scholarly journals Rényi Transfer Entropy Estimators for Financial Time Series

2021 ◽  
Vol 5 (1) ◽  
pp. 33
Author(s):  
Petr Jizba ◽  
Hynek Lavička ◽  
Zlata Tabachová
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Financial Time Series ◽  
Transfer Entropy ◽  
Information Measure ◽  
Black Swan ◽  
Directional Information ◽  
Financial Time ◽  
Bare Bones ◽  
Bivariate Time Series

In this paper, we discuss the statistical coherence between financial time series in terms of Rényi’s information measure or entropy. In particular, we tackle the issue of the directional information flow between bivariate time series in terms of Rényi’s transfer entropy. The latter represents a measure of information that is transferred only between certain parts of underlying distributions. This fact is particularly relevant in financial time series, where the knowledge of “black swan” events such as spikes or sudden jumps is of key importance. To put some flesh on the bare bones, we illustrate the essential features of Rényi’s information flow on two coupled GARCH(1,1) processes.

Multiscale Symbolic Phase Transfer Entropy in Financial Time Series Classification

2017 ◽  
Vol 16 (02) ◽  
pp. 1750019 ◽  
Author(s):  
Ningning Zhang ◽  
Aijing Lin ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Pharmaceutical Industry ◽  
Phase Transfer ◽  
Financial Time Series ◽  
Market Mechanism ◽  
Transfer Entropy ◽  
Aviation Industry ◽  
Financial Time ◽  
High Degree ◽  
Degree Of Similarity

We address the challenge of classifying financial time series via a newly proposed multiscale symbolic phase transfer entropy (MSPTE). Using MSPTE method, we succeed to quantify the strength and direction of information flow between financial systems and classify financial time series, which are the stock indices from Europe, America and China during the period from 2006 to 2016 and the stocks of banking, aviation industry and pharmacy during the period from 2007 to 2016, simultaneously. The MSPTE analysis shows that the value of symbolic phase transfer entropy (SPTE) among stocks decreases with the increasing scale factor. It is demonstrated that MSPTE method can well divide stocks into groups by areas and industries. In addition, it can be concluded that the MSPTE analysis quantify the similarity among the stock markets. The symbolic phase transfer entropy (SPTE) between the two stocks from the same area is far less than the SPTE between stocks from different areas. The results also indicate that four stocks from America and Europe have relatively high degree of similarity and the stocks of banking and pharmaceutical industry have higher similarity for CA. It is worth mentioning that the pharmaceutical industry has weaker particular market mechanism than banking and aviation industry.

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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