Comparison of transfer entropy methods for financial time series

There is a certain relationship between the global financial markets, which creates an interactive network of global finance. Transfer entropy, a measurement for information transfer, offered a good way to analyse the relationship. In this paper, we analysed the relationship between 9 stock indices from the U.S., Europe and China (from 1995 to 2015) by using transfer entropy (TE), effective transfer entropy (ETE), Renyi transfer entropy (RTE) and effective Renyi transfer entropy (ERTE). We compared the four methods in the sense of the effectiveness for identification of the relationship between stock markets. In this paper, two kinds of information flows are given. One reveals that the U.S. took the leading position when in terms of lagged-current cases, but when it comes to the same date, China is the most influential. And ERTE could provide superior results.

[1]  C. Granger.  Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[2]  Leonidas Sandoval,et al.  To lag or not to lag? How to compare indices of stock markets that operate on different times , 2014 .

[3]  Pengjian Shang,et al.  Multifractal diffusion entropy analysis on stock volatility in financial markets , 2012 .

[4]  R. A. Fisher,et al.  Statistical Tables for Biological, Agricultural and Medical Research , 1956 .

[5]  Y. Yoshikawa,et al.  Causality detected by transfer entropy leads acquisition of joint attention , 2007, 2007 IEEE 6th International Conference on Development and Learning.

[6]  M. Hénon,et al.  A two-dimensional mapping with a strange attractor , 1976 .

[7]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[8]  Leonidas Sandoval,et al.  Structure of a Global Network of Financial Companies Based on Transfer Entropy , 2014, Entropy.

[9]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[10]  Viola Priesemann,et al.  TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy , 2011, BMC Neuroscience.

[11]  Aram Galstyan,et al.  Information transfer in social media , 2011, WWW.

[12]  H. Kleinert,et al.  Rényi’s information transfer between financial time series , 2011, 1106.5913.

[13]  Ruey S. Tsay,et al.  Analysis of Financial Time Series , 2005 .

[14]  Thomas Dimpfl,et al.  The impact of the financial crisis on transatlantic information flows: An intraday analysis , 2014 .

[15]  P. G. Larsson,et al.  Reducing the bias of causality measures. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Ahmet Sensoy,et al.  Effective transfer entropy approach to information flow between exchange rates and stock markets , 2014 .

[17]  Albert Y. Zomaya,et al.  Local information transfer as a spatiotemporal filter for complex systems. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  H. Kantz,et al.  Analysing the information flow between financial time series , 2002 .

[19]  Jan Khre,et al.  The Mathematical Theory of Information , 2012 .

[20]  Luca Faes,et al.  Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series , 2013, Entropy.

[21]  Jean-François Bercher,et al.  Source coding with escort distributions and Renyi entropy bounds , 2009, ArXiv.

[22]  Wulfram Gerstner,et al.  Automatic characterization of three cortical neuron types reveals two distinct adaptation mechanisms , 2011, BMC Neuroscience.