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Group transfer entropy with an application to cryptocurrencies
- Source :
- Physica A: Statistical Mechanics and its Applications. 516:543-551
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- The detection of informational leadership is a core issue in financial market microstructure. We use effective group transfer entropy (EGTE) as a measure for the predictability of a stochastic process using lagged observations on multiple related processes within the same system. We propose an appropriate bootstrap to derive confidence bounds and show by means of a simulation study that standard linear approaches in economics and finance, such as vector autoregressions and Granger-causality tests, are not well suited to detect information transfer. We empirically examine the markets for cryptocurrencies using intraday data and reveal that the dependencies are mostly of nonlinear nature, highlighting the applicability of EGTE in the context of this new financial product.
- Subjects :
- Statistics and Probability
Cryptocurrency
Information transfer
Econophysics
Stochastic process
Computer science
Financial market
Context (language use)
Condensed Matter Physics
01 natural sciences
010305 fluids & plasmas
Granger causality
0103 physical sciences
Econometrics
Transfer entropy
Predictability
010306 general physics
Subjects
Details
- ISSN :
- 03784371
- Volume :
- 516
- Database :
- OpenAIRE
- Journal :
- Physica A: Statistical Mechanics and its Applications
- Accession number :
- edsair.doi...........49235f3e225dfd79aa91ecab2b398d0d