Long-Horizon Return Regressions With Historical Volatility and Other Long-Memory Variables
利用长记忆计量框架解释长期资产收益的可预测性随投资期限增加的现象，并引入新方法构建置信区间，检验NYSE/AMEX收益的可预测性。
The predictability of long-term asset returns increases with the time horizon as estimated in regressions of aggregated-forward returns on aggregated-backward predictive variables. This previously established evidence is consistent with the presence of common slow-moving components that are extracted upon aggregation from returns and predictive variables. Long memory is an appropriate econometric framework for modeling this phenomenon. We apply this framework to explain the results from regressions of returns on risk measures. We introduce suitable econometric methods for construction of confidence intervals and apply them to test the predictability of NYSE/AMEX returns.