Random, but not so much: A parameterization for the returns and correlation matrix of financial time series
arXiv:physics/0701025 · doi:10.1016/j.physa.2007.02.108
Abstract
A parameterization that is a modified version of a previous work is proposed for the returns and correlation matrix of financial time series and its properties are studied. This parameterization allows easy introduction of non-stationarity and it shows several of the characteristics of the true, observed realizations, such as fat tails, volatility clustering, and a spectrum of eigenvalues of the correlation matrix that can be understood as an extension of Random Matrix Theory results. The predicted behavior of this parameterization for the eigenvalues is compared with the eigenvalues of Brazilian assets and it is shown that those predictions fit the data better than Random Matrix Theory.
4 pages, 3 figures
References in corpus (9)
- Noise Dressing of Financial Correlation Matrices
- Universal and non-universal properties of cross-correlations in financial time series
- Dynamics of competition between collectivity and noise in the stock market
- Signal and Noise in Correlation Matrix
- Statistics of Atmospheric Correlations
- Towards identifying the world stock market cross-correlations: DAX versus Dow Jones
- The bulk of the stock market correlation matrix is not pure noise
- Signal and Noise in Financial Correlation Matrices
- Non-Stationary Covariance Matrices And Noise