| Publication type: | Conference other |
| Type of review: | Peer review (abstract) |
| Title: | Forecasting correlation structures |
| Authors: | Schüle, Martin Ott, Thomas Schwendner, Peter |
| Conference details: | NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017 |
| Issue Date: | 6-Jun-2017 |
| Language: | English |
| Subject (DDC): | 510: Mathematics |
| Abstract: | Often the signature of a complex system is a couple of empirically found time series. In many cases the exact processes generating these series are unknown and a merely descriptive data analysis is undertaken. A popular tool to describe the structural behaviour of the complex system is thereby the analysis of the correlation structure, i.e., the system of pairwise correlations. As the correlation coefficients are calculated from a given data set, the analysis usually does not allow to forecast the future correlated behaviour of the system. However, in many examples of complex systems, the dynamic correlation structure shows certain patterns that form and cluster and dissipate again over time. If there are thus certain consistent patterns in the analyzed correlation structure to be found, the joint behaviour of the system may be forecasted, at least for short time periods. We present such an approach to forecast correlation matrices by means of a financial time series example. By analyzing the eigenmodes of the correlation matrices for oscillation patterns the main market dynamics are identified. Then, the principal eigenmode oscillations are forecasted by multivariate autoregressive and mean-reversion models allowing to infer the future correlation structure for certain time periods. The inferred correlation matrices are further regularized and compared to benchmark models. The proposed method can be of use in any field with the need of analyzing empirical correlation structures, e.g. in climate research. |
| URI: | https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf https://digitalcollection.zhaw.ch/handle/11475/7780 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
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Schüle, M., Ott, T., & Schwendner, P. (2017, June 6). Forecasting correlation structures. NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017. https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf
Schüle, M., Ott, T. and Schwendner, P. (2017) ‘Forecasting correlation structures’, in NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017. Available at: https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf.
M. Schüle, T. Ott, and P. Schwendner, “Forecasting correlation structures,” in NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017, Jun. 2017. [Online]. Available: https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf
SCHÜLE, Martin, Thomas OTT und Peter SCHWENDNER, 2017. Forecasting correlation structures. In: NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017 [online]. Conference presentation. 6 Juni 2017. Verfügbar unter: https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf
Schüle, Martin, Thomas Ott, and Peter Schwendner. 2017. “Forecasting Correlation Structures.” Conference presentation. In NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017. https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf.
Schüle, Martin, et al. “Forecasting Correlation Structures.” NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017, 2017, https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf.
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