Yilmaz, Tolgahan (2010): Improving Portfolio Optimization by DCC And DECO GARCH: Evidence from Istanbul Stock Exchange.
Download (196Kb) | Preview
In this paper, the performance of global minimum variance (GMV) portfolios constructed by DCC and DECO-GARCH are compared to that of GMV portfolios constructed by sample covariance and constant correlation methods in terms of reduced volatility. Also, the performance of GMV portfolios are tested against that of equally weighted and cap weighted portfolios. Portfolios are constructed from the stocks listed in Istanbul Stock Exchange 30 index (hereafter, ISE-30). The results show that GMV portfolios constructed by DCC-GARCH outperformed the other portfolios. In addition, the performance of GMV portfolios estimated by DCC and DECO-GARCH methods are improved by extending calibration period from three years to four years and lowering rolling window term from one week to one day, while the performances of other GMV portfolios decrease. It shows the effect of time varying variance and dynamic correlations on portfolio optimization at Turkish stock market.
|Item Type:||MPRA Paper|
|Original Title:||Improving Portfolio Optimization by DCC And DECO GARCH: Evidence from Istanbul Stock Exchange|
|Keywords:||DCC-GARCH; DECO-GARCH; GMV portfolio|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice; Investment Decisions
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C61 - Optimization Techniques; Programming Models; Dynamic Analysis
|Depositing User:||Tolgahan YILMAZ|
|Date Deposited:||11. Dec 2010 01:21|
|Last Modified:||28. Feb 2013 05:54|
1. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31: 307-327.
2. Bollerslev, T., Engle, R.F. and Wooldridge J.M. (1988). Capital Asset Pricing Model with Time-Varying Covariances. Journal of Political Economy, 96, 116–131.
3. Bollerslev, T. (1990). Modeling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72, 498–505.
4. Chan, L., Karceski, J. and Lakonishok, J. (1999). On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model. Review of Financial Studies, 12:937–74.
5. Elton, E., and M. Gruber. (1973). Estimating the Dependence Structure of Share Prices–Implications for Portfolio Selection. Journal of Finance, 28:1203–32.
6. Engle R.F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of The Variance Of United Kingdom Inflation. Econometrica, 50: 987-1007.
7. Engle R., Kroner F.K. (1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11: 122-150.
8. Engle R.F. (2001). Dynamic conditional correlation - A simple class of multivariate GARCH models. Journal of Business and Economic Statistics, 20: 339-350.
9. Engle, R.F., and Sheppard, K. (2001). Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working Papers 8554.
10. Engle, R. F. and Kelly, B. T. (2009). Dynamic Equicorrelation. NYU Working Paper No. FIN-08-038.
11. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7, 77–91.
12. Sharpe, W. (1963). A Simpliﬁed Model for Portfolio Analysis. Management Science, 9:277–93.
13. Tsui, A.K. and Yu, Q. (1999). Constant Conditional Correlation in a Bivariate GARCH model: Evidence from the stock market in China. Mathematics and Computers in Simulation, 48, pp. 503–509.
14. Tse Y.K., Tsui A.K.C. (2002). A Multivariate GARCH Model with Time-Varying Correlations. Journal of Business and Economic Statistics, 20, 351–362.