Towards a sharp estimation of transfer entropy for identifying causality in financial time series

Àlex Serès, Alejandra Cabaña, Argimiro Arratia

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

We present an improvement of an estimator of causality in financial time series via transfer entropy, which includes the side information that may affect the cause-effect relation in the system, i.e. a conditional information-transfer based causality. We show that for weakly stationary time series the conditional transfer entropy measure is nonnegative and bounded below by the Geweke's measure of Granger causality. We use k-nearest neighbor distances to estimate entropy and approximate the distribution of the estimator with bootstrap techniques. We give examples of the application of the estimator in detecting causal effects in a simulated autoregressive stationary system in three random variables with linear and non-linear couplings; in a system of non stationary variables; and with real financial data.

Original languageAmerican English
Pages (from-to)31-42
Number of pages12
JournalCEUR Workshop Proceedings
Volume1774
Publication statusPublished - 2016

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