Towards a sharp estimation of transfer entropy for identifying causality in financial time series
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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.
CitacióSerès, 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.
Versió de l'editorhttp://ceur-ws.org/Vol-1774/MIDAS2016_paper7.pdf