Cees Diks, University of Amsterdam
Linear and Nonlinear Causal Relations in Exchange Rates and Oil Spot and Futures Prices
Various tests have been proposed recently in the literature for detecting causal relationships between time series. I will briefly review the traditional linear methods and some more recent contributions on testing for nonlinear Granger causality. The relative benefits and limitations of these methods are then compared in two different case studies with real data. In the first case study causal relations between six main currency exchange rates are considered. After correcting for linear causal dependence using VAR models there is still evidence presence for nonlinear causal relations between these currencies. ARCH and GARCH effects are insufficient to fully account for the nonlinear causality found. The second case study focuses on nonlinear causal linkages between daily spot and futures prices at different maturities of West Texas Intermediate crude oil. The results indicate that after correcting for possible cointegration, linear dependence and multivariate GARCH effects, some causal relations are still statistically significant. In both case studies the conclusion is that non-standard models need to be developed to fully capture the higher-order nonlinear dependence in the data.