MIDAS and dividend growth predictability: Revisiting the excess volatility puzzle
使用混合频率数据采样回归方法，研究多个国际股票市场的股息增长可预测性和过度波动之谜，发现考虑股息季节性显著提高可预测性，且方差界检验不拒绝市场效率假说。
Abstract We examine dividend growth predictability and the excess volatility puzzle across a large sample of international equity markets using a mixed‐frequency data sampling (MIDAS) regression approach. We find that accounting for dividend seasonality under the MIDAS framework significantly improves dividend growth predictability compared to simple regressions with annually aggregated data. Moreover, variance bounds tests that allow for nonstationary dividends consistently fail to reject the market efficiency hypothesis across all countries. Our findings suggest that the common rejection of market efficiency in the literature is most likely driven by the annual aggregation of dividend data as well as by the assumption of stationary dividends.