摘要(英) |
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. |
參考文獻 |
Campbell, J. Y. and Yogo, M. (2005). Implementing the econometric methods in‘‘Efficient tests of stock return predictability’’, Unpublished working paper. University of Pennsylvania.
Campbell, J. Y. and Yogo, M. (2006). Efficient tests of stock return predictability, Journal of Financial Economics 81, 27–60.
Cavanagh, C.L. and Elliott, G. and Stock, J.H. (1995). Inference in models with nearly integrated regressors, Econometric Theory 11, 1131–1147.
Fuh, C.D. and Pang, T.X. (2016). Asymptotic properties of the LSE in predictive regressions with possibly heavy-tailed innovations, preprint.
Goyal, A. and Welch, I.(2008). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction, The Review of Financial Studies 21, 1455–1508.
Hjalmarsson, E. (2011). New methods for inference in long-horizon regressions, Journal of Financial and Quantitative Analysis 46, 815–839.
Jansson, M. and Moreira, M. J.(2006). Optimal inference in regression models with nearly integrated regressors, Econometrica 74, 681–714.
Kostakis, A. and Magdalinos, T. and Stamatogiannis, M.P. (2015). Robust Econometric Inference for Stock Return Predictability, Review of Financial Studies 28, 1506–1553.
Lanne, M. (2002). Testing the predictability of stock returns, Review of Economics and Statistics 84, 407–415.
Phillips, P.C.B. (2014). Towards a Unified Asymptotic Theory for Autoregression, Biometrika 74, 535–547.
Phillips, P.C.B. (2014). On confidence intervals for autoregressive roots and predictive regression, Econometrica 82, 1177–1195.
Stambaugh, R.F. (1999). Predictive regressions, Journal of Financial Economics 54, 375–421.
Stock, J.H. (1991). Confidence intervals for the largest autoregressive root in US macroeconomic time series, Journal of Monetary Economics 28, 435–49.
Torous, W. and Valkanov, R. and Yan, S. (2004). On predicting stock returns with nearly integrated explanatory variables, Journal of Business 77, 937–966.
Valkanov, R. (2003). Long-horizon regressions: Theoretical results and applications, Journal of Financial Economics 68, 201–232. |