Conditional independence graph for nonlinear time series and its application to international financial markets
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Publication:1672948
DOI10.1016/j.physa.2012.07.002zbMath1402.91582OpenAlexW2091701004MaRDI QIDQ1672948
Publication date: 11 September 2018
Published in: Physica A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physa.2012.07.002
Statistical methods; risk measures (91G70) Applications of graph theory (05C90) Economic time series analysis (91B84) Non-Markovian processes: hypothesis testing (62M07)
Uses Software
Cites Work
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- Granger causality and path diagrams for multivariate time series
- Identification of nonlinear VAR models using general conditional independence graphs
- Detection of information flow in major international financial markets by interactivity network analysis
- Testing for nonlinearity in time series: the method of surrogate data
- Identification of vector AR models with recursive structural errors using conditional independence graphs
- Bootstrap methods: another look at the jackknife
- Testing for nonlinearity using redundancies: Quantitative and qualitative aspects
- Nonlinear time series. Nonparametric and parametric methods
- Detecting nonlinearity in multivariate time series
- Tests for Serial Independence and Linearity Based on Correlation Integrals
- USING THE MUTUAL INFORMATION COEFFICIENT TO IDENTIFY LAGS IN NONLINEAR MODELS
- Introduction to Graphical Modelling
- Structural Learning with Time-Varying Components: Tracking the Cross-Section of Financial Time Series
- Generalized redundancies for time series analysis
- Graphical interaction models for multivariate time series.
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