A Optimization Model of Correlation Matrix

Jong Meng; Chun Xia Guo

doi:10.4028/www.scientific.net/AMR.482-484.270

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 482-484A Optimization Model of Correlation Matrix

A Optimization Model of Correlation Matrix

Abstract:

Hamilton and Pelletier have brought forward the regime switching model for dynamic correlation matrix. It is especially for the variance time-varying financial multiple time series. In this paper, we use random matrix theory to improve the correlation matrix, which removed the noise, leaving the true information. We also present an empirical application use China's stock market data to optimize portfolio which illustrates that our model can have achieved good results.

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Periodical:

Advanced Materials Research (Volumes 482-484)

Pages:

270-273

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.482-484.270

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Online since:

February 2012

Authors:

Jong Meng, Chun Xia Guo

Keywords:

Conditional Heteroskedasticity, Correlation Matrix, Random Matrix, Regime Switching

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References

[1] Engle, R.F., Autoregressive conditional heteroskedasticity with estimates of variance of United Kingdom inflation [J], Econometica, 1982, 50: 987-1007.

DOI: 10.2307/1912773

Google Scholar

[2] Bollerslev, T., Generalized autoregressive conditional heteroskedasticity [J], Journal of Econometrics, 1986, 31: 307-327.

DOI: 10.1016/0304-4076(86)90063-1

Google Scholar

[3] Bollerslev, T., Modeling the coherence in short-run normal exchange rates: a multivariate generalized arch model [J], Review of Economics and Statistics, 1990, 72: 498-505.

DOI: 10.2307/2109358

Google Scholar

[4] Engle, R., Sheppard, K., Theoretical and empirical properties of dynamic conditional correlation multivariate garch [J], UCSD Discussion Paper 2001-15.

DOI: 10.3386/w8554

Google Scholar

[5] Engle, R.F., Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity[J], Journal of Business & Economic Statistic, 2002, 20 (3): 339-350.

DOI: 10.1198/073500102288618487

Google Scholar