARCH Model yang ding, Doctoral dissertation, Duke University Durham.