Towards a sharp estimation of transfer entropy for identifying causality in financial time series
| Autor: | Serès, A., Cabaña, A., Argimiro Arratia |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Universitat Autònoma de Barcelona ResearcherID Scopus-Elsevier Recercat. Dipósit de la Recerca de Catalunya instname |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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