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A Quantilogram Approach to Evaluating Directional Predictability Author info | Abstract | Publisher info | Download info | Related research | Statistics Oliver Linton () (London School of Economics )
Yoon-Jae Whang (Korea University )
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In this note we propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on the correlogram of quantile hits. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to stock index return data. The empirical results suggests some directional predictability in returns especially in mid range quantiles like 5%-10%.
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Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number
1454.
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Length: 23 pages
Date of creation: Mar 2004Date of revision:
Handle: RePEc:cwl:cwldpp:1454Contact details of provider: Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA Phone: (203) 432-3702 Fax: (203) 432-6167 Web page: http://cowles.econ.yale.edu/ More information through EDIRC
Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
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Keywords: Correlogram ; Dependence ; Efficient Markets ; Quantiles ; Other versions of this item:
Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
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