Towards a sharp estimation of transfer entropy for identifying causality in financial time series
Document typeConference report
Rights accessOpen Access
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.
CitationSerès, A., Cabaña, A., Arratia, A. Towards a sharp estimation of transfer entropy for identifying causality in financial time series. A: Workshop on MIning DAta for financial applicationS. "Proceedings of the 1st Workshop on MIning DAta for financial applicationS (MIDAS 2016), Riva del Garda, Italy, September 19-23, 2016". Riva del Garda: CEUR-WS.org, 2016, p. 31-42.