Robust desmoothed real estate returns
研究发现常见去平滑模型会产生极端收益率，扭曲风险度量，导致次优投资决策。提出在去平滑过程中加入稳健滤波器，用美国数据证明该方法能有效避免极端值，使去平滑序列更接近交易指数特征。
Abstract This research starts from the observation that common desmoothing models are likely to generate some extreme returns that will distort risk measurement and hence can lead to investment decisions that are suboptimal relative to those that would be made if a transaction‐based index were available. Thus, we propose to improve the desmoothing models by incorporating a robust filter into the procedure. We report that in addition to properly treating for smoothing, the method prevents the occurrence of extreme values. As shown with U.S. data, our method leads to desmoothed series whose characteristics are akin to those of transaction‐based indices.