Multiscaled Cross-Correlation Dynamics in Financial Time-Series
arXiv:1001.0497 · doi:10.1142/S0219525909002325
Abstract
The cross correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different timescales and then explore the eigenvalue spectrum over sliding time windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved. Eigenvalue dynamics are examined for both medium and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of timescale information in the context of risk management.
References in corpus (11)
- A Random Matrix Approach to Cross-Correlations in Financial Data
- Random Matrix Theory Analysis of Cross Correlations in Financial Markets
- Signal and Noise in Correlation Matrix
- Towards identifying the world stock market cross-correlations: DAX versus Dow Jones
- Increasing market efficiency: Evolution of cross-correlations of stock returns
- Cross-Correlation Dynamics in Financial Time Series
- The bulk of the stock market correlation matrix is not pure noise
- Random Matrix Theory and Fund of Funds Portfolio Optimisation
- Collective Origin of the Coexistence of Apparent RMT Noise and Factors in Large Sample Correlation Matrices
- Signal and Noise in Financial Correlation Matrices
- On the origin of the Epps effect