Abstract
A test procedure based on ranks is suggested to test for nonlinear cointegration. For two (or more) time series it is assumed that there exist monotonic transformations such that the normalized series can asymptotically be represented by independent Brownian motions. Rank test procedures based on the difference between the sequences of ranks are suggested. If there is no cointegration between the time series, the sequences of ranks tend to diverge, whereas under cointegration the sequences of ranks evolve similarly. Monte Carlo simulations suggest that for a wide range of nonlinear models the rank test performs better than parametric competitors. To test for nonlinear cointegration a variable addition test based on the ranks is suggested. As empirical illustrations we consider the term structure of interest rates and the relationship between common and preferred stock prices. It turns out that for this applications there is only weak evidence for a nonlinear long run relationship.
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© 1998 Springer Science+Business Media Dordrecht
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Breitung, J. (1998). Nonparametric Tests for Nonlinear Cointegration. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_8
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_8
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