A fragmented-periodogram approach for clustering big data time series

We propose and study a new frequency-domain procedure for characterizing and comparing large sets of long financial time series. Instead of using all the information available from data, which would be computationally very expensive, we propose some regularization rules in order to select and summarize the most relevant information for clustering purposes. Essentially, we propose to use a fragmented periodogram computed around the driving seasonal components of interest and compare the various estimates. This procedure is computationally simple, but able to condense relevant second-order information of the volatility of the time series. A simulation exercise shows that the smoothed fragmented periodogram works in general better than the non-smoothed one and not significantly worse than the complete periodogram. We apply this procedure to study the evolution of several stock markets indices, extracting information on the European financial integration. We further show the effect of the recent financial crisis over these indices behaviour.

CAIADO Jorge;  CRATO Nuno;  PONCELA BLANCO Maria Del Pilar; 

2020-04-21

SPRINGER HEIDELBERG

JRC112710

1862-5347 (online),   

https://link.springer.com/article/10.1007%2Fs11634-019-00365-8,    https://publications.jrc.ec.europa.eu/repository/handle/JRC112710,   

10.1007/s11634-019-00365-8 (online),