Understanding Exchange Rates Dynamics
With the emergence of the chaos theory and the method of surrogates data, nonlinear approaches employed in analysing time series typically suffer from high computational complexity and lack of straightforward explanation. Therefore, the need for methods capable of characterizing time series in terms of their linear, nonlinear, deterministic and stochastic nature are preferable. In this paper, we provide a signal modality analysis on a variety of exchange rates. The analysis is achieved by using the recently proposed "delay vector variance" (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV analysis are obtain via differential entropy based method using wavelet-based surrogates. A comprehensive analysis of the feasibility of this approach is provided. The empirical results show that the DVV method can be opted as an alternative way to understanding exchange rates dynamics.
|Date of creation:||Mar 2013|
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- Daniel Buncic, 2009.
"Understanding forecast failure of ESTAR models of real exchange rates,"
EERI Research Paper Series
EERI_RP_2009_18, Economics and Econometrics Research Institute (EERI), Brussels.
- Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
- Buncic, Daniel, 2009. "Understanding forecast failure in ESTAR models of real exchange rates," MPRA Paper 13121, University Library of Munich, Germany.
- Buncic, Daniel, 2009. "Understanding forecast failure of ESTAR models of real exchange rates," MPRA Paper 16526, University Library of Munich, Germany.
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