Modelling and forecasting level shifts in absolute returns
修改了CLEAR模型，用于描述和预测绝对收益序列中因高波动和低波动期引起的水平位移，并发现技术交易变量对预测有解释作用，在九个股票市场上模型拟合和预测能力优于长记忆模型。
Abstract Due to high and low volatility periods, time series of absolute returns experience temporary level shifts which differ in length and size. In this paper we modify the basic Censored Latent Effects Autoregressive [CLEAR] model, such that it can describe and forecast the location and size of such level shifts. For our particular application, we assume that technical trading variables may have explanatory value for future level shifts, where these effects may differ across upward‐ or downward‐tending markets. A natural competitor of the resultant switching regime CLEAR [SR‐CLEAR] model is a long‐memory model, which is known to pick up neglected level shifts. Hence, when we apply the SR‐CLEAR model to nine stock markets and document its good fit and forecasting ability, we compare it with a long‐memory model. Copyright © 2002 John Wiley & Sons, Ltd.