scholarly journals Clustering Huge Number of Financial Time Series: A Panel Data Approach with High-Dimensional Predictors and Factor Structures

2015 ◽  
Author(s):  
Tomohiro Ando ◽  
Jushan Bai
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Financial Time Series ◽  
High Dimensional ◽  
Huge Number ◽  
Financial Time

scholarly journals Shortcomings of Transfer Entropy and Partial Transfer Entropy: Extending Them to Escape the Curse of Dimensionality

2020 ◽  
Vol 30 (16) ◽  
pp. 2050250
Author(s):  
Angeliki Papana ◽  
Ariadni Papana-Dagiasis ◽  
Elsa Siggiridou
Keyword(s):  
Time Series ◽  
Random Forests ◽  
Financial Time Series ◽  
Transfer Entropy ◽  
High Dimensional ◽  
Multivariate System ◽  
Financial Time ◽  
Confounding Variables ◽  
Partial Transfer ◽  
Synthetic Time Series

Transfer entropy (TE) captures the directed relationships between two variables. Partial transfer entropy (PTE) accounts for the presence of all confounding variables of a multivariate system and infers only about direct causality. However, the computation of partial transfer entropy involves high dimensional distributions and thus may not be robust in case of many variables. In this work, different variants of the partial transfer entropy are introduced, by building a reduced number of confounding variables based on different scenarios in terms of their interrelationships with the driving or response variable. Connectivity-based PTE variants utilizing the random forests (RF) methodology are evaluated on synthetic time series. The empirical findings indicate the superiority of the suggested variants over transfer entropy and partial transfer entropy, especially in the case of high dimensional systems. The above findings are further highlighted when applying the causality measures on financial time series.

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