A forecast comparison of volatility models: does anything beat a GARCH(1,1)?
比较了330种ARCH类模型对条件方差的描述能力，发现对于汇率数据，GARCH(1,1)并不逊于更复杂的模型；但对于IBM收益率数据，能捕捉杠杆效应的模型明显优于GARCH(1,1)。
Abstract We compare 330 ARCH‐type models in terms of their ability to describe the conditional variance. The models are compared out‐of‐sample using DM–$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in our analysis of exchange rates, whereas the GARCH(1,1) is clearly inferior to models that can accommodate a leverage effect in our analysis of IBM returns. The models are compared with the test for superior predictive ability (SPA) and the reality check for data snooping (RC). Our empirical results show that the RC lacks power to an extent that makes it unable to distinguish ‘good’ and ‘bad’ models in our analysis. Copyright © 2005 John Wiley & Sons, Ltd.