Lyócsa, Štefan and Výrost, Tomáš and Baumöhl, Eduard (2011): The instability of the correlation structure of the S&P 500.
Download (107kB) | Preview
Using weekly returns of S&P 500 constituents, we study the time-varying correlation structure during the period of 2006 to mid-2011. Contrary to most of the previous correlation studies of many assets, we do not use rolling correlations but the DCC MV-GARCH model with the MacGyver strategy proposed by Engle (2009). We find empirical evidence that the correlation structure tends to change significantly during the periods of high volatility and market downturns.
|Item Type:||MPRA Paper|
|Original Title:||The instability of the correlation structure of the S&P 500|
|Keywords:||correlation structure; dynamic conditional correlations; range-based volatility; conditional volatility; MacGyver strategy|
|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 ; Diffusion Processes ; State Space Models
G - Financial Economics > G1 - General Financial Markets
|Depositing User:||Eduard Baumöhl|
|Date Deposited:||17. Oct 2011 14:28|
|Last Modified:||13. Feb 2013 20:03|
Cook, S. and Manning, N. (2004) Lag optimization and finite-sample size distortion of unit root tests, Economics Letters, 84, 267–274.
Ding, L., Miyake, H. and Zou, H. (2011) Asymmetric correlations in equity returns: a fundamental-based explanation, Applied Financial Economics, 26, 389–399.
Engle, R. F. (2009) High dimension dynamic correlations, in The Methodology and Practice of Econometrics: Festschrift for David Hendry, J. L. Castle & N. Shephard, Oxford University Press, Oxford, pp. 122–148.
Engle, R. F. and Sheppard, K. (2001) Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, NBER Working Paper No. 8554, University of California, San Diego.
Garman, M. B. and Klass, M. J. (1980) On the Estimation of Security Price Volatilities from Historical Data, Journal of Business, 53, 67–78.
Lee, J. and Strazicich, M. (2004) Minimum LM Unit Root Test with One Structural Break. Working Paper No. 04–17, Appalachain State University, Boone.
Molnár, P. (2010) Properties of range-based volatility estimators, in Econophysics Colloquium, Academia Sinica, Taipei.
Ng, S. and Perron, P. (1995) Unit Root Tests in ARMA Models with Data-Dependent Methods for the Selection of the Truncation Lag, Journal of the American Statistical Association, 90, 268–281.
Onnela, J. P., Chakraborti, A., Kaski, K., Kertész, J. and Kanto, A. (2003) Dynamics of market correlations: Taxonomy and portfolio analysis, Physical Review E, 68, 056110.
Tse, Ch. K., Liu, J. and Lau, F. C. M. (2010) A network perspective of the stock market, Journal of Empirical Finance, 17, 659–667.