How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?
提出一种方法，在存在结构性突变的情况下，最优利用历史数据预测长期股票收益的分布，发现忽略突变或使用固定长度移动窗口的做法被强烈拒绝，突变影响分布形状，对风险管理和长期投资有启示。
We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The empirical results strongly reject ignoring structural change or using a fixed-length moving window. The shape of the long-run distribution is affected by breaks, which has implications for risk management and long-run investment decisions.