Testing the Martingale Hypothesis
We propose new tests of the martingale hypothesis based on generalized versions of the Kolmogorov–Smirnov and Cramér–von Mises tests. The tests are distribution-free and allow for a weak drift in the null model. The methods do not require either smoothing parameters or bootstrap resampling for their...
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sg-smu-ink.soe_research-26322020-01-21T07:32:11Z Testing the Martingale Hypothesis Phillips, Peter C. B. JIN, Sainan We propose new tests of the martingale hypothesis based on generalized versions of the Kolmogorov–Smirnov and Cramér–von Mises tests. The tests are distribution-free and allow for a weak drift in the null model. The methods do not require either smoothing parameters or bootstrap resampling for their implementation and so are well suited to practical work. The article develops limit theory for the tests under the null and shows that the tests are consistent against a wide class of nonlinear, nonmartingale processes. Simulations show that the tests have good finite sample properties in comparison with other tests particularly under conditional heteroscedasticity and mildly explosive alternatives. An empirical application to major exchange rate data finds strong evidence in favor of the martingale hypothesis, confirming much earlier research. 2014-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1633 info:doi/10.1080/07350015.2014.908780 https://ink.library.smu.edu.sg/context/soe_research/article/2632/viewcontent/2014JBESMG_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Brownian functional Cramér-von Mises test Exchange rates Explosive process Kolmogorov-Smirnov test Econometrics |
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Brownian functional Cramér-von Mises test Exchange rates Explosive process Kolmogorov-Smirnov test Econometrics Phillips, Peter C. B. JIN, Sainan Testing the Martingale Hypothesis |
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We propose new tests of the martingale hypothesis based on generalized versions of the Kolmogorov–Smirnov and Cramér–von Mises tests. The tests are distribution-free and allow for a weak drift in the null model. The methods do not require either smoothing parameters or bootstrap resampling for their implementation and so are well suited to practical work. The article develops limit theory for the tests under the null and shows that the tests are consistent against a wide class of nonlinear, nonmartingale processes. Simulations show that the tests have good finite sample properties in comparison with other tests particularly under conditional heteroscedasticity and mildly explosive alternatives. An empirical application to major exchange rate data finds strong evidence in favor of the martingale hypothesis, confirming much earlier research. |
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Phillips, Peter C. B. JIN, Sainan |
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Phillips, Peter C. B. JIN, Sainan |
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Phillips, Peter C. B. |
title |
Testing the Martingale Hypothesis |
title_short |
Testing the Martingale Hypothesis |
title_full |
Testing the Martingale Hypothesis |
title_fullStr |
Testing the Martingale Hypothesis |
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Testing the Martingale Hypothesis |
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testing the martingale hypothesis |
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Institutional Knowledge at Singapore Management University |
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2014 |
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https://ink.library.smu.edu.sg/soe_research/1633 https://ink.library.smu.edu.sg/context/soe_research/article/2632/viewcontent/2014JBESMG_av.pdf |
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