Improved Dynamic Portfolio Selection Model with DCC-GARCH: Evidence from the U.S. Stock Market

Siyu Wang

doi:10.54691/bcpbm.v30i.2418

Authors

Siyu Wang

DOI:

https://doi.org/10.54691/bcpbm.v30i.2418

Keywords:

DCC-GARCH; portfolio selection; mean-variance model; the US stock market.

Abstract

As an important financial instrument in real financial market, the selection and management of portfolio has always been a significant and hot issue in the field of economics. This study collects data of stocks daily returns in the real US stock market to test whether DCC-GARCH model can improve the performance of portfolio return compared with the original static model when the assumption of fixed covariance matrix is relaxed. This study also compares the performance of improved portfolio with that of S&P500. The result shows that portfolio returns obtained by the dynamic model outperform the original static portfolio and market performance by a large margin. This shows that, without taking into account transaction costs, an effective way to enhance the traditional mean-variance model is to fit and predict the dynamic covariance using DCC-GARCH model.

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References

Markowitz H M. (1952). Portfolio selection. The Journal of Finance, 7(1), 77.

Sharpe W F. (1963). A Simplified Model for Portfolio Analysis. Management Science, 9(2), 277-293.

Markowitz H M. (1955). The Optimization of Quadratic Functions Subject to Linear Constraints. Naval Research Logistics Quarterly, 3(1-2), 111-133.

Mao J C T. (1970). Models of capital budgeting, EV vs ES. Journal of financial and quantitative analysis, 4(5), 657-675.

Konno H, Suzuki K. (1995). A mean-variance-skewness optimization model. Journal of the Operations Research Society of Japan, 38, 137-187.

Konno H, Yamazaki H. (1991). Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market. Management Science, 37(5), 519-531.

Cai X, Teo K L, Yang X, et al. (2000). Portfolio Optimization Under a Minimax Rule. Management Science, 46(7), 957-972.

Roy A D. (1952). Safety first and the holding of assets. Econometrica: Journal of the econometric society, 431-449.

Rockafellar R T, Uryasev S. (2000). Optimization of conditional value-at-risk. Journal of risk, 2, 21-42.

Merton R C. (1969). Lifetime portfolio selection under uncertainty: The continuous-time case. The review of Economics and Statistics, 247-257.

Merton R C. (1975). Optimum consumption and portfolio rules in a continuous-time model//Stochastic optimization models in finance. Academic Press, 621-661.

Balduzzi P, Lynch A W. (1999). Transaction costs and predictability: Some utility cost calculations. Journal of Financial Economics, 52(1), 47-78.

Li D, Ng W L. (2000). Optimal dynamic portfolio selection: Multiperiod mean‐variance formulation. Mathematical finance, 10(3), 387-406.

Xu Y, Wu Z. (2014). Continuous-Time Mean-Variance Portfolio Selection with Inflation in an Incomplete Market. Journal of Financial Risk Management, 03(2), 19-28.

Wu H, Yan, et al. (2012). Multi-period mean-variance portfolio selection in a regime-switching market with a bankruptcy state. Optimal Control Applications and Methods, 34(4), 415-432.

Dey K, Maitra D. (2012). Portfolio Selection Revisited: Evidence from the Indian Stock Market. The IUP Journal of Applied Finance, 18, 31-47.

Kim S M, Kim H S. (2009). Investment Performance of Markowitz's Portfolio Selection Model in the Korean Stock Market. Drugs & Therapy Perspectives, 7(12), 13-16.

Xu Q, Zuo J, Jiang C, et al. (2020). A large constrained time-varying portfolio selection model with DCC-MIDAS: Evidence from Chinese stock market. International Journal of Finance & Economics.

Miguel, Ángel, Canela, et al. (2007). Portfolio selection with skewness in emerging market industries. Emerging Markets Review.

Bongabonga L, Nleya L. (2016). Assessing portfolio market risk in the BRICS economies: use of multivariate GARCH models. Mpra Paper.

Engle III R F, Sheppard K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH.

Engle R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.