The Multiscale Causal Dynamics of Foreign Exchange Markets

This paper relies on wavelet multiresolution analysis to capture the dependence structure of currency markets and reveal the complex dynamics across different timescales. It investigates the nature and direction of causal relationships among the most widely traded currencies denoted relative to the United States Dollar (USD), namely Euro (EUR), Great Britain Pound (GBP) and Japanese Yen (JPY). The timescale analysis involves the estimation of linear vis-à-vis nonlinear and spectral causality of wavelet components and aggregate series as well as the detection of short- vs. long-run linkages and cross-scale correlations. Moreover, this study attempts to probe into the micro-foundations of across-scale heterogeneity in the causality pattern on the basis of trader behavior with different time horizons. New stylized properties emerge in the volatility structure and the implications for the flow of information across scales are inferred. The examined period starts from the introduction of the Euro and covers the dot-com bubble, the financial crisis of 2007-2010 and the Eurozone debt crisis. Technically, this paper presents an invariant discrete wavelet transform that deals efficiently with phase shifts, dyadic-length and boundary effects. It also proposes a new entropy-based methodology for the determination of the optimal decomposition level. Overall, there is no indication of a global causal behavior that dominates at all timescales. When the nonlinear effects are accounted for, the evidence of dynamical bidirectional causality implies that the pattern of leads and lags changes over time. These results may prove useful to quantify the process of integration as well as influence the greater predictability of currency markets.