Efficiently predictive test with heavy-tailed innovations
| Autor: | Meng-Hua Chung, 鍾孟華 |
|---|---|
| Rok vydání: | 2016 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 The stock returns predictability is always a popular issue in the financial market, and the Bonferroni Q-test proposed by Campbell and Yogo (2006) has been a common and general method. But in recent years, the researcher in Phillips (2014) proposed that this method is not always valid in some situations. Therefore, we need to use the different way to get the relative confidence intervals which are needed in Bonferroni Q-test procedure. We then define an estimator of the nuisance parameter and set a boundary to distinguish the time when to use the different confidence intervals, and from this, we can complete the whole predictive Bonferroni Q-test. Then we use Monte Carlo to progressively verify our composite testing method. Beyond that, the general predictive tests usually have a normal assumption, this assumption is not satisfied the practical financial data. We all know that economic and financial data have high persistent and heavy tail, so we focus on the case that the data are near unit root. And we relax the normal restriction to a heavy-tailed assumption even infinite variance to do the predictive test so that the analysis is more corresponding to the real data. We also can have the result from Monte Carlo that our composite method is valid and precise under the heavy-tailed assumption. According to the empirical analysis using the U.S. equity data, we find reliable evidence for predictability of the earnings-price ratio, and the other predictor all have high persistence and heavy-tailed property. From the empirical results, we can conclude that unlike the normal assumption in the test before, our heavy-tailed assumption in this predictive test is more corresponding to the data. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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