Long-horizon stock return predictability test with a nonlinear nonparametric bootstrap method
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A nonparametric bootstrap procedure with an LSTAR modeling of the valuation ratio is applied to the continuously compounded real stock return and the log of the price-dividend process. The empirical distribution of the test statistics shows that the evidence for a stock return predictability weakens when we take care of nonlinearity dynamics in the regressor. We split the sample into two regimes and implement the long-horizon predictability tests. Results show that the stock return is predictable in the stationary regime, while the test statistic under the null of unpredictability is insignificant in the non-stationary regime.
Abstract
A nonparametric bootstrap procedure with an LSTAR modeling of the valuation ratio is applied to the continuously compounded real stock return and the log of the price-dividend process. The empirical distribution of the test statistics shows that the evidence for a stock return predictability weakens when we take care of nonlinearity dynamics in the regressor. We split the sample into two regimes and implement the long-horizon predictability tests. Results show that the stock return is predictable in the stationary regime, while the test statistic under the null of unpredictability is insignificant in the non-stationary regime.
참고문헌 (26)
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